Near-Infrared Selective Plasmonic Electrochromic Windows
By
Guillermo Garcia Jr.
A dissertation submitted in partial satisfaction of the
requirements for the degree of
Doctor of Philosophy
in
Engineering- Mechanical Engineering
in the
Graduate Division
of the
University of California, Berkeley
Committee in charge:
Professor Van P. Carey, Chair Dr. Delia Milliron Professor Costas Grigoropoulos Professor Catherine Koshland
Spring 2012
Abstract
Near-Infrared Selective Plasmonic Electrochromic Windows
by
Guillermo Garcia Jr.
Doctor of Philosophy in Engineering- Mechanical Engineering
University of California, Berkeley
Professor Van P. Carey, Chair
Residential and commercial buildings represent a prime opportunity to improve energy efficiency and sustainability worldwide. Currently, lighting and thermal management within buildings account for 20% of the United State’s yearly energy consumption. Several approaches, such as solid state lighting, energy efficient HVAC systems, and improved insulation, are currently being investigated to help mitigate building energy consumption. The work described in this dissertation focuses on studying the use of dynamic window coatings on commercial and residential buildings. Specifically, this work focuses on near infrared selective electrochromic window coatings that optimize the amount of solar heat that enters a building without affecting the amount of solar light.
Electrochromic window coatings are based on an electrochemical cell architecture that is composed of two electrochromic layers separated by ion conducting electrolyte. During operation, an applied bias is used to control the optical properties of both electrochromic layers by shuffling a small amount of current between the counter and working electrode. In a negative bias, the window coatings dim to a dark state. If you reverse the bias, the window coatings go back to a transparent state. Intermediate states can be achieved by controlling the value of the bias applied. Unfortunately, traditional electrochromic windows require a change in visible transmittance to gain energy savings within buildings. This change affects the amount of solar daylighting and inadvertently leads to an increase in electrical lighting during the day. This work focuses on developing a nanocrystal based plasmonic electrochromic window that only modulates the near infrared portion of light while remaining visibly transparent. Taking advantage of localized surface plasmon absorption, this work approaches dynamic window coatings in a new fashion. To date, no near infrared selective electrochromic windows exist in the literature.
To achieve near infrared selective modulation, thin film layers of tin doped indium oxide (ITO) and alumnium doped zinc oxide (AZO) nanocrystal films were investigated as a potential electrochromic layers. A colloidal synthetic technique was used to generate concentrated inks of both ITO and AZO nanocrystals. Thin films of ITO and AZO
1 nanocrystals where fabricated via spin casting and tested in electrochemical half cells. Prior to testing, extensive post processing techniques were investigated to develop transparent conductive films. During optimization, variations in nanocrystal size, layer thickness, dopant concentration and electrolyte were studied. For an optimized ITO film, 35% solar near infrared modulation was achieved while maintaining less than 6% modulation in solar insolation visible to the human eye. Optimized AZO nanocrystal films achieved 42% solar near infrared modulation with no change in solar insolation visible to the human eye. Extensive models where built to elucidate the physical mechanism used to achieve this solar modulation.
Computer simulations were developed to quantify the energy performance of buildings with dynamic near infrared selective windows. This model only quantifies thermal savings and establishes the ground work for potential savings in solar daylighting. The model shows that optimized near infrared selective coatings achieve 15% and 10% energy savings in warm climate regions and cold climate regions respectively. Overall, the focus of this work sets the stage for advanced plasmonic electrochromic films that not only enhance the performance of dynamic windows but introduce a new technique for reducing building energy consumption worldwide.
.
2
This work is dedicated to my parents, Guillermo and Ofelia Garcia, and to my mentor Delia Milliron for all their support and advice throughout my academic career.
i ACKNOWLEDGEMENTS
I would like to acknowledge several people who have worked on the development of this project:
Dr. Delia Milliron Dr. Raffaella Buonsanti Dr. Anna Llordes-Gil Dr. Rueben Medelsberg Dr. Tom Richardson Dr. Andre Anders Evan Runnerstrom Amy Bergerud Tracy Mattox
ii TABLE OF CONTENTS
Chapter 1: Introduction……………………………………………………….………...1
1.1 Motivation/Building Energy Consumption………………….…….…..1
1.2 Electrochromic Windows…………………………………………...…2
1.2.1 Electrochromic Fundamentals………………….....2
1.2.2 Electrochromic Materials………………………….3
1.2.3 Electrochromic Window Basics…………………...9
1.2.4 Performance/Cost Issues & Competitive Technologies……………………………………..11
1.3 Plasmonic Electrochromic Windows………………………………...13
1.3.1 Plasmon Fundamentals…………………………..13
1.3.2 Plasmonic Electrochromic Fundamentals……..…15
Chapter 2: Indium Tin Oxide Nanocrystal Electrochromic Layers………………...17
2.1 Indium Tin Oxide Material…………………………………………..17
2.1.1 Introduction to Indium Tin Oxide………………..17
2.1.2 Colloidal Synthetic Development…………….….19
2.1.3 Colloidal Synthetic Development of Indium Tin Oxide Nanocrystals……………………………....21
2.2 Device Fabrication…………………………………………………...25
2.2.1 Deposition Methods…………………………...…25
2.2.2 Post Processing Techniques…………………...…30
2.2.3 Plasmon Coupling and Dielectric Effects……..…39
2.3 Electrochromic Properties of Indium Tin Oxide Nanocrystals………42
2.3.1 Electrochromic Characterization Techniques…....42
iii
2.3.2 Electrochromic Figures of Merit…………………45
2.3.3 Electrochromic Effect……………………………46
2.3.4 Electrochromic Layer Variations………………...49
2.3.5 Optimized Indium Tin Oxide Electrochromic Layer…………………………..52
Chapter 3: Aluminum Zinc Oxide Nanocrystal Electrochromic Layers…………....57
3.1 Aluminum Zinc Oxide Material System …………………………….57
3.1.1 Introduction to Aluminum Zinc Oxide…………….57
3.1.2 Synthetic Development of Aluminum Zinc Oxide…………………………...59
3.2 Device Fabrication…………………………………………………...61
3.2.1 Spincoating Deposition Technique………………...61
3.2.2 Post Processing Techniques………………………..62
3.3 Electrochromic Properties of Aluminum Zinc Oxide Nanocrystals……………………………………………....68
3.3.1 Layer Variations………………………..………..…68
3.3.2 Electrolyte Variation……..……………………...…71
3.3.3 Optimized Electrochromic Layer Performance…....73
3.3.4 Switching Kinetics………………………………....75
3.3.5 Durability…………………………………………..75
Chapter 4: Energy Performance of Indium Tin Oxide and Aluminum Zinc Oxide plasmonic window……………..……………….78
4.1 Introduction to Energy Performance Model…………………………78
4.1.1 Solar Angles/Solar Tracking…………………….…79
4.1.2 Radiation Heat Transfer Through Window.……….80
iv
4.1.3 Temperature Distribution of the Room……...... 82
4.1.4 Temperature Distribution, HVAC use, and Energy Savings……………………….………..82
4.2 Energy Performance with No Coating……………………………….83
4.2.1 Warm Climate Region…………………………..…83
4.2.2 Cold Climate Region……………………………….87
4.3 Indium Tin Oxide Film Energy Performance………………..…...….90
4.3.1 Warm Climate Region…………………………..…90
4.3.2 Cold Climate Region……………………………….94
4.4 Aluminum Zinc Oxide Film Energy Performance…………………..98
4.4.1 Warm Climate Regions…………………………....98
4.4.2 Cold Climate Regions………………………….....102
4.5 Energy Performance Summary…………………………………..…105
Chapter 5: Conclusions and Future work……………………………...……………106
5.1 Conclusions…………………………………………………………106
5.2 Future work/ Liquid Cells………………………………………..…108
5.1.1 Charge Compensation…………………………….108
5.1.2 Future Research Plan…………………………..…109
References……………………………………………………………...………………112
Appendix A: Electrochromic Figures of Merit Code………………….……………119
Appendix B: Electrochromic Energy Performance Code……………….………….132
Appendix C: Electrochromic Kinetics Code………………………...………………161
v
vi CHAPTER 1: INTRODUCTION
1.1 MOTIVATION/BUILDING CONSUMPTION
Residential and commercial buildings represent a prime opportunity to improve energy efficiency and sustainability in the United States. As seen in Figure 1a, the buildings sector alone accounts for 40% of the United States’ yearly energy consumption (40 quadrillion BTUs, or “quads”, out of 100 total), and 8% of the world’s energy use [U.S. Department of Energy, 2010]. Lighting and thermal management each represent about 30% of the energy used within a typical building, which corresponds to ~12 quads each of yearly energy consumption in the US [Lee, 2006 & Judkoff, 2008]. Windows cover an estimated area of about 2,500 square km in the US and are a critical component of building energy efficiency as they strongly affect the amount of natural light and solar gain that enters a building. Recent progress has been made toward improving window energy efficiency through the use of inexpensive static coatings that either retain heat in cold climates (low emissive films) or reject solar heat gain in warm climates (near infrared rejection films). Currently, static window coatings manufactured from companies such as Cardinal, PPG, Saint-Gobain, AGC Flat Glass, and Viracon, account for 54% of the commercial window market due to their low cost (~$3-5/ft2). However, these window coatings are static and not well suited for locations with varying climates. Dynamic windows overcome these limitations by enhancing the window performance in all climates. Figure 1b shows the solar irradiance as a function of wavelength. When optimizing the amount of solar energy that enters a building, there are two regions of interest: the visible and near infrared region (NIR) of light. The visible portion deals with the light that we see with our eyes while the near infrared region deals with the heat that we feel with our skin. Dynamic windows allow users within a building to optimize the amount of visible and NIR light that enters a building by modulating the tint of the coating. Large-scale adoption of dynamic windows would not only help mitigate energy consumption in a building but promote domestic job creation in the manufacturing, installation, and construction sector. The economic and environmental benefits of reducing energy use in buildings via electrochromic windows are clear: cutting one quad of energy use would yield an average annual savings of $10.6 billion, while eliminating about 21.5 gigawatts of power produced by fossil fuels and saving nearly 59 million metric tons of CO2 emissions throughout the life cycle of the device (including manufacturiung) [U.S. Department of Energy, 2010].
a b
Transportation 28% Buildings 40%
Industry 32%
Figure 1: a) U.S. energy consumption by sector. b) Solar irradiance as a function of wavelength. Note that their three sections of interest: ultraviolet, visible, and near infrared.
1 1.2 ELECTROCHROMIC WINDOWS
1.2.1 Electrochromic Fundamentals
Within the family of dynamic coatings, electrochromic films are one of the earliest technologies explored. Discovered in the late 19th century, the electrochromic phenomenom did not gain a lot of attention until the 20th century. Since then, there have been several demonstrations of electrochromism in a variety of material systems. Of those, only a few have been adapted commercially. Gentex Corporation is the only company to successfully use electrochromic coatings in a commercial application. Their technology darkens rearview mirrors in automobiles and has been installed in millions of cars worldwide. Outside of this success story, the rest are limited to lab scale demonstrations or are on their way to become commercialized (Monk, 2007).
When we describe electrochromic materials, there are two classes: electrochromes and inorganic oxides. Of the two classes, inorganic oxides have received the most attention in applications such as dynamic window coatings and rearview mirrors due to their stability and ease of fabrication. Electrochrome systems have a wider range of color change and are mostly focused on display applications. In general, both classes of materials undergo a color change when an oxidation or reduction process takes place on an electrode surface. To achieve this, electrons are either introduced or removed from the active material by an applied potential. The active material can either be a uniform film on an electrode, an atom or molecule in solution that come in contact with an electrode, or a material embedded in a matrix that is contact with an electrode. During the oxidation or reduction state, the material system changes its discrete energy levels and alters the gap in between its energy states. Light absorption of a material is governed by the size of this gap and is depicted by Plank’s law,
hc E Eq. 1
where E is the energy gap, h is Plank’s constant, c is the speed of light, and λ is the wavelength of light. Any light with energy outside the absorption band will be reflected and the material color will represent this non-absorbed light. Depending on the material, this reflected light may lie outside the visible region. Most prominent material systems studied in the literature focus on color schemes that lie within the visible portion of light.
Outside of the traditional oxidation/reduction process, there exist other technologies that also inhibit a color change when driven by a potential. A suspended particle device (SPD) is a film of particles suspended in a matrix. Upon a bias, the particles rearrange to create a change in the optical properties. Typically, they restructure in a fashion that generates haze in the coating which distorts the transparency. Similar to SPDs, liquid crystal displays (LCD) are also composed of liquid crystals that rearrange in orientation when applied by a potential. LCD screens have become the industry standard for digital televisions and there has been a lot of progress in this field. LCD screens have recently been adapated to electrochromic glasses. Due to the enhanced speed of modulation,
2 electrochromic glasses sync with three dimensional televisions to control which eye is viewing the screen. This known technique creates a three dimensional effect. Other examples of non reducing electrochromes are charged species such as 3-{4-[2-(6- dibutylamino-2naphthyl)-trans-ethenyl] pyridinum} propane sulfonate that are used by in studies of biological membrane potentials. In this process, deprotonation is the mechanism in charge of color change [Monk, 2007].
Electrochromic devices are typically structured in electrochemical cell architectures. As shown in Figure 2, there three main layers exist: a working electrode, a counter electrode, and an electrolyte. The working and counter electrode are usually composed of active materials deposited on transparent conducting substrates such as transparent conducting oxides (TCO) or metal meshes. The electrolyte is composed of either a liquid or a solid. Solid electrolytes are polymer based or ion conducting glass such as lithium phosporus oxynitride (LiPON). The solvated ions incorporated in the electrolyte vary in composition and are primarily used for charge balancing when the system is under an applied potential. Depending on the application, the working electrode, the counter electrode, or both are actively changing color under an applied bias. If you are working with solution based electrochromes, color change occurs at the surface of the electrodes during electron transfer. Overall, electrochromic devices require a system that can provide electron transfer between two electrodes and with charge balance.
Working Counter Electrode Electrode
Electrolyte Figure 2: Electrochemical cell architecture used by electrochromic devices.
1.2.2 Electrochromic Materials
As described in the above section, there are a variety of material systems that can undergo an electrochromic effect. From these material systems, there are two main classes that will be covered in detail: electrochromes and inorganic oxides.
3 Electrochromes
Electrochromes are molecules that undergo a color change when oxidized or reduced. There are three types of electrochromes: solution electrochromes, solution-solid electrochromes, and solid electrochromes. The most common form of solution electrochrome is a methyl Figure 3: Methyl Viologen electrochrome viologen, shown in Figure 3. When reduced, it turns a bright blue color. One of the biggest problems with using solution processed electrochromes in device applications is that it requires water tight seals. If the seal is damaged, you have a large probability of contaminating the solution and ultimately destroying the device [Bamfield 2010].
The second type of electrochrome is a solution/solid hybrid. The most common form of Type II electrochrome studied is viologens with hydrophobic chains. This form of electrochromes is deposited on an electrode and immersed in a water based electrolyte. Depending on the chain used, one can achieve a variety of colors. Color neutrality can be achieved by incorporating multiple layers of varying viologen/hydrophobic chains. In addition to viologens, cyanphenyl paraquat and methoxyfluorenone also distribute color changes in a variety of solvents [Bamfield 2010].
The third type of electrochrome is a solid. There two forms of solid electrochromes: inorganic and polymer. Inorganic electrochromes will be covered in more detail in the next section but the most common form used is prussian blue (Fe7(CN)18). Polymeric electrochromes are made from conjugated aromatic systems such as anline, pyrroles, furans, carbozole, and thiophenes. Polymeric electrochromes have been used in a variety of applications due to their conducting properties. By making chains of varying aromatic systems, one can potentially create a variety of colors in a single device. This form of electrochrome is an ideal candidate for display applications. They have the capability of fast switching times and one can fine tune the bang gap of the material by chemical modification. In addition, they can achieve an RGB scale by incorporating three varying sheets that depict a blue, red, and green color. Still, one of the main problems with this system is its lack of durability. Its cycle lifetime is limited and therefore has created a roadblock for commercialization. Composite materials of polymeric electrochromes with inorganic electrochromes have been studied and seem to increase the stability but work still needs to be done before these systems are optimized [Bamfield 2010].
Inorganic Oxides
The largest class of electrochromic materials investigated in the literature is inorganic oxides. There are a wide variety of materials that exhibit a color change. Certain classes of materials change color during their oxidation state (anodic coloration) while others change color when reduced (cathodic coloration). Figure 4 summarizes the coloration characteristic of each material system studied in the literature. It has been witnessed that each material system has a distinct color when oxidized or reduced. Additionally, kinetic properties, cycle durability, and contrast ratio during coloration depend heavily on the
4 crystal structure of the material [Granqvist, 1995]. Each material system will be discussed in detail in the next section. Anodic Coloration Cathodic Coloration
Figure 4: Inorganic Oxide materials investigated in the literature and their coloration type.
Titanium Oxide
Titanium dioxide is a material that darkens upon reduction. The first study that discovered this phenomenon was published in 1974. It demonstrated that a dark blue to black color change occurs when you apply a bias across a sandwiched stack of titanium oxide powder [Inque, 1974]. Most studies in the literature focus on investigating the electrochromic effect of nanocrystal titanium oxide thin films [Hagfeldt, 1994 & Wang 2010]. It was observed that after a positive bias is applied, the working electrode undergoes the reaction shown below:
- TiO2 + xLi + xe LixTiO2 Eq. 2
A comparison between thin films of TiO2 grown by chemical vapor deposition and nanocrystalline TiO2 indicates that the nanocrystalline films show enhanced coloration. It is explained that intercalation of the counter ion is improved in films with high porosity [Hagfeldt, 1994]. Enhancements in coloration have been also investigated in the literature. One example shows that incorporating mono-layers of viologen electrochromes on the surface of the nanocrystal improves the coloration efficiency substantially [Cinnsealach, 1998].
Vanadium Oxide
Vanadium oxide is a unique material system that both colors and bleaches in different regions of the solar spectrum when reduced. This phenomenon was first witnessed in the late 70s and has been explored in more detail in the last 20years [Lampert, 1984]. A study to determine the physical mechanism behind its optical properties demonstrated the
5 crystal phase of the material is crucial in understanding the electrochemical stability. During reduction, the working electrode undergoes the reaction shown below:
+ - VO2.5 + xLi + xe LixVO2.5 Eq. 3
Optical measurements of vanadium oxide show that absorption in the near infrared region is increased during intercalation while it is reduced in regions below 500nm [Talledo, 1995]. Ultimately, this material is primarily a good candidate for an anodic electrode since most of the optical change lies within the near infrared region of light.
Chromium Oxide
Chromium oxide is a material system that undergoes a color change when oxidized. This material property was first explored in the early 80s and since has been investigated as a potential counter electrode material [Lampert, 1984]. When cycled, the chromium oxide film showed very little modulation (10% luminous modulation) which is suitable for a counter electrode [Azens, 1998]. A comparison of chromium oxide and nickel oxide films demonstrated that chromium oxide shows better stability in acidic electrolytes but subpar in optical performance as compared to nickel oxide. Overall, it is a good candidate as a counter electrode in acidic environments, which is suited for materials such as tungsten oxide, but more needs to be understood when enhancing its optical properties [Kullman, 2000].
Manganese Oxide
Manganese oxide is a material system that has anodic coloration. Similar to chromium oxide, manganese oxide was first investigated in the early 80s. Unlike chromium oxide, manganese oxide is more stable in basic electrolytes [Garnich, 1990]. It is seen that in basic electrolytes, the following discharge reaction occurs:
- - MnO2 + H2O + e MnOOH + OH Eq. 4
When coloring, the films turn a dark brown color and achieve about a 30% change in transmittance [Cordobar de Torresi, 1991]. Ultimately, this material system can be used as a counter electrode but ideally is not the best candidate.
Iron Oxide
Iron oxide is a material system that changes color when oxidized. Iron oxide has been explored since the early 80s and has demonstrated decent coloration efficiencies [Monk, 2007]. Similar to other anodic coloration materials, most of the optical change is limited to the near infrared region of light [Maruyama, 1996]. When oxidized, the following reaction occurs:
+ - ` Fe2O3 + xLi + e LixFe2O3 Eq. 5
6 By incorporating sulfate residues on nanoparticulate iron oxde thin films, it has been shown that one can increase the charge capacity of lithium based electrolytes. Little change in optical density is witnessed in these films [Wang, 2001]. Overall, iron oxide is another alternative counter electrode material that is stable in cycling.
Cobalt Oxide
Cobalt oxide is a material system that undergoes a dark brown color change when oxidized. This material system was first investigated in the early 70s [Burke, 1980]. Depending on the preparation of the film, other colors may be achieved after oxidation. This is particularly true for cobalt hydroxide films. Similar to other anodic electrochromic materials, during oxidation the material undergoes the reaction shown below [Maruyama “Electrochromic properties of Cobalt Oxide…,” 1996]:
+ - Co3O4 + xLi + xe LixCo3O4 Eq. 6
Similar properties were discovered for films grown via a sol-gel technique [Svegal, 1996]. Ultimately, cobalt oxide is a material system that is a good alternative for a counter electrode.
Rhodium Oxide
Rhodium oxide is a material system that undergoes a dark green color change when oxidized. It has been investigated since the early 80s with hopes to use it as a counter electrode in electrochromic systems [Gottesfeld, 1980]. In the presence of a basic electrolyte solution, the reaction that takes place during oxidation is as follows:
+ - ½(Rh2O3*5 H2O) RhO2*2H2O + H +e Eq. 7
Its color in the natural state is a transparent yellow. This material system requires that the electrolyte be alkaline [Dautremont-Smith, 2003]. When optimized, a 70% change in visible transmittance can be achieved [Wang, 2001].
Iridium Oxide
Iridium oxide is a material system with anodic coloration that has been studied since the 70s [Shay, 1979]. Unlike rhodium oxide, iridium oxide is only stable in acidic electrolyte solutions [Dautremont-Smith, 2003]. The following reaction occurs during oxidation in an aqueous electrode:
+ - Ir(OH)n IrOx(OH)n-x + xH + xe Eq. 8
Roughly a 60% change in transmittance is observed at wavelengths of 540nm [Gottesfeld, 1998]. Showing optimized performance, iridium oxide materials make good alternatives for counter electrodes. Unfortunately, this material is expensive and it is difficult to implement this into commercial devices.
7 Niobium Oxide
Niobium oxide is a material system that undergoes color change upon reduction. Niobium oxide has been investigated in a variety of electrolyte solutions since the early 80s [Reichman, 1980]. For an aqueous and non-aqueous electrolyte solution, niobium oxide undergoes the following reaction when reduced respectively:
+ - xH + xe + Nb2O5 HxNb2O5 Eq. 9
+ - xLi + xe + Nb2O5 LixNb2O5 Eq. 10
Depending on the crystal phase of the material system, niobium oxide can change its modulation performance. Material systems with a hexagonal structure (TT phase) represent the best optical performance [Aegerter, 2001]. When amorphous, niobium oxide turns into a dark brown color when reduced. When crystalline in phase structure, the material system colors blue. Films in the crystalline state have coloration efficiencies of ~25 C/cm2 [Romero, 2009]. Unfortunately, its low conductivity affects the rate of coloration as well as its optical properties.
Molybdenum Oxide
Molybdenum oxide is a material system that colors upon reduction. This material system has been investigated since the late 70s [Lampert, 1984]. Coloration effects in molybdenum oxide systems have been noticed for both crystalline and amorphous states. In the crystalline state, molybdenum oxide turns a purplish blue color. It was shown that the oxygen partial pressure of the material during growth has a large effect on its electrochromic characteristics [Miyata, 1985]. When modulated, a large portion of the change in absorabance occurs near the blue region of visible light [Guerfi, 1989].
Tantalum Oxide
Tantalum oxide is a material system that has been used as both an electrochromic cathodic electrode as well as a solid state inorganic electrolyte. When cycled in an aqueous electrolyte, it is clearly witnessed that tantalum oxide is a good proton conductor [Tepehan, 1999]. Depending on how the film is structured, you can grow an ideal capacitor dielectric [Cavigliasso, 1998]. In certain cases, you can combine layers of tantalum oxide with tungsten oxide to make infrared switching electrochromic devices [Franke, 2000].
Nickel Oxide
Nickel oxide is the preferred material system for electrochromic counter electrodes due to its optical properties during modulation. It has been observed that it can modulate from 77% in the bleached state to 35% in the colored state [Lampert, 1986]. When changing color, it is believed that the nickel composition is a mixture of oxide and hydroxide [Niklasson, 2007]. It has been shown to function with lithium intercalation but the
8 amount of visible modulation is enhanced when used in a proton exchanged system. Since the mechanism of coloration is still not fully understood, issues with consistent performance have been identified in the literature. Hybrid systems have been grown and improved the optical modulation of the films [Granqvist, 2003]. Overall, its low cost potential and enhanced performance has made this material system the system of choice for commercial applications.
Tungsten Oxide
Tungsten oxide is the most understood and studied material system in the electrochromic field. With reports dated since the early 70s, tungsten oxide has shown significant performance in both durability and electrical parameters. When investigated, it is seen that tungsten oxide undergoes the reaction listed below when reduced [Reichman, 1979],
+ - xM + xe + WO3 MxWO3 Eq. 11 where M can be protic or metal based. The most common phase studied is the amorphous phase but there are several studies on the mechanism of reduction in the crystalline phases [Granqvist, 2000]. One in particular does an extensive study on the mechanism for coloration of tungsten oxide [Niklasson, 2007]. Overall, tungsten oxide represents the ideal electrochromic material and is the most common material used for a working electrode in commercial settings.
1.2.3 Electrochromic Window Basics
Currently, inorganic oxides are the preferred material system implemented in electrochromic window applications. Structured in the same format discussed in Figure 2, electrochromic windows are composed of three central layers sandwiched between transparent conducting oxide (TCO) substrates. TCO layers are used to channel electrons between the working and counter electrode through an external circuit while not affecting the light that transmits through the film. The most common TCO material used is tin doped indium oxide (ITO) and flourine doped tin oxide (FTO). ITO is favored due to its electrical and optical performance while FTO is chosen for its cost and durability. ITO substrates are usually deposited via a sputtering technique while FTO is deposited using a chemical vapor deposition technique. FTO substrate cost is typically reduced as it is deposited during the float glass process. Surface roughness and porosity for sputter deposited samples can be controlled by varying the pressure and temperature during deposition. On the other hand, FTO samples typically have rough surfaces which create a hazy appearance after deposition. Both material systems can be deposited on either glass or plastic. If deposited on glass, it is important to note that some of the glass used may have iron impurities that affect the optical properties in the near-infrared section [Granqvist, 2007].
From the three central layers, the working electrode is typically seen as the active material that undergoes a color change. In the literature, there exist six material systems (tungsten oxide, titanium oxide, vanadium oxide, niobium oxide, molybdenum oxide, and
9 tantalum oxide) that represent cathodic coloration. Of these, the most investigated material is tungsten oxide due to its durability and optical performance. Each of these material systems were described in detail in the section above.
For the device to work properly there must be a counter electrode. Counter electrodes can either be active or passive. Passive counter electrodes are usually metals and are typically used during testing of working electrodes. In a commercial setting, counter electrodes tend to be active. Of the active materials, there exist eight material systems (vanadium oxide, chromium oxide, manganese oxide, iron oxide, cobalt oxide, nickel oxide, rhodium oxide, and iridium oxide) that represent anodic coloration. Nickle oxide is the preferred material of choice. Each these material systems were described in the above section.
The center electrolyte layer can either be of solid or liquid form. During testing of working electrodes, liquid electrolytes are typically chosen. The liquid can either be of an aqueous or non-aqueous state. Typical, aqueous electrolytes use either potassium or sodium based salts or they incorporate acidic solvents such as sulfuric acid. In the acidic electrolytes, protons are used as the ions used for charge balance [Sawyer, 1995]. In non aqueous electrolytes, there are a wide variety of solvents that can be used. The most common are propylene carbonate, acetonitrile, dimethylformamide, and tetrahydrofuran. Typical salts used in this case are lithium perchlorate, lithium hexaflourohosphate, and tetrabutylammonium perchlorate [Izutsu, 2009]. In a commercial setting, solid state electrolytes are preferred. There are two forms of solid state electrolytes: inorganic and polymer. Inorganic solid state electrolytes range from compositions of metal oxides to ion conducting glass, such as lithium phosphorus oxynitride (Yu, 1997 & Behl, 1973). Polymer based electrolytes range from a wide variety of polymers and salts. The most common polymer used is poly(methyl methacrylate), polyvinyl butyral, and polyethylene terephthalate [Granqvist, 2012, Kobayashi, 2003, & Kraft, 2006]. Salts can range from lithium bis(triflouromethanesulfonyl)imide to sodium and potassium chloride. Certain additives are incorporated in polymer electrolytes to improve the kinetic performance of the electrolyte as well as adhesion promoters.
Figure 5 shows the states of operation for an electrochromic window. In the rest state, the window remains transparent. During this state, positively charged ions are stored in the counter electrode. When a negative bias is applied, electrons travel from the counter electrode to the working electrode through an external circuit. The TCO layers are used to get the electrons from the counter electrode to the external circuit and into the working electrode. Simultaneously, the positive charged ions stored in the counter electrode travel through the electrolyte and into the working electrode to compensate the negative charge coming from the electrons. Once the process is complete, the working electrode undergoes a color change. In a case where the counter electrode is also active, both layers become dark after this process. Ideally, the color change has a memory effect and will remain dark if the potential is removed. Yet, depending on the electrolyte, a leakage current might exist which will cause the windows to switch back to their transparent state. To get the film back to its transparent state, a positive bias can be applied to shuffle the electrons from the working electrode back to the counter electrode through the external
10 circuit. The positive ions within the working electrode migrate back to the counter electrode through the electrolyte. After this process is complete, the window will become transparent. Depending on the potential applied, one can reach different color states.
Transparent Clear Semi-Transparent Dark State Transparent Oxide Transparent Oxide Transparent Oxide Transparent Oxide Active Passive/Active Active Passive/Active Electrochromic Counter Electrochromic Counter Layer Electrode Layer Electrode Partially Transmitted Transmitted Light Light Reflected Light Ion and Heat Conducting Electrolyte Ions Ions Ion Conducting Electrolyte Applied Voltage Reversed Applied Voltage a b Figure 5: Electrochromic window operation: a) transparent state, b) semi-transparent dark state.
1.2.4 Performance/Cost Issues & Competitive Technologies
Currently, energy savings from electrochromic windows come at a cost premium. Today’s sputter-coated electrochromic windows from manufacturers such as SAGE Electrochromics and Soladigm range from $50-80/ft2 [Baetens, 2010]. This elevated price is driven by high capital and operating costs, specifically related to sputtering multiple metal oxide layers. In addition to their high cost, their performance is also limited in spectral control. These traditional electrochromic windows do not strongly modulate the NIR regions of light, so the overall solar modulation is limited. Further, their modest NIR modulation is inevitably in sync with visible modulation which reduces the amount of natural light that enters a building. This requirement increases the use of artificial lighting and lowers the overall performance of the window.
Thermochromic windows are dynamic systems that offer enhanced performance over static coatings at a lower price point. Companies such as Pleotint and Ravenbrick offer thermochromic windows at a cost of $25-50/ft2 [Gagliardi, 2009]. Thermochromic systems respond direcly to a temperature change to vary the transmittance of the window. When heated, these windows darken, and when cooled they bleach. Being slightly more competitive in cost, thermochromic windows are even more limited in performance. Thermochromic windows largely modulate visible light, failing to address the equal amount of solar energy found in the NIR region. Additionally, thermochromic windows switch automatically, prohibiting users from any control of the system to match their preferences. Lastly, window coloration may not be uniform owing to thermal gradients, e.g. resulting from partial shading of the window.
11 Mechanically operated shading High systems, such as the ones Disruptive being developed by Lono Technology LLC/Smartershade, are dynamic products that control a window’s transmittance by using motors to align polarized films. Still in R&D, this technology has many drawbacks in performance. Film Performance STATIC COATINGS First, polarized films only Low affect the visible portion of the 0 10 20 30 40 50 60 70 80 solar spectrum. NIR polarizing 2 films can be introduced to the Window Cost, $/ft. system to enhance Figure 6: Window film performance versus cost per sq. ft. performance but this would increase module cost drastically. Second, due to their polarity, misaligned polar films can only bleach to ~50% transmittance which affects the system’s performance and fails to meet consumer expectations in large market segments (e.g. residential). Lastly, color change via mechanical components may affect the durability of the system.
To date, only one electrochromic glass company has demonstrated a coating fabricated completely by solution and non-vacuum processing for building applications. This demonstration is based on a WO3/polymer electrolyte/Prussian Blue architecture and is fabricated by Gesimat, a Germany-based company. Gesimat uses an electrochemical deposition technique to grow both their counter and working electrode on conductive glass substrates [Kraft, 2009]. After deposition, they use a lamination technique to complete their device fabrication. Gesimat has demonstrated that the use of a PVB-based polymer electrolyte can be effective in the performance of the EC window [Kraft, 2006]. Still, electrochemical deposition is known to cause issues with uniform deposition and is generally low throughput. In addition, Gesimat's EC material Prusian Blue does not show selective modulation and darkens to a non-neutral color, leaving a blue tint on the window upon coloring. These aesthetics will likely cause market acceptance problems.
In addition to Geismat, there are two European companies that use a hybrid solution/sputtered fabrication technique. ChromoGenics is a Sweedish company that focuses on roll to roll fabrication. Using a flexible substrate, ChromoGenics sputter deposits WO3 for the working electrode and a NiO complex for the counter electrode [Jelle, 2012]. Chromogenics then laminates both substrates with a Poly(methyl methacrylate) (PMMA) electrolyte to complete the device fabrication [Karmhag, 2009]. Similar to ChromoGenics, E-Control Glas is a Germany-based company who also uses a sputtering technique to deposit working and counter electrodes on conducting glass substrates. E-control glass laminates its deposited electrodes with a PVB-based polymer electrolyte [Jodicke, 2009]. Both companies are able to reduce manufacturing cost by incorporating polymer based electrolytes, yet their potential for cost reduction is limited due to their use of sputtering.
12
As seen in Figure 6, the only way electrochromic windows can become commercially viable is if they can have enhanced optical performance at competitive prices. One way to improve the performance of electrochromic windows is to develop near-infrared selective coatings. The next section will cover a new type of electrochromic film that takes advantage of an absorption feature seen in conductive nanocrystal materials. This type of electrochromic material is known as a plasmonic electrochromic material. Plasmonic electrochromic films will be explored in detail in the next section.
1.3 PLASMONIC ELECTROCHROMIC WINDOWS
Plasmonic electrochromism is a new approach developed by our group at designing the ideal dynamic window system. Using a different mechanism to optimize the amount of light that enters a building, plasmonic electrochromic films utilize the same electrochemical architecture described above. The main difference between plasmonic electrochromic films and traditional electrochromic films is the material systems used for the working and counter electrode. Prior to describing the material systems used in this body of work, a description of the fundamentals of plasmonic electrochromic materials will be covered.
1.3.1 Plasmon Fundamentals
Material systems such as metals and doped semi-conductors that have large free carrier densities interact with photons to generate a plasmon resonance. As seen in Figure 7a, plasmons are light induced collective electron oscillations. The frequency of oscillation for the bulk material is depicted by the following relation,
2 2 ne p Eq. 12 meo
where, n, is the density of free carriers, e, is the charge of an electron, εo, is the permittivity of free space, and, me, is the effective mass of an electron [Ghosh, 2007]. At the surface of the material, shown in Figure 7b, free electron interaction at the interface of the material system and surrounding dielectric environment create a surface plasmon. The frequency of oscillation for the surface plasmon is depicted in the following relation,
p sp Eq. 13 1
where, ωp, is the bulk surface plasmon frequency, and, ε, is the dielectric constant of the surrounding material [Noguez, 2007]. The intensity of light absorption from surface plasmons appears as reduction in transmittance for the overall system. When considering the geometrical dependency of surface plasmon, it is noted that a modification must be incorporated for spherical material systems. When spherical material systems have a diameter that is far less than the wavelength of incoming light, a localized surface
13 plasmon resonance is witnessed. Localized surface plasmon resonance, shown in Figure 7c, is depicted by the following relation,
p sp Eq. 14 21
where, ωp, is the bulk surface plasmon frequency, and, ε, is the dielectric constant of the surrounding material [Giannini, 2011]. Localized surface plasmon resonance appears as an absorption peak in the optical spectra of the film.
a b c
Dielectric + ++ +
- - - +++ +++ - - - - Electrons Material
Figure 7: a) Bulk plasmon resonance. b) Surface plasmon resonance. c) Localized surface plasmon resonance. Note electron cloud moves across both the material system and the surrounding dielectric environment.
Depending on the dielectric environment and density of free carriers, the location of the absorption feature varies. For metals with high electron concentrations located in air, absorption features are found in the UV-blue region of light. For doped semiconductor materials with relatively high carrier concentration densities, the surface plasmon frequency lies in the near to mid infrared region.
To date, there are several examples in the literature that make use of surface plasmon in optical and electrical applications [Warren, 2012]. For instance, metallic nanosructures have been leveraged for surface enhanced spectroscopy [Link, 1999 & Larsson, 2009], gas sensing [Liu, 2011 & Elghanian, 1997], and photovoltaic devices [Atwater, 2010]. The most prevalent application is sensing in both biological and environmental applications. Any modification to the surrounding dielectric environment of a metallic or doped semiconductor system is witnessed as a shift in the surface plasmon. The sensitivity of this optical change can be enhanced by increasing the surface area of the material. This is done by implementing nanostructuring of the material system. Implementing these type of sensors allows the capability of invasive motoring. This is of particular importance for several biological applications [Homola, 1999].
Outside of sensing, the field of plasmonics merges photonics and electronics at the nanoscale dimension [Boltasseva, 2011]. When making an integrated circuit based on plasmonic devices, there are four key elements that need to be developed: light source, light modulator, waveguide, and light detector. For a light source, indium gallium nitride semiconductor nanostructures have been identified as a potential candidate [Ozbay 2006].
14 In addition, plasmonic nanoantennas made from metallic structures have also been identified as potential light sources [Giannini, 2011]. Rows of metallic nanoparticles have been shown to guide light effectively at the nanoscale [Ozbay, 2006]. In the area of modulating, femto-second modulation has been achieved for metal-dielectric waveguides [MacDonald, 2009]. Near field electrical detection of optical plasmons have demonstrated the capability to detect plasmonic signals within the integrated circuits [Falk, 2009]. Overall, the field of plasmonics demonstrates the capability of enhancing transfer rates while reducing chip real estate within integrated circuits.
Most of the applications listed above, make use of the dielectric sensitivity when designing a surface plasmon application. In the case of plasmonic electrochromic windows, independent modulation of the free carrier density will control the optical properties of the window coating. The section below will go into more detail on the architecture of a plasmonic window.
1.3.2 Plasmonic Electrochromic Fundamentals
As mentioned in the above section, plasmonic electrochromic windows are based on the same electrochemical architecture as traditional electrochromic windows. The main difference lies in the mechanism of coloration. To make the ideal electrochromic window, selective modulation of the near-infrared region of light will be desired. As seen in Figure 8, near infrared selective films allow users to control the amount of solar heat that enters a building without affecting the amount of solar light that enters. In the winter time or on a cold day, a user can allow both the sun’s heat and light to enter a building. On the other hand, on a hot day or in the summer time a user can allow the sun’s light to enter the building while blocking the sun’s heat. By making an electrochromic window in this fashion, we can reduce both the amount of electrical light needed as well as heating and ventilation used.
Figure 8: Near infrared selective plasmonic electrochromic window operation.
15 When designing the near infrared selective electrochromic plasmonic window, the working electrode must have a surface plasmon absorption at the edge of the solar near infrared region and must be transparent in the visible region by nature. When the system is placed under a negative bias, electrons from the counter electrode will be injected into the working electrode through an external circuit. These electrons will enter the conduction band of the working electrode material system and shift the plasmon frequency absorption to lower wavelengths. During this process, ions of opposing charge will travel through the electrolyte and capacitively build up on the surface of the electrode to balance the electron charge that is being injected from the counter electrode. The amount of electrons that can be injected into the working electrode depends heavily on the surface area of the working electrode. As seen in Figure 9, a nanostructured electrochromic layer increases the surface area and enhances the dynamic spectral range during modulation.
Figure 9: Nanostructured electrochromic layers in plasmonic electrochromic windows increase dynamic spetral range during modulation by increasing the surface area.
The material system for the counter electrode must also remain transparent in both the visible and near infrared region of light. In addition, it must be able to provide the right amount of charge compensation needed by the working electrode. In the section below, two material systems will be discussed as potential working and counter electrode materials. These material systems consist of doped semiconductor materials known as transperant conducting oxides. Chapter 2 will cover the use of tin doped indium oxide and Chapter 3 will cover the use aluminum doped zinc oxide as a working and counter electrode. Chapter 4 will asses energy models that depict the efficiency of these window systems in residential and commercial buildings. Finally, Chapter 5 will go over the integration of these materials in liquid based plasmonic electrochromic window cells.
16 CHAPTER 2: INDIUM TIN OXIDE NANOCRYSTAL ELECTRODES
2.1 INDIUM TIN OXIDE MATERIAL SYSTEM
When choosing the appropriate material system for the working electrode, there a few properties that need to be addressed. First, the material must be visibly transparent. Second, it must be conductive and have a surface plasmon in the near infrared region of light. The ideal material system that contains both of these properties is a transparent conducting oxide. Transparent conducting oxides have been used in commercial applications, such as solar cells, liquid crystal displays, and refrigerated glass, for several years. Out of this material class, tin doped indium oxide (ITO) is the most robust and considered the standard choice for commercial applications [Chopra, 1983]. In this section, we will discuss in detail the material properties of ITO.
2.1.1 Introduction to Indium Tin Oxide
Indium tin oxide is a degenerately doped semiconductor material with high carrier concentration (~1020) and superb optical transparency (>90%) due to its wide optical band gap (>3 eV) [Tahar, 1998]. The natural crystal structure of un-doped indium oxide is that of a byxbyite structure [Yamada, 2000]. The byxbite crystal structure of indium tin oxide is composted of a body center cubic with a 10.117 angstroms lattice parameter and 80 atoms [Gonzalez, 2004]. As seen in Figure 10, a unit cell is composed of an indium atom at the center surrounded by oxygen atoms at the corner. Substitution doping of indium atoms by Sn4+ atoms are present within the crystal structure. Interstitial oxygen vacancies occur for every Sn atom that is incorporated in the crystal. This defect model was proposed by Frank and Kostlin in the early 80s and states that interstitial incorporation of oxygen is necessary to maintain charge neutrality. Under a reducing environment, interstitial oxygen is removed which releases two free electrons into the lattice. In an oxidizing environment, there is a tin-to-oxygen ratio of ~2, which validates this model. Ultimately, this model explains the variation in free electron concentration for a variety of thin film deposition techniques [Gilstrap, 2009].
Figure 10: Crystal structure of tin doped indium tin. Note oxygen vacancies in corners
17 As mentioned in the above section, incorporation of Sn atoms into the crystal structure creates degenerate doping. In the case where Sn4+ replaces an In3+ atom, we get an excess of electrons which leads to n-type degenerate doping. As electron incorporation increases, the Fermi energy of the system begins to shift toward the conduction band. A donor energy level is also formed near the conduction band and is dependent on the free carrier density and their effective mass [Gilstrap, 2009]. The electron donated from Sn substitution is considered as an impurity state within the electronic band structure. The spatial effects of the impurity state can be determined by the Bohr radius described below,
* a ro aH * Eq. 15 mm o
* where ao is the normal hydrogen bohr radius, εr is the relative permittivity, m is the appropriate effective mass, and mo is the free electron mass of hydrogen. In the ITO literature, it is reported that m*~3.5mo and εr~4 which leaves the Bohr radius to roughly 1.3nm [Edwards, 2004]. As the doping concentration is increased, impurity states begin to interact and form individual bands. These individual bands ultimately overlap upon further doping, which reduces the activation energy and at a critical point will become zero. This finite conductivity will theoretically occur at zero degrees Kelvin and represents a semiconductor to metal transition. For semiconductors, this metallic property state occurs at a separation distance that is comparable to twice the Bohr radius. The specific point where this transition occurs is given by the Mott critical electron donor concentration described below,
1 * 3 Na cH 24.0 Eq. 16
* where aH is the Bohr radius and Nc is the critical free electron concentration. For an ITO 18 -3 crystal with a 1.3nm Bohr radius the critical free electron concentration is 5.62X10 cm [Gilstrap, 2009].
It is important to note that an increase in Sn content is necessary to achieve enhanced conductivity via semiconductor to metal transition. Yet, the amount of conductivity enhancement due to Sn incorporation is limited. It is well documented that at specific Sn %, the conductivity of ITO crystals is reduced. Depending on the system, 10-15% Sn incorporation is the maximum allowed before this effect takes place [Gilstrap, 2009 & Kanehara, 2009]. The main cause of reduction in conductivity arises from ionized impurity scattering and phonon scattering [Yamada, 2009].
When describing the optical properties of ITO, it should be noted that the band gap energy for undoped indium oxide is 3.75 eV [Edwards, 2004]. This creates a band edge absorption close to ~350nm and is primarily limited to the UV-region of light. As you begin to incorporate Sn into the material system, the optical band gap is altered and gets blue shifted due to the Burstein Moss effect. Shown in Figure 11, the Burstein Moss shift occurs when the conduction band begins to get filled with free electrons. These free
18 electrons increase the energy gap interaction with electromagnetic waves. Ultimately, this is seen as an increase in the optical band gap [Gupta, 1989].
Conduction Band
Eg + ΔEg
Eg
Undoped Valance Sn doped Band In2O3 In2O3
Figure 11: Burstein Moss shift of degenerately doped In2O3 with Sn. Optical band gap of undoped indium oxide and Sn-doped indium oxide.
ITO thin films are extremely transparent (>90%) in the visible region of light (400- 780nm). As discussed in chapter 1, light absorption occurs in the near infrared region due to plasmon resonance. The location of the surface plasmon resonance depends heavily on the free carrier concentration, which is tied to the amount of dopant incorporation. This material property establishes the groundwork for developing near infrared selective electrochromic coatings. In the next section, we will describe in detail synthetic development of nanocrystal indium tin oxide.
2.1.2 Colloidal Synthetic Development
There are several synthetic techniques that can be chosen to make nanocrystals of indium tin oxide. Some examples range from hydrothermal synthesis [Xu, 2006], solvothermal treatment [Ba, 2007], sol-gel combustion hybrid [Han, 2007], microwave assisted synthesis [Hammarberg, 2008], and sol-gel chemistry [Ba, 2006]. The preferred method for this project consists of a colloidal technique. Colloidal synthesis is an inexpensive, simple but strong approach at making semiconductor and metal nanocrystals. It is very powerful in controlling size, composition, and shape. In addition, material variations such as core shell nanocrystals allow material development for a variety of applications [Murray, 2000]. In the past years, introduction of degenerate doping via colloidal techniques have opened the field to development of a variety of material systems [Norris, 2008]. Figure 12 shows a typical set up used for the synthesis of colloidal nanocrystals which is made up of a three neck flask, a heating source, and a temperature controller.
19 During synthesis, a user mixes precursor salts, surfactants, and a solvent. These components are critical in the development of the material system. For undoped materials, precursor salts constitute the ground work from which the nanocrystals form. Dopant incorporation can also be controlled by adding specific precursor salts. Surfactants play a crucial role in the growth and shape of the nanocrystal. In addition, they enable the nanocrystal to be stable in a variety of solvents. Depending on the synthetic procedure, the components can either be mixed prior to heating or can be injected at specified times and temperatures. In addition, the flasks may also be placed under vacuum, in air, or in a specified gas environment. If the synthetic recipe requires a water free environment within the flask, a de-gassing step maybe used prior to starting the synthesis
Figure 12: Typical configuration used for colloidal synthesis of nanocrystal materials.
During the synthesis, there are several reactions that occur which lead to the creation of a monodisperse nanocrystal. Figure 13 shows the stages of growth within the synthesis. At the beginning, precursor salts begin to form monomers via nucleation. As the monomers interact with the surfactants, they begin to grow. During this phase, the dopant atoms begin to get incorporated into the growing crystal. As time progresses, the nanocrystal continues to grow. Depending on the material system, nanocrystal growth can be controlled by temperature or pressure. Throughout this process, surfactants bind to the nanocrystal surface and mediate a uniform growth. In addition, they stabilize the nanocrystal within the solution and prevent them from falling out of solution. Surfactants can be engineered to have a variety of capping ends and chains. This allows nanocrystals to be stable in a variety of environments [Murray, 2008].
20
Dopant Precursor Pre Cursor Surfactant Salt Salt
Figure 13: Colloidal growth of a doped nanocrystal from beginning to end.
2.1.3 Colloidal Synthetic Development of Indium Tin Oxide Nanocrystals
Within the literature, there are several examples that use colloidal chemistry to grow nanocrystals of indium tin oxide [Buhler, 2007 & Kanehara, 2009]. In this section, we will describe the recipes that where chosen for this project. In particular, we will highlight any modifications made.
Materials
The materials that were used in all of our syntheses are as follows: Indium acetylacetonate (In(acac)3, 99.99%), tin bis(acetylacetonate) dichloride (Sn(acac)2Cl2, 98 %), tin acetate (Sn(Ac)4 99.99%), myristic acid (MA, ≥ 98%), 1-octadecene (ODE, 90%), and oleic acid (OLAC, 90%) were purchased from Aldrich and used without further purification. Oleylamine (OLAM, 90%) was obtained from Acros.
Synthetic Method
The synthesis of ITO nanocrystals (NCs) is based on slight modifications of literature protocols [Gilstrap, 2008 & Choi, 2008] and is carried out under an inert atmosphere using standard Schlenk-line techniques. In detail:
a) 4nm diameter ITO NCs with 16.8% of Sn (Figure 14): In(acac)3 (1 mmol), Sn(acac)4 (0.2 mmol), and MA (3 mmol) were mixed with 20mL of ODE in a three-neck flask and degassed under vacuum at 110°C for 2h. Afterwards, the temperature was increased to 295°C and 1mL of a previously degassed 3M solution of OLAM in ODE was rapidly injected. The solution temperature dropped to 280°C and was maintained for 1h. The solution became yellow in color almost instantaneously, later turning to orange and finally dark green within 10 minutes of the injection. The temperature was further reduced to 240°C for an additional 1h. The NCs were collected by adding 10mL of
21 chloroform to the final reaction mixture and precipitating with ethanol. Further precipitation and washing were performed with hexane/ethanol. Finally, the NCs were dispersed in a 1:1 mixture of octane:hexane.
Figure 14: Colloidal growth of 4nm ITO nanocrystals with their statistics.
b) 7nm ITO NCs with 4.4% of Sn (Figure 15): A solution containing In(acac)3 (0.5mmol) and Sn(acac)2Cl2 (0.027mmol) in 7g of OLAM was mixed in a 50ml three-necked flask and magnetically stirred under nitrogen at 250°C for 5h. The solution became clear as the precursor salts dissolved, and progressed to a dark yellow color, followed by a dark blue-green color upon reaching 250°C. The final product was collected after repeated steps of precipitation with ethanol, centrifugation, and redispersion in hexane and 20μL OLAM and 40μL OLAC were added to further stabilize the NC dispersion. After three cycles of redispersion in hexane and reprecipitation with ethanol, ITO NCs were redispersed in a 1:1 mixture of octane:hexane .
Figure 15: Colloidal growth of 7nm ITO nanocrystals with their statistics.
22 c) 10nm ITO NCs with 4.4% of Sn (Figure 16): NCs were obtained through the same procedure described for the 7nm NCs by using 2mmol of In(acac)3 and 0.11mmol of Sn(acac)2Cl2 .
Figure 16: Colloidal growth of 10nm ITO nanocrystals with their statistics. d) 12nm ITO NCs with 4.4% of Sn (Figure 17): NCs were obtained through the same procedure described for the 7nm NCs by using a 25mL flask and reducing the OLAM to 2.3g.
Figure 17: Colloidal growth of 12nm ITO nanocrystals with their statistics.
23 e) 12nm ITO NCs with 9.4% of Sn (Fig. 18): NCs were obtained through the same procedure described for the 7nm NCs by increasing the Sn(acac)2Cl2 to 0.054mmol.
Figure 18: Colloidal growth of higher doped 12nm ITO nanocrystals with their statistics.
After synthetic development, the nanocrystal solution was cleaned several times to remove any excess surfactant and by-products. The cleaned solution of nanocrystals is shown in Figure 19a. In addition, their optical properties in a solution of tetrachloroehylene are also represented in Figure 19b. Note that the location of the surface plasmon resonance depends on the amount of Sn incorporation.
a b
Figure 19: a) Solution of ITO nanocrystals. b) Optical density of ITO nanocrystals at varying Sn content
24 Elemental analysis was performed by induced coupled plasma atomic emission spectroscopy (ICP-AES) with a Varian 720/730 Series spectrometer. The ITO samples were digested in concentrated HCl. The relative error on the extracted Sn content was within 3% of the reported percentage, as evaluated on the basis of 9 replicates per each measurement. Table 1 shows the results for the synthetic sets labeled above.
Table 1: ICP-AES results of all ITO batches used in this project.
2.2 DEVICE FABRICATION
When trying to develop uniform nanocrystal films, there are several techniques one can use. This section will cover the fundamentals behind five of these techniques: spray deposition, dropcasting, ink jet printing, dip coating, and spin casting. The latter section will focus primarily on spin casting, since it was the deposition technique of choice for this project. After successful deposition, post processing is required to remove the surfactants from the nanocrystal surface. By doing so, a conductive network is established. This section will cover in detail the post processing steps investigated on this project. Once a conductive network is built, optical properties of the nanocrystal films are measured. The last part of this section will cover in detail the optical effects of changing the dielectric environment around the nanocrystal surface and coupling of the nanocrystal surface plasmon.
2.2.1 Deposition Methods
Spray Casting
Spray casting of nanocrystal inks to create uniform nanocrystal films has been used in several applications, such as photovoltaics [Akhavan, 2010] and optical coatings [Manifacier, 1981]. This tequnique works by using an aerosol to transport liquid solutions of nanocrystal inks onto substrates. Depending on the type of solvent you are working with, the substrates may be heated. Figure 20 shows a typical layout for a commercial spray coater. A nozzle placed above the substrate is pressurized pushing an aerosol nanocrystal solution onto a substrate of choice. Depending on the application, one can
25 control the area to be deposited and the extent of deposition by changing the pressure used during deposition and the diameter of the nozzle used.
Nozzle
Substrate
Heater
Figure 20: Spray deposition of nanocrystal films on a heated substrate.
Dropcasting
Dropcasting is the simplest form of deposition. A known concentration of ink is dispensed onto a substrate and left to dry. In some cases, the substrate is also heated to speed up the drying of the solvent. Unfortunately, its simplicity also leads to un-uniform film thickness. As the solvent begins to dry, the nanocrystals have a tendency to also move. If the solvent dries at a different rate throughout the film, the nanocrystals will begin to build up at the sections that dry last. Typically, solvents dry at the center first and eventually reach the edges of the film. This effect leads to thick edges and a thin center and is known as the “coffee ring”. Figure 21 demonstrates this effect.
Substrate
Heater
Figure 21: Dropcasting of nanocrystal films on a heated substrate.
Ink Jet Printing
Very similar to the printers that you find in your home, ink jet printers use nozzle heads to dispense well defined volumes of solution at specified locations on the substrate. This technique is not really preferred when making large area nanocrystal films but rather in an application where the precise location is warranted. Commercial ink jet printers have
26 been used for locating photoluminscent nanocrystals. Figure 22 shows a typical commercial ink jet printer.
Figure 22: GIX Microplotter II by sonoplot for use with organic LED nanocrystals
Dipcoating
Dipcoating of nanocrystal films is one of the most robust techniques used. Taking advantage of the Van der Waals forces, dipcoating attracts a thin layer of nanocrystals from the solution on to the substrate surface. The speed of extraction controls the uniformity and the thickness of the film. London-Van der Wall intermolecular forces limit the extent of thickness one can gain per coating. To effectively grow a film by this method, one sticks a substrate into a known concentration of nanocrystal ink and pulls it out at a known angle and speed. The solvent left on the film after extraction is dried and the film is left on the substrate. Dipcoating leads to deposition on both sides of the substrate. A layer on one side is usually removed. Figure 23 shows the process steps of this technique [Scriven, 1988].
Figure 23: Process steps used in a dipcoating technique.
27 Spincoating
Spincoating is the deposition technique of choice for this project. Primarily, it is the most robust technique used for developing uniform films as it uses centrifugal forces to evenly distribute the nanocrystals among the substrate. Figure 24 shows the steps used in this technique. In the first step, a substrate is placed on a chuck. A known volume and concentration of nanocrystal solution is dispensed on the substrate. The substrate/solution is then spun at a known RPM. Different stages of spinning can be programmed in order to spread the nanocrystals on the substrate. A final spin is used to dry the solvent. Depending on the viscosity of the solvent and the rate at which you spin, one can control the overall uniformity of the film. The length at which you spin also affects the film properties [Sriven, 1988].
Figure 24: Process steps used in a spincasting technique.
A spincasting technique was used to generate thin films of ITO nanocrystals. Glass and transparent conducting oxide substrates were cleaned via sonication in a three step process: 15 min de-ionized water with 2% Helmenex solution, 15 min acetone, 15 min isopropanol. Three rinses were performed between each sonication step. Prior to determining the appropriate spin conditions, several solvents, nanocrystal concentrations and spin recipes were investigated. When using toluene as a solvent, non-uniform films where created for a variety of spin rates. Figure 25 shows results of these spin rates on a silicon substrate.
28
Figure 25: Toluene spincasted samples at a variety of spin rates dictated in rotations per minute (RPM)
When using only a solution of octane, uniform films where created at really high RPM rates. Unfortunately, the films where very thin to be successful. Figure 26 shows the results using only octane.
1800 2000
2200 2400
Figure 26: Octane spincasted samples at a variety of spin rates dictated in rotations per minute (RPM)
29 A reduction in the spinrate for octane films showed a reduction in the uniformity of the films. Ultimately, a coffee ring effect took place. The results of these films are shown in Figure 27.
200 RPM 400 RPM 600 RPM 800 RPM 1000 RPM
Figure 27: Octane spincasted samples at a variety of lower spin rates dictated in rotations per minute (RPM)
To improve the uniformity of the film, several combinations of solvents where tested. Of these combinations, a one to one combination of hexane octane proved to give the best results. Using a one to one octane/hexane solution of ITO nanocrystals (~67 mg/ml), 30ul were dispensed on a 2.5cm x 2.5cm glass substrate. Spinning recipe consist of an initial 1000RPM spin for 30 seconds followed by a 4000RPM spin for 20 seconds. Figure 28 shows the top down and cross section scanning electron images of the ideal film.
ITO Nano
100 200 nm Substrate
Figure 28: One to one hexane/octane spincasted samples top down and cross section views.
2.2.2 Post Processing Techniques
After successfully depositing the ITO nanocrystals on the substrate, post processing must be completed to make a conductive film. As mentioned in the colloidal synthesis section above, nanocrystals in solution have insulating hydrocarbon ligands on the surface. These surfactants have up to 18 carbon molecules per chain. These long hydrocarbon chains prohibit electrons from hopping between nanocrystals in the film. Several techniques were used to remove the ligands on the surface. The first technique investaged dealt with
30 thermal decomposition of the ligands in a variety of gasses. When decomposed in air, the films remained visibly transparent but they did not become conductive. Upon further investigation, it was determined that inadvertent filling of oxygen vacancies within the ITO crystal lattice reduced the free carrier concentration [Yamada, 2000]. Figure 29 shows the crystal structure of ITO before and after annealing in air.
Oxygen Tin Indium Oxygen Vacancy
Tin Oxide Complex
Before Air Annealing After Air Annealing
Figure 29: Oxygen vacancy of ITO nanocrystal thin films when annealed in air.
When evalutaing the optical properties of the film annealed in air, it was observed that the NIR surface plasmon absorption also disappeared, consistent with the fact that the films were not conductive. A drop in free carrier concentration will also cause a shift in the localized surface plasmon, shown in Figure 30.
Figure 30: Optical density of ITO nanocrystal thin films annealed in air.
31 To prohibit a loss of free carrier concentration during annealing, the oxygen partial pressure of the annealed films was changed. Mixing 0.5 to 5% oxygen with nitrogen, several thin films of ITO where annealed. Again, the films were transparent but not conductive leading to the conclusion that even at 0.5% oxygen partial pressure, one fills in the oxygen vacancies of the film.
An alternative approach using ozone treatment was investigated. UV-ozone treatment was performed on as deposited samples of ITO nanocrystal thin films to decompose the surfactant on the nanocrystal surface. They were left in the UV-ozone treatment for a variety of time but unfortunately the oxygen vacancies where also filled in by the ozone in the environment. Thermal decomposition of the nanocrystal in a purely inert environment was investigated. The outcome did give a conductive film but unfortunately all the carbon from the decomposed ligands remained on the film and the visible transparency was lost as the films became dark brown, as shown in Figure 31.
Figure 31: Thin films of ITO nanocrystals annealed in an inert environment.
Meerwein Ligand Exchange
To prevent carbon based ligands from turning the film dark, a ligand exchange of the nanocrystals in solution was investigated using Meerwein based salts [Rosen, 2012]. Figure 32 shows the three steps of the ligand exchange. An oleic acid capped ITO ink composed of hexane was placed on top of dymethylformamide (DMF). Due to their difference in polarity, the samples are phase segregated. Trimethyloxonium tetrafloroborate (TMO) was then added to the vial and mixing of the solution commenced. After a few minutes, the DMF solution turned a greenish blue color indicating that the nanocrystals were now stable in the opposing polar solvent. The hexane layer remained a cloudy color and was composed of all the excess oleic acid that came off the nanocrystal surface. The solution was then mixed with toluene and centrifuged for 10 minutes to clean out the excess by products. After three cleanings, the ITO nanocrystals were successfully brought back into a DMF solution. In this configuration, DMF and BF4 ions act a pseudo ligand that stabilizes the nanocrystals within the solution. By partaking in
32 this ligand exchange, the nanocrystal surfaces were now free of a long carbon based chain.
Octane/Hexane
ITO in ITO in
Octane/Hexane DMF
DMF
Figure 32: Solution ligand exchange of olaic capped ITO nanocrystals in hexane with trimethyloxonium tetrafloroborate (TMO) in DMF. From left to right, solution before TMO is added, solutions during TMO addition, solution after cleaned in DMF.
Unfortunately, DMF is not an appropriate solvent for spincoating. Figure 33 shows non- uniform films that where spincasted using DMF.
Figure 33: Thin films of ITO nanocrystals spincasted from DMF. Note the non-uniformity.
33 To improve the uniformity of the BF4/DMF capped ITO nanocrystal films during spincasting, acetonitrile was used as the solvent. For the nanocrystal solution to remain stable within acetonitrile, 2% DMF had to be added to the solution. Figure 34 shows improved uniformity but not in comparison to the octane/hexane samples.
Figure 34: Thin films of ITO nanocrystals spincasted from acetonitrile/2%DMF solution. Note the un-uniformity. Even with the improvement, the nanocrystal uniformity was unacceptable. In addition, the necessity of DMF on the surface for stabilization also meant that carbon would remain on the surface. To eliminate these issues, a ligand exchange of as deposited films from hexane/octane solution was investigated. Two candidates where chosen. The first candidate was the trimethyloxonium tetrafloroborate (TMO) salt. Figure 35 shows the process steps of the ligand exchange. After deposition, the nanocrystal film was immersed in a 20mM solution of anhydrous acetonitrile. Both the TMO solution and the ligand exchange were mixed and conducted inside of a nitrogen filed glovebox. After 40 minutes, the film was removed and rinsed in a one to one chloroform/acteonitrile solution to remove any excess oleic acid that was removed from the surface. The films where then annealed in an argon environment at 300C for 30 min. The product was a conductive, transparent ITO nanocrystal film.
Oleic Acid Ligand 20mM Meerwein Salt In Acetonitrile for 40min
Meerwein Ligand Processed Thermally Anneal in ITO Film Argon @300C for 1hr Conductive Film Figure 35: TMO ligand exchange process in order to create a conductive nanocrystal film
34 To validate that the ligands were removed, Fourier transform infrared spectroscopy (FTIR) was conducted at every step of the process and the results are shown in Figure 36. The as deposited sample shows two absorption peaks due to C-H stretiching near 2800 cm-1. These absorption peaks come from the carbon hydrogen bonds located on the surface of the nanocrystals. After the ligand exchange is performed, the absorption peaks minimize to almost zero which indicate that the C-H bonds where removed from the surface. After annealing in Ar for 30 min, the C-H absorption peaks completely disappear.
1
0.9
0.8
0.7 0.6 0.5
0.4
0.3
Normalized Transmittance (Fraction) 0.2 As Deposited 20mM TEO 0.1 300C Ar Anneal
0 4000 3500 3000 2500 2000 1500 1000 500 -1 Wavenumber (cm ) Figure 36: FTIR of every phase in the TMO ligand exchange process. Note removal of C-H bond stretching at ~ 2800 cm-1 is removed after ligand exchange.
To ensure that the nanocrystal surface is not etched during the ligand exchange, X-ray diffraction (XRD) was performed for every processing step. The results are shown in Figure 37. It was noticed that no apparent etching occurred and the peaks remained the same width for each processing step.
1
0.9 As Deposited 20mM TEO 0.8 300C Ar Anneal 0.7 0.6
0.5
0.4
0.3
Normalized Intensity (Fraction) 0.2 0.1 0 20 25 30 35 40 45 50 55 60 Two Theta (Degrees) Figure 37: XRD of every phase in the TMO ligand exchange process. Note removal of C-H bond stretching at ~ 2800 cm-1 is removed after ligand exchange.
35 Formic Acid Ligand Exchange
The second ligand exchange candidate consisted of formic acid [Zarghami, 2010]. Formic acid has the same capping end as oleic acid, but a substantially smaller chain. One advantage of using formic acid is that it has a very low decomposition temperature. Figure 38 shows thermogravimetric (TGA) measurement of formic acid in a nitrogen environment. It was noticed that decomposition begins slightly under 200°C.
Figure 38: Thermogravimetric analysis of formic acid in a nitrogen environment.
Similar to the TMO ligand exchange described above, the as deposited ITO coatings were immersed in a solution of 1M formic acid in acetonitrile. Figure 39 shows the process steps taken for this exchange. The ITO coatings remained immersed for 40 minutes. Once completed, the films where removed from the solution and rinsed with a solution of acetonitrile. The processed films where then annealed at 250°C in an argon environment. After heating, the films were both transparent and conductive.
36 Oleic Acid Ligand 1M Formic Acid in Acetonitrile for 40min
Formic Acid Ligand Processed Thermally Anneal in ITO Film Argon @250C for 1hr Conductive Film
Figure 39: Formic acid ligand exchange process for ITO nanocrystal films.
Similar to the TMO exchange, FTIR was conducted at each step of the process to confirm that the surfactant was removed, shown on Figure 40. The C-H absorption peaks are present near 2800 cm-1 for the as deposited films. After the formic acid ligand exchange, the peaks are reduced close to zero. After annealing in air, the C-H absorption peaks completely disappear. One should note that the surface plasmon frequency shifts to lower wavenumbers at each of the processing steps. This optical feature will be discussed in more detail in the following section.
Figure 40: FTIR of formic acid ligand exchange process for ITO nanocrystal films.
37 XRD was performed at each of the processing steps to see if there was any change in the material system. Figure 41 shows that there was no apparent change in crystal structure or in nanocrystal size as the peaks remain the same width.
Figure 41: XRD of formic acid ligand exchange process for ITO nanocrystal films.
Sheet resistance of formic acid post processed films was performed at increasing annealing temperatures (Figure 42). As the annealing temperature increases, a reduction in sheet resistance was noticed. After 500°C, the sheet resistance became stable. It is estimated that at this temperature, the nancrystals begin to sinter at the walls, which initiates pathways for electron transport. Prior to sintering, the main form of electron transport comes from a hopping mechanism [Vanmaekelbergh, 2005]. The distance between nanocrystals is largest at low annealing temperatures, creating a larger resistance to electron transport. As you increase the annealing temperature, the nanocrystal distance is reduced until nanocrystals bind together at the wall. This mechanism is responsible for a decrease in nanocrystal sheet resistance.
38
Figure 42: Sheet resistance as a function of annealing temperature for 9.4% Sn doped ITO nanocrystal films with formic acid ligand exchange.
2.2.2 Plasmon Coupling and Dielectric Effects
As described in the above section, post processing of the nanocrystal films led to a change in the surface plasmon location between each processing step. To determine the mechanism for these shifts, extended Drude models where used to fit the optical transmission spectra [Mendelsberg 2012, Ederth 2003, Mergel 2002, Hamberg, 1986 & Solieman, 2005]. The four part electronic susceptibility frequency model used in the simulation is described below,
)( B BG UV D ()()( ) Eq. 17 where εB is the dielectric background (or otherwise high frequency dielectric constant), χD is the free carrier contribution due to the Drude theory, χBG is the contribution due to the band gap, and χUV is the UV absorption deep in the conduction band.
The free carrier concentration due to the Drude theory is described below,
2 )( P Eq. 18 D 2 i )(
39 where ωp is the bulk plasma frequency described in the first chapter and Γ(ω) is the frequeny dependent damping constant. The frequency dependent constant used for this study is based on a semiempirical model that accounts for the fact that ionized impurity scattering is the dominant scattering mechanism in transparent conducting oxides at optical frequencies. Based on the energy loss calculations by Gerlach [Gerlach, 1986], the frequency damping constant is described below,
2/3 f L f H )())(1()()( Eq. 19 x where ΓL is the low frequency damping constant, ΓH is the high frequency damping constant, and Γx is the crossover frequency. The frequency function in the equation is described below,
1 f )( Eq. 20 x )exp(1 w where Γw is the cross over width.
Bandgap absorption was accounted for using the O’Leary-Johnson-Lim model [O’Leary, 1997], which has been applied to ITO previously [Mergel, 2002]. However, this model assumes absorption into unfilled parabolic bands, which is certainly not the case for highly doped ITO. As such, this model was chosen to qualitatively account for the bandgap and little trust was put into the extracted bandgap parameters.
The third component to the susceptibility was a harmonic oscillator which describes UV absorption from the valence band to the upper half of the conduction band [Mergel, 2002]. For this work, a simple harmonic oscillator was used and was typically restricted to above 60,000 cm-1. The inaccuracy of the O’Leary-Johnson-Lim model for a partially filled, non parabolic conduction band necessitates this term in the optical model.
For each geometry, the Maxwell-Garnett (MG) effective medium approximation was used to model the ITO nanocrystal layers. Typically the Bruggeman effective medium approximation is used for conductive nanocrystal films when the volume fractions are above about 0.3 [Ederth 2003 & Solieman, 2005]. However, it was found that this model did not provide as good of a fit for the thin films in this study, which had volume fractions of 0.4 and higher. This outcome was surprising since Bruggeman’s model accounts for electronic coupling between particles and the ITO films were all laterally conductive when tested with 4 point probe measurements.
By starting with a large number of very different initial guesses, a high level of confidence was obtained for the fitted plasma frequency since it converged to the same value in every case. Typically, film thickness extracted by the model was also reliable and agreed well with profilometer measurements. Absolute transmittance data was
40 modeled using a SCOUT software package (www.wtheiss.com) to accurately account for the geometry of the system. Figure 43 shows uniform fits for each step in the formic acid ligand exchange.
Figure 43: Optical fits for surface plasmons of ITO nanocrystal films at each step of the formic acid processing stage. NOTE: Fits are in yellow.
From the optical fits, several parameters such as carrier concentration, dielectric environment, nanocrystal volume fraction, and variations in surface damping are identified and displayed in Table 2. It was determined that the shifts in the absorption peak were primarily ascribed to an increasing ITO volume fraction. In the solvent solution, the volume fraction is extremely low. As you move along the processing steps, the ITO volume fraction begins to increase. The ITO volume increase in each step enhances the coupling between adjacent nanocrystals and raises the average dielectric environment surrounding the nanocrystals [Ghosh 2007 & Hallas, 2011]. There was notably little change to the actual plasmon frequency during the film processing.
Table 2: Optical fits parameters for surface plasmons of ITO nanocrystal films at each step of the formic acid processing stage.
41 2.3. ELECTROCHROMIC PROPERTIES OF INDIUM TIN OXIDE NANOCRYSTALS
Electrochromism of nanocrystals have been investigated for several material systems. For instance, electrochemical doping of CdSe NC films was previously shown to bleach the exciton peak at the onset of the visible band gap absorption and to introduce a new intraband absorption peak in the far infrared region [Wang, C. 2001 & Guyot-Sionnest, 2003]. However, the LSPR modulation of our ITO NCs is a collective response of the free electrons, more analogous to the electrochemical response of Au or Ag LSPR [Ung, 1997 & Novo, 2009]. In such metal nanostructures, acute screening by a high background charge density limits the shift of the LSPR peak to 10 or 20 nm, at most. In addition, spectroelectrochemistry has been investigated on antimony doped tin oxide systems [Zum Felde, 2000 & Pflughoefft, 2002]. These systems showed a change in plasmon intensity but did not specifically show a change in plasmon frequency. While it was shown very recently that the localized surface plasmon of copper deficient Cu2S and Cu2Se NCs shifts in response to oxidizing or reducing chemical treatments, this composition-driven optical response relies on the unusually high mobility of Cu+ ions and the mechanisms for reversing oxidative doping remain uncertain [Luther, 2011 & Dorfs, 2011]. In addition, it is not practical when implementing in a commercial application. The goal of this section is to show the capability of electrochemically controlling the surface plasmon frequency of indium tin oxide nanocrystal thin films. The first section will cover fundamental electrochemistry techniques used throughout this project. The second section will discuss the results for a variety of variations. Finally, the section will end with an overview of optical modeling that describes the mechanism for optical modulation as well as results for its performance.
2.3.1 Electrochromic Characterization Techniques
This section will cover a variety of electrochemical techniques that are performed with the use of a potentiostat and custom made electrochemical cells. For all experiments, a three electrode cell will be considered standard.
Cyclic Voltammetry
Cyclic voltammetry is a very powerful technique that measures current as a function of an applied potential. With this technique, one can determine the potential needed to reduce and oxidize the material system being investigated. In addition, it allows the capability of quantifying where charge is originating from and whether it is diffusive or capacitive. Durability tests are usually performed using this technique. Figure 44 shows a typical set up that is used for cyclic voltammetry. Composed of three electrodes immersed in a liquid electrolyte, cyclic voltammetry controls the potential across the reference and working electrodes while measuring the current that flows between the working and counter electrodes. The different combinations one can assemble makes this technique highly versatile. Primarily, one can choice a variety of salts, solvents, and electrodes. Care should be taken when choosing the reference electrode as it must remain stable in the solvent of choice. In addition, each configuration has a stability range from
42 which one can test safely. Beyond this range, one can break down the electrolyte or damage the working electrode permanently or break down the electrolyte. One can also perform this technique on solid state devices. For this project, non-aqueous solvents are used in combination with pseudo reference electrodes.
Change Voltage Measure Current
Reference Working Counter Electrode Electrode Electrode
Liquid Electrolyte
Figure 44: Electrochemical cell used for cyclic voltammetry test. NOTE: The electrolyte can also be solid state.
Chronoamperometry
In this technique, one uses the same electrochemical set up shown in Figure 44. During this test, a sourced current is applied across the working and counter electrodes. As you source the current, the potential is measured across the working and reference electrodes. This technique is used to determine variations in potential across the film during oxidation and reduction states. Charge measurements are also made by this technique. The charge capacity can be determined by integrating the time it takes the working electrode to reach the boundaries of the potential reached.
Chronopotentiometry
This technique is similar to chronoamperometry except it applies a set potential across the working and reference electrodes and measures the current that is driven across the working and counter electrodes. This technique is powerful in understanding the kinetics of switching between the working and counter electrodes. Additionally, it can be used to determine the leakage current across the electrolyte solution. One can also use this technique to measure the amount of charge capacity that is collected in the working electrode for a given potential range.
43 Spectroelectrochemistry
Spectroelectrochemsitry is a technique that combines electrochemical manipulation with optical detection. As seen in Figure 43, an optical path is measured across the working electrode while driven by a specified potential. Spectroelectochemistry utilizes the electrochemical architecture described in Figure 45. Depending on the application, one must control the path length of the electrolyte as it will affect the optical measurement. When trying to measure samples in the near infrared portion, water cannot be used as a solvent as it has a high absorption affinity in that region of light. Several electrochemical techniques can be performed on the sample while optical monitoring occurs.
Working
Electrode
Light Light Detector Source Liquid
Electrolyte
Figure 45: Cross section of a spectroelectrochemical cell configuration
For this project, three variations of custom built liquid cells were designed. Figure 44 shows the first generation designed that consisted of a sealed cell that can be placed in between a dual beam spectrometer. Unfortunately, this design did not adequately seal and had to be dismissed for safety concerns. Figure 46 and 47 show the cell designs that where chosen for this project. Figure 47 shows a design that gives the user access to a wide variety of vertical lengths. Figure 48 shows a design with a fixed light path distance. Both of these designs are used with a optical fiber spectrometer.
Figure 46: First generation spectroelectrochemical design used for this project
44
Figure 47: Second generation spectroelectrochemical design used for this project
Figure 48: Third generation spectroelectrochemical design used for this project
2.3.2 Electrochromic Figures of Merit
When describing the performance of electrochromic coatings, there are four main figures of merit described. The first figure of merit deals with the amount of solar modulation. The solar transmittance, Tsol, is described in the below
*TE fsol Tsol Eq. 21 E sol where Esol is the solar black body radiation at the surface of earth and Tf is the transmittance through the electrochromic film. A modification of this equation gives the
45 solar transmittance of only the near infrared region of light. This near infrared solar transmittance, TNIR, is given by the relation below,
*TE _ NIRfNIR TNIR Eq. 22 E NIR where ENIR is the black body radiation of just the near infrared portion of light (780- 2500nm) and Tf_NIR is the film transmittance in the near infrared region of light. The luminous solar transmittance, Tlum, that is only visible by the human eye is depicted below,
** TEE sol flum TLum Eq. 23 * EE sol lum where Elum is the phototic spectral sensitivity of the human eye. The last figure of merit describes the efficiency of modulation and is called the coloration efficiency. It is the ratio of optical density and the injected charge per unit area. The relation is depicted below,
OD CE Eq. 24 / AQ where ΔOD is the change in optical density, Q is the injected charge during modulation, and A is the area of the film. Note should be taken that this figure of merit is not a percentage and has units of cm2/C. The larger the coloration efficiency, the better performing the film is.
2.3.3 Indium Tin Oxide Electrochromic Effect
Using the electrochemical configuration described in the previous section, thin films of formic acid processed ITO nanocrystals deposited on glass were electrochemically modulated. Prior to testing, gold electrodes of ~110nm in thickness were thermally evaporated at the top of the films. For our initial test, a 0.1M lithium perchlorate in propylene carbonate solution was used with separate lithium foils used for both the counter and reference electrodes. Figure 49 shows the optical density of the nanocrystal film in air and in the electrolyte solution. Note should be taken that the change in dielectric environment had no effect on the location of the plasmon frequency.
46
Figure 49: Optical density of 4.1nm, 16.8% Sn ITO nanocrystal films in air and in the electrolyte solution.
Figure 50 shows the spectroelectrochemical measurements of the film held at four different potentials. It is shown that the surface plasmon of the film was successfully shifted between the potential ranges. In addition, the absorption intensity also changed. When applying lower voltages versus the reference electrode, the plasmon frequency undergoes a blue shift to lower wavelengths. In this case, the plasmon will color the near infrared portion of light. When the potential is reversed to higher values versus the reference electrode, the plasmon frequency red shifts to higher wavelengths.
Figure 50: Optical density of 4.1nm, 16.8% Sn ITO nanocrystal films at different applied potentials.
47 Using the same Drude theory optical model described in the previous section, nice fits of the optical properties were ascertained. As before, absolute transmittance measurements where fitted to account for the cell geometry. Starting with a large number of very different initial guesses, a high level of confidence was obtained for the fitted plasma frequency since it converged to the same value in every case. This level of confidence was true for the all the geometries studied here; thin films, solutions, and electrochemical half cell devices. Figure 51 shows the nice fits to the system.
Figure 51: Absolute optical density of 4.1nm, 16.8% Sn ITO nanocrystal films at different applied potentials. All potentials are referenced to Li/Li+ electrode. Note fits are in yellow.
Figure 52 shows the carrier concentration and plasma frequency attained from the simulation. It was observed that there was nearly a factor of three change in carrier concentration.
Figure 52: Plasma frequency and carrier concentration at applied potential attained from optical model.
48 Cyclic voltammetry of the nanocrystal films showed capacitive charging. Figure 53 shows 10 cycles in a 0.1M lithium perchlorate in propylene carbonate electrolyte. Note should be taken that the high sheet resistance leads a slight deviation in the square shape typically seen for capacitive charging.
Figure 53: Cyclic voltammetry of ITO nanocrystals in a Li based electrolyte. Ten cycles are shown.
2.3.3 Indium Tin Oxide Nanocrystal Film Variations
In this section, we will describe several iterations that were tested to optimize the electrochromic performance. Systematic characterization of each variation was compared and figures of merit where used to choose the best conditions for optimal performance
Nanocrystal Size
The nanocrystal diameter was systematically varied to change the surface area of the films. Figure 54 shows the change in optical density for three nanocrystal films of varying diameter. To be systematic in the comparison, every nanocrystal size was modulate for the same potential range, had ~4.65% Sn content, and consisted of the same thickness. It was noticed that the change in optical density is dependent heavily on the surface area of the film. For smaller diameters, there was a larger change in optical density. This can be explained by the need for charge compensation. The amounts of electrons that can enter the film are primarily limited by the amount of charge compensation that can be achieved [Roest, 2004]. Since the electrochemical doping is capacitive in nature, the compensating charges can only build up on the surface of the nanocrystals. Ultimately, smaller nanocrystals lead to larger surface areas which increase the available charge compensation.
49
Figure 54: Change in optical density for nanocrystal films of the same thickness and doping level but varying nanocrystal diameter.
Dopant Level
The effect of dopant incorporation during electrochemical modulation was also investigated. Figure 55 shows the change in optical density for ITO nanocrystals of the same diameter and thickness but varying dopant incorporation. No systematic trend was found for the change in dopant incorporation. The nanocrystal size tested in this sample was ~4nm in size.
0.6
4.4% Sn 0.5 9.5% Sn 14% Sn
0.4
OD 0.3
∆
0.2
0.1
0 400 600 800 1000 1200 1400 1600 1800 2000 2200 Wavelength (nm) Figure 55: Change in optical density for nanocrystal films of the same thickness and nanocrystal diameter but varying dopant incorporation.
50 Electrolyte
To confirm that the intercalation is not present during electrochemical doping, the films where tested in an electrolyte solution with bulky ions. Figure 56 shows spectroelectrochemical measurements of a nanocrystal film modulate in a 0.1M lithium perchlorate in propylene carbonate electrolyte solution and in a 0.1M tetrabutylammonium perchlorate (TBAP) solution in acetonitrile solution. For the TBAP based electrolyte, a platinum wire and a Ag/Ag+ were used as the counter and reference electrode respectively. It was seen that identical performance was achieved with the bulkier ion. In addition, Figure 57 shows similar cyclic voltammograms with relatively close charge capacities (3.3 and 3.5mC respectively). Overall, it was validated that intercalation is not necessary to achieve electrochemical doping of ITO nanocrystal thin films.
Figure 56: Spectroelectrochemistry of an ITO nanocrystal film in a lithium based electrolyte and in a TBAP based electrolyte.
Figure 57: Cyclic volotamograms of an ITO nanocrystal film in a lithium based electrolyte and in a TBAP based electrolyte
51 Film Thickness
The optical properties of ITO nanocrystal films with varying thickness were investigated. For this test, 4nm nanocrystals with 16.9% tin were processed at varying thickness. Figure 58 shows the modulation of the nanocrystal films between specified potentials. It was noted that the thinnest film had good bleaching capabilities but did not color substantially due to the reduced surface area. The thickest film had great coloring capability but overstauration reduces its bleaching capability. It was determined that moderate thickness is necessary to achieve good modulation in both colored and bleached state.
Figure 58: Transmittance of ITO nanocrystal films in the bleached and colored state at varying thickness. The black outlined sample is ~460nm, the red outlined sample is ~310nm, and the blue outlined sample is ~150nm
2.3.6 Optimized Indium Tin Oxide Electrochromic Layer
In this section, we will describe the electrochromic performance for the optimized ITO nanocrystal layer. Figure 59 shows the solar modulation achieved for the optimized layer. In the colored state, the film achieved a Tsol ~0.72 and TNIR~0.48. In the bleached state, the film achieved a Tsol ~0.93 and TNIR~0.81. Ultimately, 21% solar modulation was achieved across the solar spectrum and 35% near infrared solar modulation was achieved.
52
Figure 59: Solar Transmittance of ITO nanocrystal films in the bleached and colored state for a 310nm thick film composed of ~4nm ITO nanocrystal with 16.8% tin.
Modulation of the luminous transmittance is shown in Figure 60. It was determined that in the bleached state Tlum~0.97 and in the colored state Tlum~0.89. Overall, only ~8% of the solar energy visible to the human was modulated. Note should be taken that it always remained close to 90%.
Figure 60: Luminous Solar Transmittance of ITO nanocrystal films in the bleached and colored state for a 310nm thick film composed of ~4nm ITO nanocrystal with 16.8% tin.
53 The efficiency of coloration as a function of wavelength is shown by Figure 61. The highest coloration efficiency lies within the near infrared region due to the large optical density change. It should also be noted that the value for the largest coloration efficiency is almost twice as high as the best performing electrochromic films in the market.
Figure 61: Coloration efficiency of ITO nanocrystal films for a 310nm thick film composed of ~4nm ITO nanocrystal with 16.8% tin.
Durabality testing of ITO nanocrystal films shows reduced charge capacity for nanocrystals of small diameter thickness. Figure 62 shows the charge capacity as a function of cycle number for an ITO nanocrystal film composed of 4nm and ~16.8% tin. Charge reduction is witnessed for the first 2500 cycles and becomes stable for the rest of the cycles.
54
0.0011
0.001
0.0009
0.0008
0.0007
0.0006 (mA*Hr) Capacity 0.0005
0.0004 Charge Discharge
0.0003 0 2500 5000 7500 10000 12500 15000 17500 20000 Cycle Number Figure 62: Capacity as a function of cycle number of ITO nanocrystal films for a 310nm thick film composed of ~4nm ITO nanocrystal with 16.8% tin.
Optical measurements before and after cycling show some reduction in coloring capability and also show a slight reduction in the visible region of light (Figure 63). This loss in reduction is estimated to come from reduction of some of the Sn within the film. Naturally, Sn4+ is used during synthetic incorporation. It is estimated that some of the 4+ 2+ 2+ Sn is changing to Sn during cycling. If converted to Sn , charge capacity will be reduced. 1
0.9
0.8
0.7
0.6
0.5 0.4
0.3 (Fraction) Transmittance 0.2 ITO Before Cycling 0.1 ITO After Cycling
0 400 600 800 1000 1200 1400 1600 1800 2000 2200 Wavelength (nm) Figure 63: Optical properties before and after cycling of an ITO nanocrystal films for a 310nm thick film composed of ~4nm ITO nanocrystal with 16.8% tin.
55 XPS measurements of a cycled sample and of a sample before cycling show the increased presence of Sn2+ in the cycled sample. Table 3 shows the integrated values for both sets of samples. This result confirms that an enhanced surface area can lead to reduction of the incorporated tin and ultimately a reduction in charge capacity.
Oxidation Peak FWHM Uncycled Cycled ‘ State Position Area Area Sn 2+ 486.3 2.3 12000 17000 Sn 4+ 487.3 3.7 26500 33500
Table 3: XPS measurements of cycled and uncyled ITO nanocrystal samples. Half time switching speeds for the ITO nanocrystal film were recorded for a variety of electrolyte solutions and concentrations. Note should be taken that the nanocrystal films were deposited on both transparent conducting oxide (TCO) substrates and glass. Table 4 highlights the change in switching between the colored and bleach state. It was seen that the switching time between colored and bleached states varied for ITO nanocrystals deposited on glass in every condition, with coloration times always being longer. For ITO nanocrystals deposited on TCO substrates, switching times between the colored and bleached state did not vary when used in a TBAP electrolyte. They also did not vary at high concentrations of Li electrolyte but varied slightly for low concentrations. The switching time in all cases was always faster on the TCO substrates versus the glass substrates because conduction is dominated across the vertical plane of the film. When deposited on glass, the electron transport must go through plane to reach the evaporated electrode at the top of the film, which increases the number of hoping barriers that must overcome.
Coloration (s) Bleaching (s)
Glass 26 2 0.1M TCO Li 1.53 3.40
Glass 12.4 1.52
Electrolyte 1M TCO 0.28 0.30
Glass 3.35 1.11
0.1M TCO 0.10 0.11 Glass 2.76 1.70 TBAP 1M TCO 0.05 0.06 Electrolyte Table 4: Switching half life measurements of ITO nanocrystal films in a variety of conditions.
The overall performance of ITO nanocrystal thin films is encouraging. Their optical performance during modulation is exceptional. Yet, the uncertainty in durability leaves room for investigation of alternative material systems. Chapter 3 will discuss the use of aluminum doped zinc oxide as a potential material system for either a working or counter electrode.
56 CHAPTER 3: ALUMINUM ZINC OXIDE NANOCRYSTAL ELECTRODES
3.1 ALUMINUM ZINC OXIDE MATERIAL SYSTEM
As discussed in the previous chapter, the ideal material system for a working electrode must be visibly transparent, conductive, and have a surface plasmon in the near infrared region of light. Aluminum doped zinc oxide (AZO) is a promising TCO material that has been investigated in the literature since the 1980s [Minami, 1984]. Composed of inexpensive abundant materials, AZO is a perfect alternative for ITO based material systems. Initial investigations show reasonable conductivity and optical transparency. Yet, a lot of work is still being conducted to enhance its transport properties as well as its stability [Mendelsberg, 2011]. In this section, the material properties of AZO will be discussed in detail.
3.1.1 Introduction to Aluminum Zinc Oxide
Aluminum zinc oxide is another degenerately doped semiconductor with high carrier concentration (~1020) and good optical transparency (>85%). Depending on the doping level, it has a wide band gap of 3.46-3.54 eV [Kim, 1997]. The undoped structure of ZnO is of a hexagonal Wurtzite structure [Cebulla, 1998]. A single crystal structure is composed of 32 atoms and has lattice parameters of ao=3.249 and co=5.207 anbstroms [Maldonado, 2010]. When degenerately doped with aluminum, the structure remains the same. Figure 64 shows a typical hexagonal Wurtzite structure for aluminum doped zinc oxide. Substitution doping of Al3+ with zinc takes place at the center of the crystal structure. Similar to ITO, oxygen vacancies exist in the structure (not shown in the figure). AZO material systems are very sensitive to annealing in oxygen filled environments. They are also very sensitive to moisture and at times loose conductivity when introduced to moisture environments for extended periods of time. Depending on the growth technique, one can get good vertical growth (c-axis orientation) versus the substrate [Kim, 1997].
Figure 64: Hexagonal Wurtzite structure of aluminum doped zinc oxide [Maldonado, 2010].
57 During doping, the extra electron donated by the aluminum atom is transferred from the local energy in the band gap to the conduction band and absorbed by the 4s orbital of the zinc atom [Maldonado, 2010]. This extra electron increases the carrier concentration of the material system and creates an n-type degenerate semiconductor. The amount of aluminum incorporation is limited to a few percent (1-4 Al%) before localized impurity scattering begins to reduce the conductivity of the film. Figure 65 shows a summary of typical resistivities for doped tin oxide, zinc oxide, and indium oxide thin films up to 2005 [Minami, 2005]. For AZO, resistivity values of 0.8x10-4 Ωcm, 5x10-4 Ωcm, and 4.8x10-4 Ωcm for pulsed laser deposition, physical vapor deposition, and dc filtered cathodic arc deposition techniques respectively have been reported in the literature [Mendelsberg, 2011]. The last technique demonstrated an electron mobility of 60 cm2V- 1mV-1 for a thin film grown at increased deposition rate.
Figure 65: Resistivity values of doped tin oxide, indium oxide and zinc oxide thin films [Minami, 2005].
Similar to ITO, AZO thin films have desired optical properties for near infrared selective electrochromic devices. With a wide band gap, optical absorption takes place in the UV- region of light. Depending on the doping level, a Burnstein-moss shift will change the location of the band gap absorption. It was computed that ~0.06eV shift in the band gap is observed when a zinc oxide crystal is doped with one aluminum atom [Maldonado, 2010]. Visible transparency of >85% is witnessed for thin films. As the thickness increases, the color of the film becomes slightly yellow white. In the near infrared region of light, surface plasmon casues a slight amount of absorption is witnessed due to the surface plasmon effect. Since the carrier concentration of AZO is less than ITO thin films, the surface plasmon is located at lower energies. The location of the absorption feature can be seen as a disadvantage when trying to repeat the performance levels of ITO, but
58 can be seen as an advantage when considering a potential counter electrode material. In the next section, we will describe in detail the synthetic development of nanocrystal based aluminum doped zinc oxide.
3.1.2 Synthetic Development of Aluminum Zinc Oxide
In the literature, there have been several examples of synthetic recipes for aluminum doped zinc oxide nanocrystals [Kadam 2008, Lu 2011, Chen 2008, & Thu 2010] and nanowires [Kusinski, 2009]. Out of these protocols, a lack of doping control and mono- disparity is still observed. For this project, a new synthetic recipe was developed [Buonsanti, 2011].
Materials and Synthetic Method
A solution (A) containing ZnSt2 (Alfa Aesar , 1 mmol), Al(acac)3 (Aldrich 99%, 0.05-1 mmol), OLAC (Aldrich 90%, 3 mmol) in 4 mL of ODE (Aldrich 90%) and a mixture (B) of 1,2-HDDIOL (Aldrich 90%, 10 mmol) in 11 mL ODE were loaded in three-neck flasks and magnetically stirred at 140°C under argon for 1 h. Afterward, the temperature in B was increased to an injection temperature Tinj and solution A was rapidly injected into B, which was accompanied by a temperature drop ΔT≈20°C (Tgrowth=Tinj-ΔT). After 5 hours at T growth, the reaction mixture was allowed to cool. Ethanol was added (a white flocculate from the clear yellow-orange solution was generally only observed for the largest NCs) and the NCs were separated from the reaction mixture by centrifugation (9000 rpm for 20min). After two cycles of redispersion in hexane (1 mL) and reprecipitation by ethanol, 20-30 mg of precipitate was eventually collected and dispersed in a suitable nonpolar solvent.
Aluminum incorporation can be modified by changing the molar concentration of the aluminum precursor during the synthesis. Note should be taken that the surface plasmon frequency blue shifts to higher energies when aluminum incorporation is greater. For this project, two sizes were investigated. Figure 66 shows the size distribution for the nanocrystal solution used during spectroelectrochemical testing. An average nanocrystal size of 9.0±1.5nm was attained.
18 9.0±1.5nm 16 14
12
10
8 Frequency (%) Frequency 6 4 2 0 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 Diameter (nm) Figure 66: Aluminum zinc oxide nanocrystals grown used for spectroelectochemical testing. Average size is 9.0±1.8nm.
59 Figure 67 shows the nanocrystal set used for durability and transport measurements. An average nanocrystal size of 12.9±2.0nm was achieved.
30 12.9±2.0nm
25
20
15 (%) Frequency 10
5
0 012345678910111213141516171819202122232425 Diameter (nm)
Figure 67: Aluminum zinc oxide nanocrystals grown used for durability and transport testing. Average size is 12.9±2.0nm.
Elemental analysis was performed by induced coupled plasma atomic emission spectroscopy (ICP-AES) with a Varian 720/730 Series spectrometer. The AZO samples were digested in concentrated HCl. The relative error on the extracted Al content was within 3% of the reported percentage, as evaluated on the basis of 9 replicates per each measurement. Table 5 shows the results for the synthetic sets labeled above.
Al Zn AZO Sample Molar % Molar %
Post Processing 3.91 96.09 Spectroelectrochemistry 3.68 96.32
Durability & Transport* 3.58 96.42 Table 5: ICP-AES results of all AZO batches used in this project.
60 3.2 DEVICE FABRICATION
As described in the previous chapter, device fabrication requires several steps. Similar techniques were implemented on the deposition and post processing of the AZO nanocrystal inks. This section describes the deposition technique and post processing techniques investigated for this project.
3.2.1 Spincasting Deposition Technique
A spincasting deposition method was chosen for this project. Using the idealized spincasting recipe discussed in the previous chapter, an 85mg/ml AZO solution of a one to one octane/hexane was deposited on a variety of substrates. During deposition, a specified volume of solution (15ug for 6.25cm2 area and 8ug for a 1cm2) was dispensed on top of the substrate. The spinning recipe consists of a three second ramp to 1000RPM, a two and a half minute hold at 1000RPM, and a 30 second 4000RPM spinoff. After spinning, the samples are placed on 100°C hotplate for 5 minutes. Figure 68 shows the outcome of films deposited on a silicon and glass substrate.
Figure 68: Images of AZO spincated samples on silicon and glass.
Figure 69 shows scanning electron images of spincasted films. The cross section images show the uniformity of the film and top down images display a network of nanocrystals.
Figure 69: Images of scanning electron images of AZO spincated samples on silicon.
61 3.2.2 Post Processing Technique
Similar to ITO thin films, aluminum doped zinc oxide films also require post processing to create a transparent, conductive film. As mentioned in the previous chapter, TCO based materials are composed of oxygen vacancies that are inadvertently filled when annealed in an oxygen environment. To decompose the surface ligands on the nanocrystal surface, thermal annealing must be performed in an inert environment. Prior to attempting any surface treatments, AZO thin films were annealed at 350°C for 30 minutes. Figure 70 shows a dark brown residue on the annealed film. The carbon residue comes from surface ligand decomposition.
Figure 70: AZO thin film decomposed in argon at 350°C.
Formic Acid Ligand Exchange
To eliminate the carbon residue from the thin film, surface treatments discussed in the previous chapter were also attempted with AZO nanocrystal thin films. The first ligand exchange was attempted with a solution of formic acid. Figure 71 shows the ligand exchange process used for formic acid. The spincasted films are immersed in a 0.1M anhydrous formic acid in acetonitrile solution. After being soaked for 40 minutes, the films were removed from the solution and rinsed three times with a one to one acetonitrile/chloroform solution. The films were then placed on a 100°C hot plate for 5 min to dry. After they were dried, the films were thermally annealed at 300°C for 30 minutes in an argon environment. The final outcome was a transparent conductive film.
62 Oleic Acid Ligand 0.1M Formic Acid in Acetonitrile for 40min
Formic Acid Ligand Processed Thermally Anneal in AZO Film Argon @300C for 1hr Conductive Film
Figure 71: Formic acid ligand exchange process of aluminum doped zinc oxide films.
To validate that the ligand exchange was successful, FTIR was conducted at each of the processing steps. Figure 72 shows the results of the FTIR. For the as deposited trace, two absorption peaks associated to the C-H bond are located near 2800 cm-1. After the formic acid ligand exchange, the absorption peaks were reduced. When annealed in argon, the absorption was eliminated which leads to a successful removal of the surface ligands.
110
105
100
95
90 85
80
Transmittance 75 70 Before 65 After Anneal 60 4000 3500 3000 2500 2000 1500 1000 500 Wavenumber Figure 72: FTIR of formic acid ligand exchange process for aluminum doped zinc oxide films.
63 XRD was also performed for each of the processing steps. There was no change in crystal structure or nanocrystal size in between each processing step. Figure 73 shows the results.
180
160 Before After 140 Anneal
120
100
80 Intensity (a.u.) Intensity 60
40
20
0 30 35 40 45 50 55 60 65 Two Theta (degrees)
Figure 73: XRD of formic acid ligand exchange process for aluminum doped zinc oxide films.
Meerwein Ligand Exchange
Formic acid ligand exchange is a powerful technique to remove surface ligands but is limiting when it comes to removing all the carbon from the film. To make the nanocrystal surface bare and carbon free, a Meerwein ligand exchange was investigated using both the methyl and the ethyl based salts. Figure 74 shows the ligand exchange process performed on thin films of AZO nanocrystals. Similar to the formic acid process, thin films of AZO are immersed in a 20mM solution of Meerwien saltin anhydrous acetonitrile for one hour. This process is performed in nitrogen filled glove box to elimate any moisture contamination. Once finished the films are removed and rinsed four times with a one to one acetonitrile/chloroform solution. The rinsed films are placed on a 110°C hot plate to dry. Dried films are then annealed in an argon environment at 300°C for 30 min. The outcome is a transparent, conductive thin film.
64 Oleic Acid Ligand 20mM Meerwein Salt In Acetonitrile for 1hr
Meerwein Ligand Processed Thermally Anneal in AZO Film Argon @300C for 1hr Conductive Film
Figure 74: Meerwien ligand exchange process for AZO nanocrystal thin films.
In this process, the methyl based salt was too aggressive and had a tendency to etch the nanocrystal particles and ultimate remove the entire film from the substrate. For this reason, tryethyloxonium tetrafluoroborate (TEO) was preferred for this ligand exchange process. Figure 75 shows FTIR for each of the processing steps. Similar to the formic acid, an absorption peak associated to C-H stretching is located at ~2800cm-1 for as deposited films. After the ligand exchange, this absorption peak is reduced substantially. After annealing in argon, the peaks are eliminated completely. This is a clear indication that the ligand exchange was successful.
1
0.9
0.8
0.7
0.6 0.5
0.4
0.3 Normalized Transmittance (Fraction) 0.2 As Deposited 20mM TEO 0.1 300C Ar Anneal 0 4000 3500 3000 2500 2000 1500 1000 500 -1 Wavenumber (cm ) Figure 75: FTIR of Meerwien ligand exchange process for AZO nanocrystal thin films.
65 XRD was performed at each of the processing steps to ensure that the morphology remained the same. It was seen that there was no change in nanocrystal size or structure. Figure 76 shows the normalized XRD patterns as a function of two-theta for each processing steps.
1
0.9 As Deposited 20mM TEO 0.8 300C Ar Anneal
0.7
0.6
0.5
0.4
0.3 Normalized Intensity (Fraction) Intensity Normalized 0.2
0.1
0 20 25 30 35 40 45 50 55 60 Two Theta (Degrees)
Figure 76: XRD of Meerwien ligand exchange process for AZO nanocrystal thin films.
Sheet resistance
The sheet resistance of formic acid treated films was investigated as a function of annealing temperature. Figure 77 shows the films after processing.
300 350
400 450 500
Figure 77: AZO samples with formic acid ligand exchange process at a variety of annealing temperatures.
66 Figure 78 shows the results after performing 4-probe conductivity measurements. It was seen that there is no trend in sheet resistance as a function of annealing temperature. The sheet resistance is so high that any deviations in annealing temperature do not effect the overall conduction of the film.
600
500
)
Ω 400
300
200 (k Sheet Resistance 100
0 300 350 400 450 500 Annealing Temperature (C) Figure 78: Sheet resistance of AZO samples with formic acid ligand exchange process at a variety of annealing temperatures.
Figure 79 shows top down SEM images of the AZO nanocrystals after post processing. It is clear that the nanocrystals do not form close packed networks which leads to really high film sheet resistance. Conduction by electron hopping is very sensitive to the way the nanocrystal films pack and annealing temperature will not have an affect on this packing [Vanmaekelbergh, 2005].
100 nm
Figure 79: SEM of AZO samples with TEO exchange process.
67 3.3 ELECTROCHROMIC PROPERTIES OF ALUMINUM ZINC OXIDE NANOCRYSTAL FILMS.
Thin films of aluminum doped zinc oxide nanocrystals where deposited on both glass and TCO coated substrates and post processed. Gold electrodes with chromium adhesion layers where thermally evaporated on thin films deposited on glass. Using the same electrochemical approach discussed in the previous chapter, electrochromic properties of AZO films where investigated. Figure 80 shows the electrochromic effect of an AZO nanocrystal thin film immersed in a 0.1M lithium perchlorate in propylene carbonate electrolyte. The nanocrystal films were driven at the same potential range as the ITO nanocrystals discussed in the previous chapter. The amount of NIR modulation is limited compared to ITO due to the starting location of the surface plasmon frequency. To reach the same level of modulation as ITO, the thickness of the film must be increased.
1
0.9
0.8
0.7
0.6
0.5 0.4 Transmittance 0.3
0.2 1.5V 0.1 4V
0 350 500 650 800 950 1100 1250 1400 1550 1700 1850 2000 2150 2300 2450 Wavelength (nm)
Figure 80: Spectroelectrochemistry of an AZO nanocrystal thin film. 9.0±1.4nm particle with 3.7% aluminum doping were used. The film thickness is ~280±20nm. Reported potentials are versus a lithium electrode.
3.3.1 Layer Variation
Consecutive layer depositions on formic acid processed films where made to test the electrochromic dependence on film thickness. Figure 81 shows the spectroelectrochemical properties of one layer deposition. Similar to the data reported in Figure 80, the near infrared modulation is minimal. Visible transparency remained high between both colored and bleached state.
68 1
0.9 0.8
0.7 0.6
0.5 0.4 Transmittance 0.3 0.2 1.5V 0.1 4V 0 350 500 650 800 950 1100 1250 1400 1550 1700 1850 2000 2150 2300 2450 Wavelength (nm) Figure 81: Spectroelectrochemistry of an AZO nanocrystal thin film. 9.0±1.4nm particle with 3.7% aluminum doping were used. The film thickness is ~280±20nm. Reported potentials are versus a lithium electrode.
Figure 82 shows the spectroelectrochemical properties a thin film with 4 layer depositions. By increasing the thickness, the near infrared modulation is enhanced without affecting the visible transmittance. This change in performance leads to an increase in charge injection.
1 0.9
0.8
0.7
0.6
0.5
0.4 Transmittance 0.3 1.5V 0.2 4V 0.1 0 350 500 650 800 950 1100 1250 1400 1550 1700 1850 2000 2150 2300 2450 Wavelength (nm) Figure 82: Spectroelectrochemistry of an AZO nanocrystal thin film. 9.0±1.4nm particle with 3.7% aluminum doping were used. The film thickness is ~1080±20nm. Reported potentials are versus a lithium electrode.
69 Figure 83 shows the spectroelectrochemical measurements of a 10 layer deposition film. Note that the near infrared modulation matched the performance achieved by the ITO nanocrystal films discussed in the previous chapter. This performance was accomplished by increasing the amount of charge that was injected in the film. Saturation in the colored state also increased the amount of near infrared that is absorbed.
1
0.9
0.8
0.7
0.6
0.5
0.4 Transmittance
0.3 1.5V 0.2 4V 0.1
0 350 500 650 800 950 1100 1250 1400 1550 1700 1850 2000 2150 2300 2450 Wavelength (nm) Figure 83: Spectroelectrochemistry of an AZO nanocrystal thin film. 9.0±1.4nm particle with 3.7% aluminum doping were used. The film thickness is ~2435±30nm. Reported potentials are versus a lithium electrode.
Unfortunately, the visible transmittance of the sample in Figure 83 was reduced substantially as the decomposition of formic acid still leaves a slight amount of carbon residue. To improve the visible transmittance, 10 layer deposition samples were created using the Meerwein ligand exchange process. Figure 84 shows a comparison of the color difference between formic acid treated films and Meerwein treated films. A thickness of ~2200nm, an excess of carbon residue increased the color of the formic acid treated samples, where as the Meerwein treated samples remain transparent.
TEO Formic Acid
Figure 84: Formic Acid and TEO treated AZO nanocrystal thin film of ~2200±40nm.
70 Figure 85 shows the spectroelectrochemical measurements of the TEO treated samples. The near infrared performance was again similar to the performance achieved by ITO nanocrystal films. Yet, the visible transmittance was once again reduced. Even though the color issue is resolved, scattering reduces visible transmittance. As seen in the SEM image in Figure 79, large pores are formed after the TEO ligand exchange due to an excess shrinking. These pores scatter the incoming light and create a slight haze on the film. Haze can be controlled during the deposition layer by reducing the thickness of the film in each step. The fabrication time increases but the optical properties improve. Note should be taken that electrolyte interference is present at wavelengths >2200nm.
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Transmittance (Fraction) Transmittance 0.2
0.1 0 350 600 850 1100 1350 1600 1850 2100 2350 Wavelenght (nm) Figure 85: Spectroelectrochemistry of an AZO nanocrystal thin film. 9.0±1.4nm particle with 3.7% aluminum doping were used. The film thickness is ~2921±200nm. Reported potentials are versus a lithium electrode.
3.3.2 Electrolyte variation
AZO nanocrystal thin films were tested in a lithium and TBAP electrolytes to investigate if the optical modulation can be achieved without intercalation. Figure 86 shows spectroelectrochemical properties of an AZO nanocrystal film modulated in both electrolytes. A 0.1M lithium perchlorate in propylene carbonate was used to test the modulation in the lithium electrolyte. A 0.1M TBAP in propylene carbonate electrolyte was used to test the TBAP electrolyte. The optical modulation is maintained regardless of the electrolyte chosen.
71
1 0.9
0.8 0.7
0.6 0.5
0.4
0.3 Transmittance (Fraction) Transmittance 0.2 Li 0.1 TBAP 0 400 600 800 1000 1200 1400 1600 1800 2000 2200 Wavelength (nm) Figure 86: Spectroelectrochemistry of an AZO nanocrystal thin film in a lithium and TBAP electrolyte. 12.9±2.0nm particle with 3.7% aluminum doping were used. The film thickness is ~1800±200nm.
Figure 87 shows that a capacitive effect is maintained during cyclic voltammetry in both electrolytes. Similar to the performance of ITO nanocrystals, lithium insertion is not necessary for attaining charge injection.
0.05
0.04
0.03
) 2 0.02
0.01
0
-0.01
-0.02
Current /Area (mA/cm /Area Current
-0.03 TBAP -0.04 Li
-0.05 -1.5 -1 -0.5 0 0.5 1 1.5 Volatege vs NHE (V) Figure 87: Cyclic voltamettry of an AZO nanocrystal thin film in a lithium and TBAP electrolyte. 12.9±2.0nm particle with 3.7% aluminum doping were used. The film thickness is ~1800±200nm.
72 3.3.3 Optimized Electrochromic Layer Performance
In this section, we will describe the electrochromic figures of merit for the optimized AZO nanocrystal film. Figure 88 shows the solar modulation achieved for the optimized layer. For the colored state, the film achieved a Tsol ~0.62 and TNIR~0.38. In the bleached state, the film achieved a Tsol ~0.82 and TNIR~0.77. Ultimately, 20% solar modulation was achieved across the solar spectrum and 39% near infrared solar modulation was achieved.
1.8
Sun 1.6 Bleached
) Colored -1 1.4
* nm 1.2 -2
1
0.8
0.6
0.4 Solar Irradiance (W m (W Irradiance Solar
0.2
0 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm)
Figure 88: Solar modulation of an AZO nanocrystal thin film. 9.0±1.4nm with 3.7% aluminum doping were used. The film thickness is ~2435±30nm.
Modulation of the luminous transmittance is shown in Figure 89. It was determined that in the bleached state Tlum~0.85 and in the colored state Tlum~0.82. Overall, only ~3% of the solar energy visible to the human was modulated. Note should be taken that the reduction of solar modulation is attributed to haze. Scattering of light by pores in the nanocrystal films create this loss in visible transmittance.
73
1.6 Solar energy*Eye Luminious 1.4 Colored 1.2 Bleached 1
0.8 0.6
0.4 Solar Irradiance Solar Irradiance (W m-2 * nm-1)
0.2
0 400 425 450 475 500 525 550 575 600 625 650 675 700 725 750 775 800 Wavelength (nm) Figure 89: Solar luminous modulation of an AZO nanocrystal thin film. 9.0±1.4nm particle with 3.7% aluminum doping were used. The film thickness is
The efficiency of coloration as a function of wavelength is shown by Figure 90. Note should be taken that the highest coloration efficiency lies at the edge the near infrared region due to the large optical density change. It should also be noted that the value for the largest coloration efficiency is almost twice of the best performing ITO nanocrystal electrochromic films in the market and four times traditional electrochromic materials.
500
450
400 /C) 2 350
300
250 200
150
Coloration Efficiency (cm Efficiency Coloration 100
50
0 400 600 800 1000 1200 1400 1600 1800 2000 2200 Wavelength (nm) Figure 90: Coloration efficiency of an AZO nanocrystal thin film. 9.0±1.4nm particle with 3.7% aluminum doping were used. The film thickness is ~2435±30nm.
74 3.3.3 Switching Kinetics
Half time switching speeds for the AZO nanocrystal film were recorded for a variety of electrolyte solutions and concentrations. The nanocrystal films were deposited on both transparent conducting oxide (TCO) substrates and glass. A Meerwein ligand exchange was used to make sure that the nanocrystals had a bare surface. Table 6 highlights the change in switching between the colored and bleach state. Samples that were deposited on glass were not conductive enough to take measurements. In the lithium electrolyte, there was no clear trend when comparing the switching times for a colored state and bleaching state. At lower concentrations, bleaching takes substantially longer. This longer time could be associated to an increased sheet resistance caused by the nanocrystal network. At higher concentrations, the bleaching and colored times are virtually identical and within their error. Similar switching times can be associated to enhanced charged compensation. Since there are enough counter ions in the electrolyte solution, the double layer capacitance is built up and removed at similar rates. Accordingly, switching times for higher concentration electrolytes are faster than electrolytes with lower concentrations. For both TBAP and Li based electrolytes. In the TBAP electrolyte, there is no difference in charging and discharging for both electrolytes. In addition, both electrolytes show that switching rates are increased when the concentration is increased.
Coloration (s) Bleaching (s) Li 0.1M 0.50 0.90
Electrolyte 1M 0.19 0.16 0.1M 0.24 0.23 1M 0.07 0.06
TBAP
Electrolyte
Table 6: Half life switching speeds for AZO nanocrystal thin films deposited on at TCO substrate in a lithium and TBAP electrolyte at two different concentrations.
3.3.4 Durability
Durability testing of AZO nanocrystal thin films was performed in a lithium based electrolyte. A ~2200nm, 10 layer deposited sample that was treated via Merwien ligand exchange process was immersed in a 0.1M lithium perchlorate in propylene carbonate and cycled 20000 times at a rate of 60 mV/s. Each cycle went from 4V to 1.5V versus a lithium electrode. The nanocrystals tested where ~12.9±2.0nm in diameter and had ~3.68% aluminum doping. Figure 91 shows the capacity for charge and discharge cycles as a function of cycle number. It was determined that AZO nanocrystal thin films are very stable during cycling. A very slight degradation was noticed throughout the cycling process.
75 0.0035
0.003
0.0025
0.002
0.0015 Capacity (mA*Hr) 0.001
0.0005 Charge Discharge 0 0 2500 5000 7500 10000 12500 15000 17500 20000 Cycle Number
Figure 91: Durability testing for AZO nanocrystal thin films deposited on at TCO substrate in a 0.1M LiClO in PC electrolyte. 4
Spectroelectrochemical measurements were performed before and after 20000 cycles. Figure 92 shows that there is roughly no change in optical modulation before and after cycling. Ultimately, these nanocrystal films are extremely stable and no material degradation is witnessed upon enhanced life time testing.
1
0.9 0.8
0.7
0.6
0.5
0.4 0.3 Transmittance (Fraction) Transmittance
0.2 Before Cycling 0.1 After 20,000 Cycles 0 400 600 800 1000 1200 1400 1600 1800 2000 2200 Wavelength (nm) Figure 92: Spectroelectrochemcial testing for AZO nanocrystal thin films as deposited and after 20000 cycles.
76 Overall, AZO nanocrystal thin films have the capability to achieve optical modulation in the near infrared comparable to ITO electrodes. Yet, device fabrication must be optimized to reduce the amount of haze that is formed during post processing. On the upside, the potential of achieving enhanced performance that is stable leaves room for a deeper investigation for its use as a working electrode. One of the added benefits of using AZO nanocrystal thin films lies with its capability of also being a passive counter electrode. As seen in this chapter, very thin films of AZO nanocrystals (~200nm) do not have a lot of optical modulation in the near infrared region. Yet, they do provide enough charge capacity for an ITO nanocrystal working electrode. Ultimately, AZO can be modified to act as a very thick active working electrode (~2200nm) or a thin passive counter electrode for ITO nanocrystal thin films. The next chapter will discuss the potential energy savings associated with installing these types of working electrode materials in a commercial building.
77 CHAPTER 4: ENERGY PERFORMANCE OF INDIUM TIN OXIDE AND ALUMINUM ZINC OXIDE PLASMONIC WINDOWS
In this chapter, we will discuss the energy performance associated with installing plasmonic electrochromic windows in a commercial building with four glass walls. The purpose is to quantify the potential amount of energy savings one can achieve and to determine which geographical locations are perfect candidates for these windows. The model that will be discussed only considers thermal savings in HVAC use and does not consider any savings in daylighting. Only the active working electrode is modeled in this program. This first order model creates a foundation from which one can incorporate a more detailed analysis.
4.1 INTRODUCTION TO ENERGY PERFORMANCE MODEL
Figure 93 shows a rectangular floor of a commercial high rise building. For this model, we will assume that each wall is made of a double pane window with an electrochromic coating installed at the inner surface of the outer pane facing the outdoors. Corner effects will be ignored for this model. The objective of setting up the model in this fashion is to represent a floor in a high rise building. A user can assign the size and volume of the floor. The only constraint is that it must be a rectangle.
Figure 93: Typical structure modeled in the energy performance of the plasmonic electrochromic windows.
The model uses four main tasks to calculate annual energy savings. The first task tracks the solar radiation that reaches each wall surface every hour of the day. The second task analyzes the heat transfer across the double pane window and determines the temperature at the inner wall. The third task determines the temperature distribution across the room and the fourth task determines the energy use required to bring the area back down to room temperature for a standard energy star HVAC system. Each of these four tasks will be covered in more detail in the next section. For this model, it is assumed that the sun
78 shines everyday of the year without any weather effects such as shading from clouds. During the fall and winter months (October-March), the electrochromic windows are left in the bleached state to let heat into the building. In the remaining months, the electrochromic windows are left in the colored state to block out the suns heat. Ultimately, this model simulates an ideal case and determines the maximum savings one can achieve with the current performance.
4.1.1 Solar Angles/Solar Tracking
To calculate the amount of incident incoming solar radiation that hits the surface of each window, tracking of solar angles was initiated. Figure 94 shows the solar angles on a surface throughout the day.
Figure 94: Solar angles for radiation on the surface of earth.
A number of parameters such as solar time of day, day in the year, geographical location (latitude and longtitude), and orientation with respect to the sun must be defined to determine the angle dependence on solar radiation. The intensity of the direct normal solar radiation is found from the following equation,
B sin 2 IAeDN Eq. 25
where A is the solar irradiance from the sun (1310 W/m2), B is a constant (0.18), and χ is the solar zenith angle which is the angle measured in radians between the vertical at the local position and a line drawn from the sun to the local position. The solar zenith angle is found by employing the equation below,
79 cos1 sin sin cos cos cos radians, Eq. 26 where λ is the latitude angle in radians, δ is the declination angle in radians, and α is the hour angle measured in radians. The latitude angle is a specified value depending on where the building is located on the globe. The declination angle is dependent on the day of year n and found by employing the following equation.
n 80 23.44sin 360 radians, Eq. 27 365.25 180
The day of the year is measured from n = 1 on January 1st. The hour angle is based on the solar time t, and can be found using the following equation.
15t 12 radians, Eq. 28 180
From Eq. 25 through 28, the incident direct solar flux ID can be found,
IIDDNcos cos sin sin cos Eq. 29
where ε is the tilt of the collector in radians (ε = 90 corresponds to the window perpendicular to a flat ground), ζ is the surface azimuth angle measured from the north clockwise, and ξ is the solar azimuth angle which can be computed with the following equation and table [Da Rosa, 2005].
sin tan Eq. 30 sin cos cos tan
Table 7: Table used in determining ξ
Sign of α Sign of tan(ξ) ξ
Positive Positive π + tan-1(tan(ξ))
Positive Negative 2π + tan-1(tan(ξ))
Negative Positive tan-1(tan(ξ))
Negative Negative π + tan-1(tan(ξ))
4.1.2 Radiation Heat Transfer Through Window
After the incident energy is determined at the window surface, a one dimensional heat transfer analysis is conducted across the window surface. Figure 95 shows a cross section
80 schematic of the double pain window used. The temperature outside the window surface, Tout, alters every month and is based on a monthly average recorded by weather.com. The thickness of the glass and the gap spacing can vary. For this model, a 4mm thickness was set for the outer glass pane and a 3mm thickness was set for the inner pane. The gap length is set to a standard 1 mm distance. The insulating gas can vary. For this project, air was used. In order to determine the temperature at the inner surface of the wall, a one dimensional resistive heat transfer model was used [Incoprera, 2006].
Glass Glass
Insulating
Tout gas Tin
L L2 L 1 3 Figure 95: Cross section of double pain window used in the model
Figure 96 shows the resistance circuit. The heat transfer properties where specified for each of the materials used. It was assumed that convection was seen at the outer and inner surface of the window. Conduction was dominated through the glass and within the air gap. The energy of the outer surface of the window is associated with the radial energy determined in the solar tracking task.
1/Hair 1/Hair L1/Kg L2/Kair L3/Kg T Tout in
Figure 96: Thermal resistance model used for heat transfer analysis through the double pain window.
A Newon-Rhapson iterative scheme was used to solve for the temperature at the inner wall. An initial guess is imported to the program and iteration is performed until the energy balance is satisfied. The temperature is recorded every hour of the day that the sun is present.
81 4.1.3 Temperature Distribution of the Room
After determining the temperature at the inner wall, a finite difference method is implemented to determine the temperature across the room. A multi dimensional steady state heat conduction method was used [Ozisik, 1993]. Figure 97 shows a standard nodal picture for the two dimensional area.
North
West East
South
Figure 97: Nodal distribution of the finite difference method used to determine room temperature distribution.
Depending on the location of the room, different balance equations were used. Internal nodes where described by the following relation,
jijijiji TTTTT ,1,1,,1,1 ji 04 Eq. 31 where T is the temperature of the node location. Boundary nodes are established and set as the inner temperature that was determined by the previous task. The spacing of the nodes is set to be equal in both the x and y direction. The program solves for the unknowns using vector algebra. The user has the capability to choose the number of nodes to solve. A more accurate distribution is made for larger nodal distribution.
4.1.4 Temperature Distribution, HVAC use, and Energy Savings
Once the room temperature distribution is established, an average is taken. The average room temperature is used to calculate the amount of HVAC needed to bring the temperature down to 21°C. A Carnot cycle is used to determine the energy required to reach this temperature distribution [Sonntag, 2006]. The relationship for air conditioning is discussed below,
TVC E P Eq. 32 air *COPt where Cp is the specific heat of air, ρ is the density of air, ΔT is the temperature difference, V is the room volume, t is the time of operation, and COP is coefficient of performance.
82 The coefficient of performance is estimated to be energy star rated. In the case where heating is used, the following relationship was used,
TVC E P Eq. 33 heat COPt )1(*
where Cp is the specific heat of air, ρ is the density of air, ΔT is the temperature difference, V is the room volume, t is the time of operation, and COP is coefficient of performance. Similar to the air conditioning case, the coefficient of performance is estimated to be energy star rated for heating. The energy for heating and cooling is then averaged for the day and for the month. Monthly energy use is recorded and the overall cost of energy is determined by incorporating the local rates for electricity.
4.2 ENERGY PERFORMANCE WITH NO COATING
The energy performance of a structure with no coatings on its windows was modeled with the program described above. Two case samples were tested. One sample focuses on a warm climate and is located in Phoenix, AZ. The second case is focused on a northern cold climate and is located in Boston, MA. The results for each case study will be described below.
4.2.1 Warm Climate Region
Figure 98 shows the room temperature distribution at noon for the summer solstice. Note that the temperature distribution is warmest in the south facing wall. The average room temperature is in the low 30s with about 35°C near the surface of the south wall. The north and west facing walls are the coolest and are at about 21°C
South
East West
North
Figure 98: Temperature distribution at noon for the summer solstice in Phoenix, Az. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
83 Figure 99 shows the average hourly temperature distribution for the summer solstice. Note the temperature is highest at around 5PM.
Figure 99: Average hourly temperature distribution for the summer solstice in Phoenix, Az. Note that the temperature is in °C.
Figure 100 shows the room temperature distribution at noon for the winter solstice. Note that the temperature distribution is warmest in the south facing wall. The average room temperature is in the low 30s with about 35°C near the surface of the south wall. The north and west facing walls are the coolest and are at about 21°C
South
East West
North Figure 100: Temperature distribution at noon for the winter solstice in Phoenix, Az. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
84 Figure 101 shows the average hourly temperature distribution for the winter solstice. Note the temperature is highest at around 3PM.
Figure 101: Average hourly temperature distribution for the winter solstice in Phoenix, Az. Note that the temperature is in °C.
The monthly average temperature distribution is shown in Figure 102. Note that the highest temperature is seen in the month of March.
Figure 102: Average monthly temperature distribution for the year in Phoenix, Az. Note that the temperature is in °C.
85 The monthly energy consumption is shown in Figure 103. Note should be taken that the month of March has the highest energy expense. The overall energy consumed in one year is ~645.2 kW/year.
Annual Energy Use ~645.2 kW/yr
Figure 103: Average monthly energy use for the year in Phoenix, Az. Note that the energy is in kW.
The monthly energy expense is shown in Figure 104. Note should be taken that March is the highest energy bill. The overall expense for the year is $88.7.
Annual Energy Expense ~$88.7/yr
Figure 104: Average monthly energy expense for the year in Phoenix, Az. Note that the energy expense is in dollars.
86 4.2.2 Cold Climate Region
Figure 105 shows the room temperature distribution at noon for the summer solstice in Boston, MA. Note that the temperature distribution is warmest in the south facing wall. The average room temperature is in the low 30s with about 50°C near the surface of the south wall. The north and west facing walls are the coolest and are at about 21°C
South
East West
North Figure 105: Temperature distribution at noon for the summer solstice in Boston, MA. Note that the temperature is in °C and the distance in the X-Y axis is in meters. Figure 106 shows the average hourly temperature distribution for the summer solstice. Note the temperature is highest at around 5PM.
Figure 106: Average hourly temperature distribution for the summer solstice in Boston, MA. Note that the temperature is in °C.
87 Figure 107 shows the room temperature distribution at noon for the winter solstice. Note that the temperature distribution is warmest in the south facing wall. The average room temperature is in the low 20s with about 40°C near the surface of the south wall. The north and west facing walls are the coolest and are at about 21°C
South
West East
North Figure 107: Temperature distribution at noon for the winter solstice in Boston, MA. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
Figure 108 shows the average hourly temperature distribution for the winter solstice. Note the temperature is highest at around 3PM.
Figure 108: Average hourly temperature distribution for the summer solstice in Boston, MA. Note that the temperature is in °C.
88 The monthly average temperature distribution is shown in Figure 109. Note that the highest temperature is seen in the month of March.
Figure 109: Average monthly temperature distribution for the year in Boston, MA. Note that the temperature is in °C.
The monthly energy consumption is shown in Figure 110. Note should be taken that the month of March has the highest energy expense. The overall energy consumed in one year is ~1251 kW/year.
Annual Energy Use ~1251 kW/yr
Figure 110: Average monthly energy consumption for the year in Boston, MA. Note that the energy is in kW.
89 The monthly energy expense is shown in Figure 111. Note should be taken that March is the highest energy bill. The overall expense for the year is $88.7.
Annual Energy Expense ~$146/yr
Figure 111: Average monthly energy expense for the year in Boston, MA. Note that the energy expense is in dollars.
4.3 INDIUM TIN OXIDE FILM ENERGY PERFORMANCE
The energy performance of a structure with indium tin oxide plasmonic electrochromic windows was modeled with the program described above. Two case samples were tested. One sample focuses on a warm climate and is located in Phoenix, AZ. The second case is focused on a northern cold climate and is located in Boston, MA. The results for each case study will be described below.
4.3.1 Warm Climate Region
Figure 112 shows the room temperature distribution at noon for the summer solstice with an installed ITO plasmonic electrochromic coating. Note that the temperature distribution is warmest in the south facing wall. The average room temperature is in the low 30s with about 35°C near the surface of the south wall. The north and west facing walls are the coolest and are at about 21°C.
90 South
East West
North
Figure 112: Temperature distribution at noon for the summer solstice in Phoenix, Az. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
Figure 113 shows the average hourly temperature distribution for the summer solstice. Note the temperature is highest at around 4PM.
Figure 113: Average hourly temperature distribution for the summer solstice in Phoenix, Az. Note that the temperature is in °C.
91 Figure 114 shows the temperature distribution at noon for the winter solstice with an ITO plasmonic coating installed. The southern window gets extremely warm while the remainder of the walls are cool. The inner room temperature is in the low 30s. This is ideal for a winter climate.
South
East West
North Figure 114: Temperature distribution at noon for the winter solstice in Phoenix, Az. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
Figure 115 shows the average hourly temperature distribution for the winter solstice. Note the temperature is highest at around 2PM.
Figure 115: Average hourly temperature distribution for the winter solstice in Phoenix, Az. Note that the temperature is in °C.
92 The monthly average temperature distribution is shown in Figure 116. Note that the highest temperature is seen in the month of March with August close by.
Figure 116: Average monthly temperature distribution for the year in Phoenix, Az. Note that the temperature is in °C.
The monthly energy consumption is shown in Figure 117. The month of March the highest energy expense. The overall energy consumed in one year is ~582.4 kW/year.
Annual Energy Use ~582.4 kW/yr
Figure 117: Average monthly energy use for the year in Phoenix, Az. Note that the energy is in kW.
93 The monthly energy expense is shown in Figure 118. Note should be taken that March is the highest energy bill. The overall expense for the year is $80.
Annual Energy Expense ~$80/yr
Figure 118: Average monthly energy expense for the year in Phoenix, Az. Note that the energy expense is in dollars.
4.3.1 Cold Climate Region
Figure 119 shows the room temperature distribution at noon for the summer solstice with an installed ITO plasmonic electrochromic coating. Note that the temperature distribution is warmest in the south facing wall. The average room temperature is in the high 20s with about 50°C near the surface of the south wall. The north and east facing walls are the coolest and are at about 21°C. South
East West
North Figure 119: Temperature distribution at noon for the summer solstice in Boston, MA. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
94 Figure 120 shows the average hourly temperature distribution for the summer solstice. Note the temperature is highest at around 4PM.
Figure 120: Monthly room temperature distribution in Boston, MA. Note that the temperature is in °C.
Figure 121 shows the temperature distribution at noon for the winter solstice with an ITO plasmonic coating installed. The southern window gets extremely warm while the remainder of the walls are cool. The inner room temperature is in the low 30s. This is ideal for a winter climate.
South
East West
North
Figure 121: Temperature distribution at noon for the winter solstice in Boston, MA. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
95 Figure 122 shows the average hourly temperature distribution for the winter solstice. Note the temperature is highest at around 8AM.
Figure 122: Average hourly temperature distribution for the winter solstice in Boston, MA. Note that the temperature is in °C.
The monthly average temperature distribution is shown in Figure 123. Note that the highest temperature is seen in the month of March through August.
Figure 123: Average hourly temperature distribution for the winter solstice in Boston, MA. Note that the temperature is in °C.
96 The monthly energy consumption is shown in Figure 124. The month of March the highest energy expense. The overall energy consumed in one year is ~1146 kW/year.
Annual Energy Use ~1146 kW/yr
Figure 124: Average monthly energy use for the year in Phoenix, Az. Note that the energy is in kW.
The monthly energy expense is shown in Figure 125. March is the highest energy bill. The overall expense for the year is $133.7.
Annual Energy Expense ~$133.7/yr
Figure 125: Average monthly energy expense for the year in Phoenix, Az. Note that the energy expense is in dollars.
97 4.4 ALUMINUM ZINC OXIDE FILM ENERGY PERFORMANCE
The energy performance of a structure with aluminum zinc oxide plasmonic electrochromic windows was modeled with the program described above. Two case samples were tested. One sample focuses on a warm climate and is located in Phoenix, AZ. The second case is focused on a northern cold climate and is located in Boston, MA. The results for each case study will be described below.
4.4.1 Warm Climate Region
Figure 126 shows the room temperature distribution at noon for the summer solstice with an installed AZO plasmonic electrochromic coating. Note that the temperature distribution is warmest in the south facing wall. The average room temperature is in the high 20s with about 33°C near the surface of the south wall. The north and west facing walls are the coolest and are at about 21°C.
South
East West
North Figure 126: Temperature distribution at noon for the summer solstice in Phoenix, Az. The temperature is in °C and the distance in the X-Y axis is in meters.
98 Figure 127 shows the average hourly temperature distribution for the summer solstice. Note the temperature is highest at around 4PM.
Figure 127: Average hourly temperature distribution for the summer solstice in Phoenix, Az. The temperature is in °C.
Figure 128 shows the room temperature distribution at noon for the winter solstice with an installed AZO plasmonic electrochromic coating. The temperature distribution is warmest in the south facing wall. The average room temperature is in the mid 20s with about 45°C near the surface of the south wall. The north, east, and west facing walls are the coolest and are at about 21°C.
South
East West
North Figure 128: Temperature distribution at noon for the winter solstice in Phoenix, Az. The temperature is in °C and the distance in the X-Y axis is in meters.
99 Figure 129 shows the average hourly temperature distribution for the winter solstice. The temperature is highest at around 2PM.
Figure 129: Average hourly temperature distribution for the winter solstice in Phoenix, Az. Note that the temperature is in °C.
The monthly average temperature distribution is shown in Figure 130. The highest temperature is seen in the month of March with August close by.
Figure 130: Average monthly temperature distribution for the year in Phoenix, Az. The temperature is in °C.
100 The monthly energy consumption is shown in Figure 131. Note should be taken that the month of March the highest energy expense. The overall energy consumed in one year is ~545 kW/year.
Annual Energy Use ~545 kW/yr
Figure 131: Average monthly energy use for the year in Phoenix, Az. Energy is in kW.
The monthly energy expense is shown in Figure 132. Note should be taken that March is the highest energy bill. The overall expense for the year is $74.9.
Annual Energy Expense ~$74.9/yr
Figure 132: Average monthly energy expense for the year in Phoenix, Az. Energy expense is in dollars.
101 4.4.2 Cold Climate Region
Figure 133 shows the room temperature distribution at noon for the summer solstice with an installed AZO plasmonic electrochromic coating. The temperature distribution is warmest in the south facing wall. The average room temperature is in the high 20s with about 33°C near the surface of the south wall. The north and west facing walls are the coolest and are at about 21°C.
South
East West
North Figure 133: Temperature distribution at noon for the summer solstice in Boston, MA. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
Figure 134 shows the average hourly temperature distribution for the summer solstice. Note the temperature is highest at around 4PM.
Figure 134: Average hourly temperature distribution for the summer solstice in Boston, MA. Note that the temperature is in °C.
102 Figure 135 shows the temperature distribution at noon for the winter solstice with an AZO plasmonic coating installed. The southern window gets extremely warm while the remainder of the walls are cool. The inner room temperature is in the low 30s. This is ideal for a winter climate.
South
East West
North Figure 135: Temperature distribution at noon for the winter solstice in Boston, MA. Note that the temperature is in °C and the distance in the X-Y axis is in meters.
Figure 136 shows the average hourly temperature distribution for the summer solstice. Note the temperature is highest at around 8AM.
Figure 136: Average hourly temperature distribution for the winter solstice in Boston, MA. Note that the temperature is in °C.
103 The monthly average temperature distribution is shown in Figure 137. The highest temperature is seen in the month of March through August close by.
Figure 137: Average monthly temperature distribution for the year in Boston, MA. Note that the temperature is in °C.
The monthly energy consumption is shown in Figure 138. Note should be taken that the month of March the highest energy expense. The overall energy consumed in one year is ~1135 kW/year.
Annual Energy Use ~1135 kW/yr
Figure 138: Average monthly energy use for the year in Boston, MA. Note that the energy is in kW.
104 The monthly energy expense is shown in Figure 139. March is the highest energy bill. The overall expense for the year is $132.4.
Annual Energy Expense ~$132.4/yr
Figure 139: Average monthly energy expense for the year in Boston, MA. Note that the energy expense is in dollars.
4.5 ENERGY PERFORMANCE SUMMARY
The thermal energy consumption of a building located in Phoenix, AZ and Boston, MA was simulated to quantify the impact windows have on its energy performance. Three window variations consisting of no coating, an ITO electrochromic film, and an AZO electrochromic film where simulated. It was observed that the absolute energy consumption in Boston was greater than that of Phoenix. In the case where no coating is present, the anual energy consumption is 645 kW for Phoenix and 1251 kW for Boston. When a dynamic coating is installed, the energy consumption is reduced. AZO based electrochromic windows achieve the best performance in Phoenix, AZ with 15% and 16% savings in annual energy use and expense. Similarly, a 9% and 10% saving in energy consumption and expense respectively is achieved for a building with AZO based electrochromic windows in Boston MA. ITO based electrochromic windows achieved slightly less energy savings (9% and 8% respectively) in Phoenix and Boston.
105 CHAPTER 5: CONCLUSIONS AND FUTURE WORK
5.1 CONCLUSIONS
In the United States and worldwide, there is a need to reduce building energy consumption. The buildings sector alone accounts for 40% of the United States’ yearly energy consumption and 8% of the world’s energy use. Lighting and thermal management each represent about 30% of the energy used within a typical building. Windows cover an estimated area of about 2,500 square km in the US and are a critical component of building energy efficiency as they strongly affect the amount of natural light and solar gain that enters a building. Electrochromic windows are a promising technology that can enhance thermal management in buildings. To date, electrochromic windows have not penetrated the window market due to its high cost and limited performance. This dissertation described a new approach that increases the energy performance of electrochromic windows while potentially reducing the cost of fabrication. Utilizing a plasmonic effect, the devices described in this dissertation dynamically optimize the amount of solar heat that enters a building without affecting solar lighting. This new approach will reduce the need for electrical daylighting as well as HVAC use. Throughout this project, two material systems were investigated as potential working electrodes.
The first material system is composed of tin doped indium oxide nanocrystal thin films. When immersed in electrochemical half cell, one can control the location of the surface plasmon via electrochemical doping. This phenomenon was also true for aluminum doped zinc oxide nanocrystals. When optimizing the performance, it was determined that the size of the nanocrystals affects the overall modulation range. By decreasing the size of the nanocrystal, one can increase the surface area and therefore increase the amount of charge that can be injected during modulation. Similarly, one can increase the available surface area by increasing the thickness of the film. Electrochemical doping is seen as capacitive for both systems and was validated by achieving identical modulation in a TBAP based electrolyte. When investigating the durability of the material systems during cycling, it was noticed that indium tin oxide nanocrystal thin films showed a dramatic loss of charge injection. This reduction affected the amount of optical modulation. XPS test showed that a portion of the tin in the film was reduced from a Sn4+to a Sn2+. This reduction is seen as the reason for change in optical modulation. On the other hand, aluminum doped zinc oxide films showed stable cycling for 20000 cycles. No change in optical modulation was observed. Both material systems demonstrated stable and fast switching times when deposited on TCO substrates. If deposited on glass, the switching is reduced dramatically due to poor cross plane conduction.
After investigating the optical properties of both material systems, a model was constructed to asses its energy performance in different geographical locations in the United States. Table 8 summarizes the performance for three idealized structures in Phoenix, AZ. Each structure either had no coating installed, an indium tin oxide nanocrystal plasmonic electrochromic window, or an aluminum zinc oxide nanocrystal plasmonic electrochromic window. AZO coated structures used the least energy and had
106 the lowest energy bill. About 15% savings in annual energy cost could be achieved when AZO nanocrystal films are installed.
Annual Energy Use (kW/yr) Annual Energy Expense ($/yr) No Coating 645 88 ITO Coating 582 80 AZO Coating 545 75 Table 8: Annual energy use and expense for structures located in Phoenix, AZ.
Table 9 summarizes the performance of the same three structures but located in Boston, MA. It was determined that AZO once again showed the best energy performance. Yet, the performance of the indium tin oxide films was pretty close. Overall, buildings in northern climates have unfavorable latitude locations and high utility expenses which lead to an increase in energy consumption and cost.
Annual Energy Use (kW/yr) Annual Energy Expense ($/yr) No Coating 1251 146 ITO Coating 1146 134 AZO Coating 1135 132 Table 9: Annual energy use and expense for structures located in Boston, MA.
This energy model is a good foundation to determine the energy performance of the working electrodes. A second generation model will be developed to incorporate lighting energy use. This model will paint a better picture of the overall energy savings that can be achieved with plasmonic electrochromic windows. Additionally, a simulation should be done on complete electrochromic assemblies that incorporate the effects of the counter electrode as well.
When quantifying the impact plasmonic electrochromic windows can have on global energy consumption, it is clear that the potential for energy reduction is high. The data assessed from the model demonstrated that a 9-15% reduction in thermal energy use can be achieved for an optimized AZO working electrode. This energy saving on a larger scale could account for yearly reduction of 1-1.8 quads in the United States. Furthermore, this would reduce 21-38.7 gigawatts of power produced by fossil fuels and nearly 59- 106.2 million metric tons of CO2 annually throught the life of the device (including manufacturing). By continuing efforts on material selection, one can improve the optical performance of the device and achieve greater reductions in building energy consumption. In addition, these figures account only for thermal energy savings and an added reduction in electrical lighting can be achieved by enhanced solar daylighting. Ultimately, it is clear that installing plasmonic based electrochromic windows cold create significant benefits in the dynamic window market.
107 Installion of these windows in a variety of geographical locations will impact the overall energy performance of the system. In particular, it was observed that geographical locations with warm climates achieve greater performance. Still, energy savings can be achieved for cold northern climates. If control systems were combined with plasmonic electrochromic windows to control the level of modulation needed throughout the day, one could optimize the performance for every geographical location. With that said, it is estimated that this device can be successfully implemented worldwide.
This dream can only be achieved if the overall price is competitive with technologies that are out in the market today. To date, static coatings represent the majority of the window market. They range in cost of $3-5 per square foot. However, these window coatings are static and not well suited for locations with varying climates. An electrochromic window overcomes these limitations by enhancing the window performance in all climates. Large-scale adoption of electrochromic windows using plasmonic based devices could lead to overall cost reduction. Unlike traditional electrochromic layers, nanocrystal coatings are fabricated using a solution process. It has been estimated that by substituting spray deposition techniques for sputtering, one can achieve a 96% reduction in electrical use during fabrication. Our analysis indicates that final product costs for static optical coatings can be reduced by at least 30% using spray deposition. Considering the high cost of sputtering multiple metal oxide layers, the cost savings using spray deposition to manufacture plasmonic electrochromic coatings are expected to be even greater, easily exceeding 50% of final product cost (~$5-10/ sq.ft. at scale).
Overall, advantages in both optical performance and cost make this device the ideal candidate for establishing a dynamic window market. A robust dynamic window market will not only help mitigate energy consumption within buildings but promote domestic job creation in the manufacturing, installation, and construction sectors. To achive this task, a complete lab scale prototype must be fabricated and characterized. The steps necessary to achieve this goal will be discussed in the next section.
5.2 Future Work
As described in the above section, a research plan to develop a complete solid state cell will be discussed in detail. To complete this goal, a lamination technique will be investigated. This will be achieved by conducting three task: 1) fabricating a liquid based EC window to test the compatibility and performance of a counter and working electrode 2) prepare a polymer electrolyte film based on a stable polymer, an ionic salt, and an additive, and 3) laminating NC electrodes with a stable polymer electrolyte to fabricate a NC EC window.
5.2.1 Charge Compensation
When investigating the aluminum doped zinc oxide nanocrystal electrodes, it was determined that it could be an ideal candidate for a passive electrode. Since its carrier concentration is lower than ITO based films, its surface plasmon is located in the mid infrared range. The near infrared radiation is minimal but the available charge injection is
108 comparable to that of ITO. To form a passive electrode, it must not affect the optical performance and provide enough charge compensation for the working electrode. An initial cell configuration of ITO working electrode and AZO counter electrode should be investigated. Other combinations such as AZO/AZO, ITO/ITO, and AZO/ITO can also be investigated by modifying the film thickness and nanocrysatl size.
5.2.2 Future Research Plan
Task 1: Liquid Cell Fabrication Fabrication of the liquid cell starts by choosing combinations of optimized working and counter electrodes that have been prepared via spincasting. Prior to film deposition, tiny holes of 2mm in diameter will be sandblasted into the TCO coated glass (~3cm x 3cm) used for the counter electrode. Squares of 50μm thick Surlyn sealant will be cut out to seal both the inner cell and the filling hole, as shown in Figure 140. The cut out sealant will be placed between the working and counter electrode and sandwiched on top of a 100°C hotplate for a minute with a large copper press until cured. Once cured, another seal will pressed around the sandblasted hole with a Teflon sheet. About 30μl of electrolyte solution (0.1M lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in propylene glycol phenyl ether (P2P)) will placed on top of the sandblasted hole. The entire cell will be placed under vacuum using a roughing pump and a custom made funnel. During vacuum, all the air will escape inside the cell. After vacuum is removed, the electrolyte solution will be filled into the cell via capillary forces. Once filled, the sandblasted hole will be covered with sealant and glass and heated with a soldering tip until the sealant is cured.
Hole
Glass Surlyn Sealant
Counter Electrode
Liquid Electrolyte
Working Electrode Figure 140: Fabrication of liquid cells with working and counter electrode.
Optical, electrical, and electrochromic properties will be systematically characterized for every device. Special attention will be made to understand the switching kinetics between the bleached and colored state as well as the lifetime durability of the system. The composition with the highest performance will be recorded and used as a benchmark for the polymer based prototypes.
109 Task 2: Polymer Electrolyte Film Development The objective of the second task is to demonstrate the feasibility of fabricating a polymer electrolyte film with high ionic conductivity and transparency. Each sample prepared will be tested using conducting glass substrates. A polymer, an ionic salt, and an additive will be chosen to create a polymer based electrolyte. Care must be taken when choosing each component as it will affect the mechanical, thermal, and electrical stability of the electrolyte film. In the case of an EC window, ionic conductivities must remain high (>10-7 S/cm) over a temperature range of -30°C to 90°C. In addition, the electrolyte layer must have >85% average solar transparency. During Task 2, we plan on exploring the effects film thickness, component weight percentage (polymer, salt, additive), lamination properties (time, temperature, and pressure) and cross link density have on both ionic conductivity and optical transparency of the electrolyte film.
Based on its performance in other EC windows, our initial efforts will use polyvinyl butyral (PVB) as the active polymer in the electrolyte layer [Kraft, 2006]. LiTFSI and P2P will be used as the ionic salt and additive respectively [Kraft, 2010]. Differential scanning calorimetry (DSC) will be performed on samples with variable amounts of salt and additive to determine the glass transition temperatures (Tg). Understanding how Tg changes at different weight % will accelerate our optimization of the electrolyte films. Samples with different polymer/salt/additive weight % ratios will be laminated between TCO coated glass as seen in Figure 141. To achieve good contact between the conducting glass and the electrolyte, the sandwiched stack will be placed in a nylon bag and vacumm sealed. The vacumm bag holding the sample will then be placed in an autocloave and heated in two steps: 1) ramp at specified, time, temperature and pressure 2) held specified time, temperature, and pressure. We will vary the time, temperature, and pressure between 10min to 60 min, 20°C to 250°, and 100psi to 500psi respectively to explore its affects on lamination. Once heated the sample will be cooled to room temperature, removed from the nylon bag, and sealed at the edges to complete lamination. This lamination process will form the basis for lamination of our complete solid state EC device in Task 3.
ITO Glass
Electrolyte
ITO Glass
Figure 141: Lamination of polymer electrolyte between ITO coated glass.
Task 3: Fabricate NC Window with Polymer Electrolyte The objective of Task 3 is to demonstrate the feasibility of fabricating solid state nanocrystal EC windows based on polymer electrolytes. As seen in Figure 142, we will laminate combinations of nanocrystal EC layers that reached our target criteria for Task 1
110 with the best polymer electrolyte films from Task 2. The time, pressure, and temperature during lamination will be varied to explore its effects on lamination. Thickness of the electrolyte film will also be varied to determine any effects on the electrical properties of the nanocrystal EC window. Systematic characterization will be performed on the laminated nanocomposite EC window to establish which aspects of the cell (electrodes, electrolyte, and their interfaces) limits the device performance. Special care will be taken on investigating the pore penetration of the electrolyte during lamination and how much of an affect this property has on its optical and electrical performance. In addition to pore penetration, close attention will be taken on any delamination problems assessed during lifetime testing. Electrochemical impedance spectroscopy (EIS) will be used to quantify ion kinetics within the electrolyte, at the electrolyte/nanocrystal interface, and within the nanocrystal EC layer. By understanding these parameters, we could establish a plan for which adhesion additives we would explore during future testing.
Figure 142: Lamination of polymer electrolyte between nanocrystal electrodes.
Upon successful fabrication of a lab scale prototype, progress on commercialization can begin. Establishing appropriate scaling will initiate a direct path to a consumer product. Optical models will set performance metrics that must be met to achieve optimal energy saving for a variety of geographical locations. Ultimately, succesfull development of this technology will not only impact market penetration in the dynamic window sector but will set a new benchmark for performance, cost, and energy savings.
111 REFERENCES
Aegerter, M.A. “Sol-gel niobium pentoxide: A promising material for electrochromic coatings, batteries, nanocrystalline solar cells and catalysis.” Solar Energy Materials and Solar Cells 68, 401-422 (2001). Akhavan, V.A., et al. “Spray-deposited CuInSe2 nanocrystal photovoltaics.” Energy and Environmental Science 3,1600-1606 (2010). Apte, J., D. Arasteh, Y.J. Huang.“Future Advanced Windows for Zero-Energy Homes.” ASHRE Transactions, 109:2, 871-882 (2003). Atwater, H. A. & Polman, A. “Plasmonics for improved photovoltaic devices.” Nature Mater. 2010, 9, 205-213. Ba, J., D. F. Rohlfing, A. Feldhoff, T. Brezesinski, I. Djerdj, M. Wark, M. Niederberger. “Nonaqueous synthesis of uniform indium tin oxide nanocrystals and their electrical conductivity in dependence of the tin oxide concentration.” Chem. Mater. 18, 2848- 2854 (2006). Ba. J., A. Feldhoff, D.F. Rohlfing, M. Wark, M. Antonietti, M. Niederberger. “Crystallization of indium tin oxide nanoparticles: From cooperative behavior to individuality.” Small 3, 310-317 (2007). Bamfield, P., M.G. Hutchings. Chromic Phenomena: Technical Applications of Colour Chemistry. RCS Publishing, 2010. Behl, W.K., J.A. Christopulos, M. Ramirez, S. Gilman. “Lithium Inorganic Electrolyte Cells Utilizing Solvent Reduction” J. Electrochem. Soc. 12, 1619-1623 (1973). Boltasseva, A. & Atwater, H.A. “Low-Loss Plasmonic Metamaterials.” Science, 331, 290 (2011). Buhler, G., D. Tholmann, C. Feldmann. “One pot synthesis of highly conductive indium tin oxide nanocrystals.” Adv. Mater. 19, 2224-2227 (2007). Buonsanti, R., A. Llordes, S. Aloni, B.A. Helms, D.J. Milliron. “Tunable infrared absorption and visible transmittance of colloidal aluminum-doped zinc oxide nanocrystals.” Nano Letters 11, 4706-4710 (2011). Burke, L.D., O.J. Murphy. “Electrochromic behavior of electrodeposited cobalt oxide films.” Journal of Electroanalytical Chemistry and Interfacial Electrochemistry 112, 379-382 (1980). Cavigliasso, G.E., M.J. Esplandiu, V.A. Macagno. “Influence of the forming electrolyte on the electrical properties of tantalum and niobium oxide films: an EIS comparitive study.” J. of App. Electrochem. 28, 1213-1219 (1998). Cebulla, R., R. Wendt, K. Ellmer. “Al-doped zinc oxide films deposited by simultaneous rf and dc excitation of a magnetron plasma: Relationships between plasma parameter and structural and electrical film properties.” J. Appl. Phys. 83, 1087- 1095 (1998). Chen, K.J., T.H. Fang, F.Y. Hung, L.W. Ji, S.J. Chang, S.J. Young, Y.J. Hsiao. “The crystallization and physical properties of Al-doped ZnO nanoparticles.” Applied Surface Science 254, 5791-5797 (2008). Chopra, K.L., S. Major, D.K. Pandya. “Transparent Conductors: A status review.” Thin Solid Films 102, 1-46 (1983).
112 Choi, S., K.M. Nam, B.K. Park, W.S. Seo, J.T. Park. “Preperation and optical properties of colloidal, monodisperse, and highly crystalline ITO nanoparticles.” Chem. Mater. 20, 2609-2611 (2008). Cinnselalach, R., G. Boschloo, S. N. Rao, D. Fitzmaurice. “Electrochromic windows based on viologen-modified TiO2 films” Solar Energy Materials and Solar Cells 55, 215-223 (1998). Cordoba de Torresi, S. I., A. Gorenstein “Electrochromic Behaviour of Manganese Dioxide Electrodes in Slightly Alkaline Solutions.” Electrochimica Acta 37, 2015- 2019 (1992). Da Rosa, A.V. Fundamentals of Renewable Energy Process. Elsevier Academic Press, 2005. Datremont-Smith, W.C. “Transition metal oxide electrochromic materials and displays: a review: Part 2: oxides with anodic coloration.” Displays 3, 67-80 (1982). Edwards, P.P., A. Porch, M.O. Jones, D.V. Morgan, R.M. Perks. “Basic materials physics of transparent oxides.” Dalton Trans. 19, 2995-3002 (2004). Ederth, J., Heszler, P., Hultaker, A., Niklasson, G. A. & Granqvist, C. G. “Indium tin oxide films made from nanoparticles: Models for the optical and electrical properties.” Thin Solid Films 445, 199-206 (2003). Elghanian, R., Storhoff, J. J., Mucic, R. C., Letsinger, R. L. & Mirkin, C. A. “Selective colorimetric detection of polynucleotides based on the distance-dependent optical properties of gold nanoparticles.” Science 1997, 277, 1078-1081. Falk, A.L., F.H.L. Koppens, C.L. Yu, K. Kang, N.de leon Snapp, A.V. Akimov, M. Jo, M.D. Lukin, H. Park. “Near-field electrical detection of optical plasmons and single-plsmon sources.” Nature Physics 5, 475-479 (2009). Pflughoefft, M. & Weller, H. “Spectroelectrochemical analysis of the electrochromism of antimony-doped nanoparticulate tin-dioxide electrodes.” J. Phys. Chem. B 106, 10530-10534 (2002). Gagliardi, M “Smart Glass: Technologies and Global Markets.” BCC Research (2009). Garnich, F., P.C. Yu, C.M. Lampert. “Hydrated manganese oxide as a counter-electrode material for an electrochromic optical switching device.” Solar Energy Materials 20, 265-275 (1990). Gerlach, E. “Carrier scattering and transport in semiconductors treated by the energy-loss method.” J. Phys. C. 19, 4585-4603 (1986). Ghosh, S.K., T. Pal. “Interparticle Coupling Effect on the Surface Plasmon Resonance of Gold Nanoparticles: From Theory to Applications.” Chem. Rev. 107, 4797-4862 (2007). Giannini,V.,A.I. Fernandez-Dominguez, S.C. Heck, S.A. Maier. “Plasmonic Nanoantennas: Fundamentals and their use in controlling the radiative properties of nanoemitters.” Chem. Rev. 111, 3888-3912 (2011). Gilstrap, R.A., C.J. Capozzi, C. G. Carson, R. A. Gerdhart, C.J. Summers. “Synthesis of a nonagglomerated indium tin oxide nanoparticle dispersion.” Adv. Mater. 20, 4163- 4166 (2008). Gilstrap, R.A. “A colloidal nanoparticle form of indium tin oxide system development and characterization.” Unpublished doctoral dissertation, Georgia Institute of Technology, Atlanta, Georgia (2009).
113 Gonzalez, G.B. et. al. “Defect structure of bulk and nano-indium-tin oxide.” J. of App. Phys. 96, 3912-3920 (2004). Gottesfeld, S., J.D.E. Mcintyre, G. Beni, J.L.Shay. “Electrochromism in anodic iridium oxide films.” Appl. Phys. Lett. 33, 208-210 (1978). Gottesfeld, S. “The anodic rhodium oxide film: A two-color electrochromic system.” J. Electrochem. Soc. 127, 272-277 (1980) Granqvist, C.G. “Electrochromic tungsten oxide films: Review of progress 1993-1998.” Solar Energy Materials & Solar Cells 60, 201-262 (2000). Granqvist, C.G., E.Avendano, A. Azens. “Electrochromic coatings and devices: survey of some recent advances.” Thin Solid Films 442, 201-211 (2003). Granqvist, C.G. “Oxide Electrochromics: An Introduction to Devices and Materials” Solar Energy Materials & Solar Cells 99, 1-13 (2012). Granqvist, C.G. Handbook of Inorganic Electrochromic Materials. Elsevier Science, 1995. Granqvist, C.G. “Transparent conductors as solar energy materials: A panoramic View”. Solar Energy Materials & Solar Cells 91, 1529-1598 (2007). Guergi, A., L.H. Dao, “Electrochromic molybdenum oxide thin films prepared by electrodeposition.” J. Electrochem. Soc. 136, 2435-2436 (1989). Gupta, L., A. Mansingh, P.K. Srivastava, “Band gap narrowing and the band structure of tin-doped indium oxide films.” Thin Solid Films 176, 33-44 (1989). Guyot-Sionnest, P. & Wang, C. “Fast voltammetric and electrochromic response of semiconductor nanocrystal thin films.” J. Phys. Chem. B 107, 7355-7359 (2003). Hagfeidt, A., N. Viachpoulos, M. Gratzel. “Fast Electrochromic Switching with Nanocrystalline Oxide Semiconductor Films” J. Electrochem. Soc. 141, L82-L84 (1994). Halas, N. J., Lal, S., Chang, W.-S., Link, S. & Nordlander, P. “Plasmon in strongly coupled metallic structures.” Chem. Rev. doi:10.1021/cr200061k. (2011). Hamberg, I. & Granqvist, C. G. “Evaporated Sn-doped In2O3 films - basic optical- properties and applications to energy-efficient windows.” J. Appl. Phys. 60, 123- 159 (1986). Hammarberg, E., A. Prodi-schwab, S. Feldmann. “Microwave-assisted synthesis of indium tin oxide nanocrystals in polyol media and transparent, conductive, layers therof.” Thin Solid Films 516, 7437-7442 (2008). Han, C., S. Han, J. Gwak, S.P. Khatkar. “Synthesis of indium tin oxide (ITO) and fluorine-doped oxide (FTO) nano-powder by sol-gel combustion hybrid method.” Materials Letters 61, 1701-1703 (2007). Homola, J., S.S.Yee, G. Gauglitz. “Surface plasmon resonance sensors: review.” Sensors and Actuators 54, 3-15 (1999). Incropera, F.P, D.P. DeWitt, T.L. Bergman, A.S. Lavine. Fundamentals of Heat and Mass Transfer. John, Wiley, & Sons, 6th ed. 2006. Inque, E., H. Kokado, A. Izawa, “Electrochromism of Titanium Oxide” Oyo Buturi 43, 54-55 (1974). Izutsu, K. Electrochemistry in Non Aqueous Solutions. Wiley-VCH, 2009. Jodicke, D. “Electrochromic elements using antioxidants to suppress self discharging.”U.S. Patent 2009/0225393 A1.
114 Jelle, B.P., A. Hynd, A. Gustavsen, D. Arasteh, H. Goudey, R.Hart. “Fenstration of today and tomorrow: A state of the art review and future research opportunities.” Solar Energy Materials & Solar Cells 96, 1-28 (2012). Judkoff., R. “Increasing Building Energy Efficiency Through Advances in Materials.” MRS Bulletin 33, 449-454 (2008). Kadam, P., C. Agashe, S. Mahamuni. “Al-doped ZnO nanocrystals.” J. of Appl. Phys. 104, 103501 (2008). Karmhag, R., G. Gustavsson, C.G. Granqvist, A. Azens.“Manufacturing of electrochromic devices.” U.S. Patent 2009/0316248 A1. Kanehara, M., H. Koike, T. Yoshinaga, T.Teranishi. “Indium Tin Oxide Nanoparticles with compositionally tunable surface plasmon resonance frequencies in the near-IR region.” J. Am. Chem. Soc. 131, 17736-17737 (2009). Kim, K.H., K.C. Park, D.Y. Ma. “Structural, electrical and optical properties of aluminum doped zinc oxide films prepared by radio frequency magnetron sputtering.” J. Appl. Phys. 81, 7764-7772 (1997). Kraft, A., M. Rottmann, K. Heckner. “Large-area electrochromic glazing with ion- conducting PVB interlayer and two complementary electrodeposited electrochromic layers” Solar Energy Materials & Solar Cells 90, 469-476 (2006). Kraft, A., M. Rottmann.“Properties and current status of laminated electrochromic glass of Geismat.” Solar Energy Materials & Solar Cells. 93:12, 2088-2092 (2009). Kobayashi, N., H. Chinone, A. Miyazaki. “Polymer electrolyte for Novel Electrochromic Display” Electrochimica Acta 48, 2323-2327 (2003). Kullman, L., A. Azens, G. Vaivars, C.G. Granqvist. “Electrochromic devices incorporating Cr oxide and Ni oxide films: A comparison.” Solar Energy 68, 517- 522 (2000). Kusinski, G.J., J.R. Jokisaari, R. Noriega, L. Goris, M. Donovan, A. Salleo. “Transmission electron microscopy of solution-processed, intrinsic and Al-doped ZnO nanowires for transparent electrode fabrication.” Journal of Microscopy 237 443-449 (2009). Lampert, C. “Electrochromic materials and device for energy efficient windows” Solar Energy Materials 11, 1-27 (1984). Lampert, C.M., T.R. Omstead, P.C. Yu. “Chemical and optical properties of electrochromic nickel oxide films.” Solar Energy Materials 14, 161-174 (1986). Larsson, E. M., Langhammer, C., Zoric, I. & Kasemo, B. “Nanoplasmonic probes of catalytic reactions.” Science 2009, 326, 1091-1094. Lee, E.S., S.E. Selkowitz, R.D. Clear, D.L. DiBartolomeo, J.H. Klems, L.L. Fernandes, G.J. Ward,V. Inkarojrit, M. Yazdanian. 2006. Advancement of Electrochromic Windows. California Energy Commission, PIER. Publication number CEC-500-2006- 052. Link, S. & El-Sayed, M. A. “Spectral properties and relaxation dynamics of surface plasmon electronic oscillations in gold and silver nanodots and nanorods.” J. Phys. Chem. B. 1999, 103, 8410-8426. Liu, N., Tang, M. L., Hentschel, M., Giessen, H. & Alivisatos, A. P. “Nanoantenna- enhanced gas sensing in a single tailored nanofocus.” Nature Mater. 2011, doi:10.1038/nmat3029.
115 Lu, Z., J. Zhou, A. Wang, N. Wang, X. Yang. “Synthesis of aluminum-doped ZnO nanocrystals with controllable morphology and enhanced electrical conductivity.“ J. Mater. Chem. 21 4161-4167 (2011). Luther, M. J., Jain, P. K., Ewers, T. & Alivisatos, A. P. “Localized surface plasmon resonance arising from free carriers in doped quantum dots.” Nature Mater. 10, 361- 366 (2011). Maldonado, F., A. Stashans. “Al-doped ZonO: “Electronic, electrical and structural properties.” Journal of Physics and Chemistry of Solids 71, 784-787 (2010). Manifacier, J.C., J.P. Fillard. “Deposition of In2O3-SnO2 layers on glass substrates using a spraying method.” Thin Solid Films 77, 67-80 (1981). Maruyama, T., T. Kanagawa. “Electrochromic properties of iron oxide thin films prepared by chemical vapor deposition.” J. Eletrochem. Soc. 143, 1675-1677 (1996). Maruyama, T., S. Arai. “Electrochromic properties of cobalt oxide thin films prepared by chemical vapor deposition.” J. Electrochem. Soc. 143, 1383-1386 (1996). MacDonald, K.F., Z.L. Samson, M.I. Stockman, N.I. Zheludev. “Ultrafast active plasmonics.” Nature Photonics 3, 55-58 (2009). Mendelsberg, R., S.H.N. Lim, Y.K. Zhu, J. Wallig, D.J. Milliron, A. Anders. “Achieving high mobility ZnO:Al at very high growth rates by dc filtered cathodic arc deposition.” J. Phys. D.: Appl. Phys. 44, 232003 (2011). Mendelsberg, R., G. Garcia, D. Milliron. “Extracting reliable electronic properties from transmission spectra of indium tin oxide films and nanocrystal films by careful applications of the Drude theory.” J. Appl. Phys. 111, DOI:10.1063/1.3695996 (2012). Mergel, D. & Qiao, Z. “Dielectric modelling of optical spectra of thin In2O3:Sn films.” J. Phys. D 35, 794-801 (2002). Minami, T., H. Nanto, S. Takata. “Highly conductive and transparent aluminum doped zinc oxide thin films prepared by RF Magnetron Sputtering.” Japanese J. of Appl. Phys. 23, L280-L282 (1984). Minami, T. “Transparent conducting oxide semiconductors for transparent electrodes.” Semicond. Sci. Technol. 20, S35-S44 (2005). Miyata, N., S. Akiyoshi. “Preperation and electrochromic properties of rf-sputtered molybdenum oxide films.” J. Appl. Phys. 58, 1651-1655 (1985). Monk, P., R. Mortimer, D. Rosseinsky, Electrochromism and Electrochromic Devices. Cambrige University Press, 2007. Murray, C.B., C.R. Kagan, M.G. Bawendi. “Synthesis and characterization of monodisperse nanocrystals and close-packed nanocrystal assemblies.” Annual Review of Material Science 30, 545-610 (2000). Niklasson, G.A., C.G. Granqvist. “Electrochromics for smart windows: thin films of tungsten oxide and nickel oxide, and devices based on these.” J. Mater. Chem. 17, 127-156 (2007). Noguez, C. “Surface plasmons on metal nanoparticles: The influence of shape and physical environment.” J. Phys. Chem. C. 111, 3806-3819 (2007). Norris, D.J., A.L. Efros, S.C. Erwin. “Doped Nanocrystals.” Science 319, 1776-1779 (2008). Novo, C., Funston, A. M., Gooding, A. K. & Mulvaney, P. “Electrochemical Charging of Single Nanorods.” J. Am. Chem. Soc. 131, 14664-14666 (2009).
116 O'Leary, S. K., Johnson, S. R. & Lim, P. K. “The relationship between the distribution of electronic states and the optical absorption spectrum of an amorphous semiconductor: An empirical analysis.” J. Appl. Phys. 82, 3334-3340 (1997). Ozbay, E. “Plasmonics: Merging photonics and electronics at the nanoscale dimensions.” Science 311, 189-193 (2006). Ozisik, M.N. Heat Conduction. Wiley Interscience, 2nd ed. 1993. Talledo, A., C.G. Granqvist. “Electrochromic vanadium-pentoxide-based films: Structural, electrochemical, and optical properties.” J. Appl. Phys. 77, 4655-4666 (1995). Reichman, B.A., A. J. Bard. “The electrochromic process at WO3 electrodes prepared by vacuum evaporation anodic oxidation of W.” Electrochemical Kinetics 126, 583-591 (1979). Reichman, B., A. J. Bard. “Electrochromism at niobium pentoxide electrodes in aqueous and acetonitrile solutions.” J. Electrochem. Soc 127, 241-242 (1980). Roest, A.L., A.J. Houtepen, J.J. Kelly, D. Vanmaerkelbergh. “Electron-conducting quantum-dot solids with ionic charge compensation.” Faraday Discuss, 125, 55-62 (2004). Romero, R., E.A. Dalchiele, F. Martin, D. Leinen, J.R. Ramos-Barrado. “Electrochromic behavior of Nb2O5 thin films with different morphologies obtained by spray pyrolysis.” Solar Energy Material and Solar Cells 93, 222-229 (2009). Rosen, E. L., R. Buonsanti, A. Llordes. A. M. Sawvel, D.J. Milliron, B.A. Helms. “Exceptionally mild reactive stripping of native ligands from nanocrystal surfaces by using Meerwein’s salt.” Angew. Chem. Int. Ed. 50, 1-7 (2011). Sawyer, D.T., A. Sobkowiak, J.L. Roberts. Electrochemistry for Chemists. Wiley- Interscience (1995). Scriven, L.E. “Physics and application of dip coating and spin coating.” Mat. Res. Soc. Symp. 121, 717-729 (1988). Shay, J.L., G. Beni. “Anodic Iridium Oxide Films: A New Electrochromic.” IEEE Transactions on Electron Devices 26, 1138-1143 (1979). Solieman, A. & Aegerter, M. A. “Modeling of optical and electrical properties of In2O3:Sn coatings made by various techniques.” Thin Solid Films 502, 205-211 (2005). Sonntag, R., C. Borgnakke, J. Van Wylen. Fundamentals of Thermodynamics. Wiley, 6th Ed. 2002 Svegl, F., B.Orel, M.G. Hutchins, K.Kalcher. “Structural and spectroelectrochemical investigations of sol-gel derived electrochromic spinel Co3O4 films.” J. of Electrochemical Soc. 143, 1532-1539 (1996). Tahar, R.B.H., T. Ban, Y. Ohya, Y. Takahashi. “Tin doped indium oxide thin films: Electrical properties.” J. of App. Phys. 83, 2631-2645 (1998). Tephan, F., F.E. Ghodsi, N. Ozer, G.G.Tepehan. “Optical properties of sol-gel dip-coated Ta2O5 films for electrochromic applications.” Solar Energy Materials & Solar Cells 59, 265-275 (1999). Thu, T., S. Maenosono. “Synthesis of high-quality Al-doped ZnO nanoink.” J. Appl. Phys. 107, 014308 (2010). Ung, T., Giersig, M., Dunstan, D. & Mulvaney, P. Spectroelectrochemistry of Colloidal Silver. Langmuir, 13, 1773-1782 (1997).
117 U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. 2010 Buildings Energy Data Book. Washington, DC, 2010. Vanmaekelbergh, D., P. Liljeroth. “Electron-conducting quantum dot solids: novel materials based on colloidal semiconductor nanocrystals.” Chem. Soc. Rev. 34, 299- 312 (2005). Wang, C., Shim, M. & Guyot-Sionnest, P. “Electrochromic nanocrystal quantum dots.” Science 291, 2390-2392 (2001). Wang, H., M. Yan, Z. Jiang. “Electrochromic properties of rhodium oxide films prepared by sol-gel method.” Thin Solid Films 401, 211-215 (2001). Wang, J., X.W. Sun, Z. Jiao. “Aplications of nanostructures in electrochromic materials and devices: Recent progress.” Materials 3, 5029-5053 (2010). Wang, Z., X. Hu, P. Kall, U. Helmersson. “High Li+ ion storage capacity and double electrochromic behavior of sol-gel derived iron oxide thin films with sulfate residues.” Chem. Mater. 13, 1976-1983 (2001). Warren, S.C., D.A. Walker, B.A. Crzybowski. “Plasmoeletronics:coupling plasmonic excitation with electron flow.” Langmuir DOI: 10.1021/Ia300377j (2012). Xu, J., X. Wang, J. Shen. “Hydrothermal synthesis of In2O3 for detecting H2S in air.” Sensors and Acutuators B 115, 642-646 (2006) Yamada, N, I. Yasui, Y. Shigesato, H. Li, Y.Ujihira, K. Nomura. “Donor compensation and carrier-transport mechanisms in tin-doped In2O3 films studied by means of conversion electron 119Sn Mossbauer spectroscopy and hall effect measurements.” Jpn. J. Appl. Phys. 39, 4158-4163 (2000). Yu, X., J.B.Bates, G.E. Jellison, F.X. Hart. “A Stable Thin-Film Lithium Electrolyte: Lithium Phosphorus Oxynitrile” J. Eletrochem. Soc. 144, 524-532 (1997). Zarghami, M. H. et al. “P-Type PbSe and PbS quantum dot solids prepared with short chain acids and diacids.” ACS Nano 4, 2475-2585 (2010). Zum Felde, U., Haase, M. & Weller, H. “Electrochromism of highly doped nanocrystalline SnO:Sb.” J. Phys. Chem. B 104, 9388-9395 (2000).
118 Appendix A: Electrochromic Figures of Merit Code clear all; close all;
% This script is being written to characterize different parameters that % validate the figure of merit for our electrochromic devices. More % specifically this will calculate the coloration efficiency, capacitance, % Tlum, Tsolar, Tsolar_NIR, capacitance and surface area for our % electrochromic device.
% Input Parameter:
% Area= Area of the sample that is being tested (cm^2) % Concentration= Concentration of Electrolyte (mol/L) % OCV= Open circuit voltage vs Reference (V) % Voff= Voltage in the off state vs Reference (V) % Von= Voltage in the on state vs Reference (V) % U= Overall heat transfer Coefficient (W/m*K) % T_atm_summer= Average summer temperature in SF (K) % T_atm_winter= Average winter temperature in SF (K) % T_room= Average room temperature (K) % L= thickness of the film (m) % Helmholtz_layer= Helmholtz layer thickness at the surface (m) % Dielectric_constant= Dielectric constant of electrolyte (mF/m) % onData= Optical transperancy of the ON state (nm,Fraction) % offData= Optical transperancy of the OFF state (nm,Fraction) % OCVData= Optical transperancy of the OFF state (nm,Fraction) % elecData_OFF_to_ON=Current injected to the film from applied voltage from % ON to the OFF state (s,mA)
% Output Parameters:
% Charge_Avg= Charge necessary to switch from OFF to ON state (mC) % Coloration_Effeciency_ON_to_OFF= CE for ON to OFF (cm^2/mC) % Capacitance_OFF_to_ON= Capaticance of OFF to ON (mF) % Surface_Area_OFF_ON= Surface area of film from OFF to ON (m^2) % Tsolar_ON= Tsolar of the on state (Fraction) % Tsolar_OFF= Tsolar of the OFF state (Fraction) % Tsolar_OCV= Tsolar of the OCV state (Fraction) % Tsolar_ON_NIR= Tsolar_NIR of the ON state (Fraction) % Tsolar_OFF_NIR= Tsolar_NIR of the OFF state (Fraction) % Tsolar_OCV_NIR= Tsolar_NIR of the OCV state (Fraction) % Tlum_ON= Tlum of the ON state (Fraction) % Tlum_OFF= Tlum of the OFF state (Fraction) % Tlum_OCV= Tlum of the OCV state (Fraction) % SHGC_ON_Summer= SHGC for the ON state in the summer (Fraction) % SHGC_ON_Winter SHGC for the OFF state in the winter (Fraction) % SHGC_OFF_Summer= SHGC for the ON state in the summer (Fraction) % SHGC_OFF_Winter SHGC for the OFF state in the winter (Fraction) % SHGC_OCV_Summer= SHGC for the ON state in the summer (Fraction) % SHGC_OCV_Winter SHGC for the OFF state in the winter (Fraction)
119 %% INPUT PARAMTERS
% Film Parameters Area=3.67; % Cm^2 OCV=3.2; % V Voff=4; % V vs. Reference (Lithium or Ag/Ag+) Von=1.5; % V vs. Reference (Lithium or Ag/Ag+)
% Heat Transfer Parameters U=11.5; %W/m*K % Overall Heat Transfer Coefficient (just conduction for Sputtered ITO) T_atm_summer=342.5; %K T_atm_winter=333; % K T_room=293; %k L=120E-9; % M Thickness of film
% Capacitance Parameters Concentration=0.1; % mol/L Dielectric_constant=64; %F/m
% Import Optical Spectra onData=dlmread('1.5V After Cycling.txt',',',1); offData=dlmread('4V After Cycling.txt',',',1); OCVData=dlmread('OCV.txt',',',1);
% Import Cronoamperomettry data elecData_ON_to_OFF=dlmread('AZO158 10uA 5 cycles.mpt','\t',66);
%% Calculate Coloration Efficiency
Wavelength=onData(:,1);
% Plotting All optical Spectra on the same plot figure ('name','Optical Spectra','NumberTitle','off') plot(Wavelength,offData(:,2),'r',Wavelength,onData(:,2),'b',Wavelength, OCVData(:,2),'g') xlabel('Wavelength (nm)') ylabel('Transmittance') title('Optical Spectra of all STATES') axis([350 2500 0 1]) legend('OFF State','ON State', 'OCV State')
% Calculating the change in optical density ON to OFF state deltaOD_ON_to_OFF=log10(offData(:,2))-log10(onData(:,2));
%Calculating the charge that is pumped into the system to color the %film from ON to OFF State
% Time and Current Data from Imported File time_ON_to_OFF=elecData_ON_to_OFF(:,7); % s Current_ON_to_OFF=elecData_ON_to_OFF(:,8); % mA
120 Voltage_ON_to_OFF=elecData_ON_to_OFF(:,9);
% Determine the amount of time for the sample Current=Current_ON_to_OFF(1)*-1; % mA
count=Current_ON_to_OFF(1); count1=1; for j=1:length(Voltage_ON_to_OFF) if Current_ON_to_OFF(j)==count else Time_vect(count1)=time_ON_to_OFF(j); count1=count1+1; count=Current_ON_to_OFF(j); end end
% Take average time between ON and OFF for j=1:(length(Time_vect)-1) Time(j)=Time_vect(j+1)-Time_vect(j); end
Charge=Time*Current; Charge_avg=mean(Charge); Charge_STD=std(Charge);
% Plotting Time and Current Data with Curve Fit figure ('name','t vs I for ON to OFF','NumberTitle','off') plot(time_ON_to_OFF,Current_ON_to_OFF,'b') xlabel('Time (s)') ylabel('Current (mA)') title('Current vs Time for Step Potential Function of ON to OFF State')
% Calculating the coloration efficiency for the OCV to ON State
Coloration_Effeciency_ON_to_OFF= deltaOD_ON_to_OFF/(Charge_avg/Area); %cm^2/mC
Coloration_Effeciency_ON_to_OFF=Coloration_Effeciency_ON_to_OFF*1000; %cm^2/C B2=xlswrite('Coloration_Effeciency_ON_to_OFF.xls', Coloration_Effeciency_ON_to_OFF);
% PLotting it as a Function of Wavelength figure ('name','Coloration Efficiency ON to OFF','NumberTitle','off') plot(Wavelength,Coloration_Effeciency_ON_to_OFF) xlabel('Wavelength (nm)') ylabel('Colouration Efficiency (cm^2/C)') Title( 'Colouration Efficiency for ON to OFF State')
%% Calculating the Capacitance
% Calculating the Debye length
121 DebyeLength=(0.03436)*(Dielectric_constant/Concentration)^(1/2); %nm
% Calculate the Capacitance Capacitance_OFF_to_ON= Charge_avg/(Voff-Von); %mF
% Calculate the Surface Area Surface_Area_OFF_ON=((Capacitance_OFF_to_ON*10^-3*DebyeLength*10^- 9)/(Dielectric_constant*8.85E-12))*(10000); %cm^2
%% Calculating the T solar Change in spectra
% Importing the AM 1.5 Spectra AM1_5g=xlsread('ASTMG173.xls','SMARTS2','C143:C1704'); AMlambda=xlsread('ASTMG173.xls','SMARTS2','A143:A1704');
% Integrate the Solar Spectrum
% Establishing a Curve Fitting for TOTAL Solar Spectrum [curve_fit,gof] = fit(AMlambda,AM1_5g,'cubicinterp');
% Plotting Total Spectra with Curve Fit figure ('name','Solar Spectrum with Fit','NumberTitle','off') plot(curve_fit,'x',AMlambda,AM1_5g,'r') xlabel('Wavelength (nm)') ylabel('Solar Irradiance (mA)') legend('Data','Fitting') title('AM 1.5g vs Fitting')
% Integrating Total Spectra M=length(AMlambda); a=AMlambda(1); b=AMlambda(M); AM1_5g_int=MyIntegration(curve_fit,a,b,M); %(W/m^2)
% Establishing a Curve Fitting for NIR Solar Spectrum for j=1:1080 AM1_5g_NIR(j)=AM1_5g(431+j); AMlambda_NIR(j)= AMlambda(431+j); end [curve_fit,gof] = fit(AMlambda_NIR',AM1_5g_NIR','cubicinterp');
% Plotting Total Spectra with Curve Fit figure ('name','NIR Solar Spectrum with Fit','NumberTitle','off') plot(curve_fit,'x',AMlambda_NIR,AM1_5g_NIR,'r') xlabel('Wavelength (nm)') ylabel('NIR Solar Irradiance (mA)') legend('Data','Fitting') title('AM 1.5g vs Fitting')
122 % Integrating Total Spectra M=length(AMlambda_NIR); a=AMlambda_NIR(1); b=AMlambda_NIR(M); AM1_5g_NIR_int=MyIntegration(curve_fit,a,b,M); %(W/m^2)
% Calculating Tsol for ON State
%Calculate Tsolar from range of 350-400 Tsolprod_ON(1,1)=AM1_5g(1)*onData(1,2); for j=1:48 Tsolprod_ON(j+1,1)=AM1_5g(j+2)*onData(j+1,2); end
%Calculate Tsolar from range of 400-1700 for j=50:1350 Tsolprod_ON(j,1)=onData(j+1,2)*AM1_5g(j+51); end
%Calculate Tsolar from range of 1705-2500 g=5; z=0; for j=1351:1510 g=g+z; z=4; Tsolprod_ON(j+1,1)=onData(j+g,2)*AM1_5g(j+52); end
% Make Wavelength Scale for k=1:1351 TsolLambda(k,1)=349+k; end for k=1403:1562 TsolLambda(k-51,1)=AMlambda(k); end
% Integrate Tsolar product as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TsolLambda,Tsolprod_ON,'cubicinterp');
% Integrating M=length(TsolLambda); a=TsolLambda(1); b=TsolLambda(M); Tsolprod_int_ON=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Solar Transmittance as of the ON state Tsolar_ON=Tsolprod_int_ON/AM1_5g_int; % Percentage
% Integrate Tsolar product as a function of Lambda for NIR
% Establishing a Curve Fitting
123 for j=1:1080; TsolLambda_NIR(j)=TsolLambda(431+j); Tsolprod_ON_NIR(j)=Tsolprod_ON(431+j); end [curve_fit,gof] = fit(TsolLambda_NIR',Tsolprod_ON_NIR','cubicinterp');
% Integrating M=length(TsolLambda_NIR); a=TsolLambda_NIR(1); b=TsolLambda_NIR(M); Tsolprod_int_ON_NIR=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Solar Transmittance as of the ON state Tsolar_NIR_ON=Tsolprod_int_ON_NIR/AM1_5g_NIR_int; % Percentage
% Calculating Tsol for OFF State
%Calculate Tsolar from range of 350-400 Tsolprod_OFF(1,1)=AM1_5g(1)*offData(1,2); for j=1:48 Tsolprod_OFF(j+1,1)=AM1_5g(j+2)*offData(j+1,2); end
%Calculate Tsolar from range of 400-1700 for j=50:1350 Tsolprod_OFF(j,1)=offData(j+1,2)*AM1_5g(j+51); end
%Calculate Tsolar from range of 1705-2500 g=5; z=0; for j=1351:1510 g=g+z; z=4; Tsolprod_OFF(j+1,1)=offData(j+g,2)*AM1_5g(j+52); end
% Make Wavelength Scale for k=1:1351 TsolLambda(k,1)=349+k; end for k=1403:1562 TsolLambda(k-51,1)=AMlambda(k); end
% Integrate Tsolar product as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TsolLambda,Tsolprod_OFF,'cubicinterp');
% Integrating
124 M=length(TsolLambda); a=TsolLambda(1); b=TsolLambda(M); Tsolprod_int_OFF=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Solar Transmittance as of the OFF state Tsolar_OFF=Tsolprod_int_OFF/AM1_5g_int; % Fraction
% Integrate Tsolar product of NIR as a function of Lambda for NIR
% Establishing a Curve Fitting for j=1:1080; TsolLambda_NIR(j)=TsolLambda(431+j); Tsolprod_OFF_NIR(j)=Tsolprod_OFF(431+j); end [curve_fit,gof] = fit(TsolLambda_NIR',Tsolprod_OFF_NIR','cubicinterp');
% Integrating M=length(TsolLambda_NIR); a=TsolLambda_NIR(1); b=TsolLambda_NIR(M); Tsolprod_int_OFF_NIR=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Solar Transmittance as of the ON state Tsolar_NIR_OFF=Tsolprod_int_OFF_NIR/AM1_5g_NIR_int; % Percentage
% Calculating Tsol for OCV State
%Calculate Tsolar from range of 350-400 Tsolprod_OCV(1,1)=AM1_5g(1)*OCVData(1,2); for j=1:48 Tsolprod_OCV(j+1,1)=AM1_5g(j+2)*OCVData(j+1,2); end
%Calculate Tsolar from range of 400-1700 for j=50:1350 Tsolprod_OCV(j,1)=OCVData(j+1,2)*AM1_5g(j+51); end
%Calculate Tsolar from range of 1705-2500 g=5; z=0; for j=1351:1510 g=g+z; z=4; Tsolprod_OCV(j+1,1)=OCVData(j+g,2)*AM1_5g(j+52); end
% Make Wavelength Scale for k=1:1351
125 TsolLambda(k,1)=349+k; end for k=1403:1562 TsolLambda(k-51,1)=AMlambda(k); end
% Integrate Tsolar product as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TsolLambda,Tsolprod_OCV,'cubicinterp');
% Integrating M=length(TsolLambda); a=TsolLambda(1); b=TsolLambda(M); Tsolprod_int_OCV=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Solar Transmittance as of the ON state Tsolar_OCV=Tsolprod_int_OCV/AM1_5g_int; % Percentage
% Integrate Tsolar product of NIR as a function of Lambda for NIR
% Establishing a Curve Fitting for j=1:1080; TsolLambda_NIR(j)=TsolLambda(431+j); Tsolprod_OCV_NIR(j)=Tsolprod_OCV(431+j); end [curve_fit,gof] = fit(TsolLambda_NIR',Tsolprod_OCV_NIR','cubicinterp');
% Integrating M=length(TsolLambda_NIR); a=TsolLambda_NIR(1); b=TsolLambda_NIR(M); Tsolprod_int_OCV_NIR=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Solar Transmittance as of the ON state Tsolar_NIR_OCV=Tsolprod_int_OCV_NIR/AM1_5g_NIR_int; % Percentage
%% Calculating T luminous
% Importing the Eye Luminous EyeLum=xlsread('EyeLuminous.xls','Data','B1:B43'); EyeLambda=xlsread('EyeLuminous.xls','Data','A1:A43');
%Plot the Eye Photic Spectrum figure ('name','Luminous Spectrum','NumberTitle','off') plot(EyeLambda,EyeLum) xlabel('Wavelength (nm)') ylabel('Percentage')
126 title('Photic Eye Luminous')
%Calculate Product of Solar Spectrum with Luminesance and integral
%Calculate Tlumprod from range of 350-380 for j=1:3 Tlumprod(j,1)=0; end % Calculate from 380 to 400 Tlumprod(4,1)=AM1_5g(61)*EyeLum(1); Tlumprod(5,1)=AM1_5g(82)*EyeLum(2);
%Calculate Tlum from range of 400-770 g=91; for j=6:43 Tlumprod(j,1)=AM1_5g(g+10)*EyeLum(j-3); g=g+10; end
%Calculate Tsolar from range of 770-2500 for j=44:216 Tlumprod(j,1)=0; end
% Calculate TlumLambda TlumLambda=[350:10:2500]';
% Integrate Tlum as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TlumLambda,Tlumprod,'cubicinterp');
% Integrating M=length(TlumLambda); a=TlumLambda(1); b=TlumLambda(M); Tlumprod_int=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Calculating Tlum for ON State
%Calculate Tlum from range of 350-380 for j=1:3 Tlumprod_ON(j,1)=0; end
% Calculate from 380 to 400 Tlumprod_ON(4,1)=onData(31,2)*AM1_5g(61)*EyeLum(1); Tlumprod_ON(5,1)=onData(41,2)*AM1_5g(81)*EyeLum(2);
%Calculate Tlum from range of 400-770
127 g=41; gg=91; for j=6:43 Tlumprod_ON(j,1)=onData(g+10,2)*AM1_5g(gg+10)*EyeLum(j- 3); g=g+10; gg=gg+10; end
%Calculate Tsolar from range of 770-2500 for j=44:216 Tlumprod_ON(j,1)=0; end
% Integrate Tsolar product as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TlumLambda,Tlumprod_ON,'cubicinterp');
% Integrating M=length(TlumLambda); a=TlumLambda(1); b=TlumLambda(M); Tlumprod_int_ON=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Tlum Transmittance as of the ON state Tlum_ON=Tlumprod_int_ON/Tlumprod_int; % Percentage
% Calculating Tlum for OFF State
%Calculate Tlum from range of 350-380 for j=1:3 Tlumprod_OFF(j,1)=0; end
% Calculate from 380 to 400 Tlumprod_OFF(4,1)=offData(31,2)*AM1_5g(61)*EyeLum(1); Tlumprod_OFF(5,1)=offData(41,2)*AM1_5g(82)*EyeLum(2);
%Calculate Tlum from range of 400-770 g=41; gg=91; for j=6:43
Tlumprod_OFF(j,1)=offData((g+10),2)*AM1_5g(gg+10)*EyeLum(j-3); g=g+10; gg=gg+10; end
%Calculate Tsolar from range of 770-2500 for j=44:216 Tlumprod_OFF(j,1)=0; end
128
% Establishing a Curve Fitting [curve_fit,gof] = fit(TlumLambda,Tlumprod_OFF,'cubicinterp');
% Integrating M=length(TlumLambda); a=TlumLambda(1); b=TlumLambda(M); Tlumprod_int_OFF=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Tlum Transmittance as of the ON state Tlum_OFF=Tlumprod_int_OFF/Tlumprod_int; % Percentage
% Calculating Tlum for OCV State
%Calculate Tlum from range of 350-380 for j=1:3 Tlumprod_OCV(j,1)=0; end
% Calculate from 380 to 400 Tlumprod_OCV(4,1)=OCVData(31,2)*AM1_5g(61)*EyeLum(1); Tlumprod_OFF(5,1)=OCVData(41,2)*AM1_5g(82)*EyeLum(2);
%Calculate Tlum from range of 400-770 g=9; for j=6:43 Tlumprod_OCV(j,1)=OCVData(j+((j- 1)*g),2)*AM1_5g((g+j)+41+j)*EyeLum(j-3); end
%Calculate Tsolar from range of 770-2500 for j=44:216 Tlumprod_OCV(j,1)=0; end
% Integrate Tsolar product as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TlumLambda,Tlumprod_OCV,'cubicinterp');
% Integrating M=length(TlumLambda); a=TlumLambda(1); b=TlumLambda(M); Tlumprod_int_OCV=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Solve for Tlum Transmittance as of the OCV state Tlum_OCV=Tlumprod_int_OCV/Tlumprod_int; % Percentage
129 %% Calculate the Solar Heat Gain Coefficient
% Calculate SHGC for the ON State for the Summer SHGC_ON_Summer= (Tsolprod_int_ON*Area*1E-4-L*U*(T_atm_summer- T_room))/(AM1_5g_int*Area*1E-4);
% Calculate SHGC for the ON State for the Winter SHGC_ON_Winter= (Tsolprod_int_ON*Area*1E-4-L*U*(T_atm_winter- T_room))/(AM1_5g_int*Area*1E-4);
% Calculate SHGC for the OFF State for the Summer SHGC_OFF_Summer= (Tsolprod_int_OFF*Area*1E-4-L*U*(T_atm_summer- T_room))/(AM1_5g_int*Area*1E-4);
% Calculate SHGC for the OFF State for the Winter SHGC_OFF_Winter= (Tsolprod_int_OFF*Area*1E-4-L*U*(T_atm_winter- T_room))/(AM1_5g_int*Area*1E-4);
% Calculate SHGC for the OCV State for the Summer SHGC_OCV_Summer= (Tsolprod_int_OCV*Area*1E-4-L*U*(T_atm_summer- T_room))/(AM1_5g_int*Area*1E-4);
% Calculate SHGC for the OFF State for the Winter SHGC_OCV_Winter= (Tsolprod_int_OCV*Area*1E-4-L*U*(T_atm_winter- T_room))/(AM1_5g_int*Area*1E-4);
%% Create a dataset
Summary=dataset(Charge_avg,Charge_STD,Capacitance_OFF_to_ON,Surface_Are a_OFF_ON,Tsolar_ON,Tsolar_OFF,Tsolar_NIR_ON,Tsolar_NIR_OFF,Tlum_ON,Tlum _OFF) export(Summary,'file','i11 Sample Au 1 Chronopotent Summary.txt')
130 function Charge=MyIntegration(f,a,b,M)
% This function will use the Composite Simpson rule to integrate the % current as a function of timefor the charge calculation
% Input -f is the integrand input as a string. In this case the Current % -a and b are the upper and lower limits of the integration. In this % case time 0 to XXX in seconds % -M is the number of sub intervals. In this case the length of the % time vector % Output -Charge is the value of the charge that we recieve h=(b-a)/(2*M); s1=0; s2=0; for k=1:M x=a+h*(2*k-1); s1=s1+feval(f,x); end for k=1:(M-1) x=a+h*2*k; s2=s2+feval(f,x); end Charge=h*(feval(f,a)+feval(f,b)+4*s1+2*s2)/3;
131 Appendix B: Electrochromic Energy Performance Code clear all; close all; % This program is written to calculate the temperature profile, energy % savings from HVAC systems and estimate cost reduction in heating and % cooling for an IDEAL 4 wall building with ideally glass walls and % mininumal framing and a covered roof.
% Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/1/11 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % OPTICAL SPECTRA OF COATINGS % onData= Optical transperancy of the ON state (nm,Fraction) % offData= Optical transperancy of the OFF state (nm,Fraction) % Astatic=Tsolar of static film (Fraction) % Dynamic= whether or not its a dynamic film (0 or 1) % Static= whether or not its a static film (0 or 1) % % SOLAR ANGLE INPUTS % deltagamma=surface azimuth angle from north (degrees) % e=surface inclination (degrees) % l=lattitude (Degrees) % long= longtitude (degrees) (east>0, west <0) % Ut= universal time (hours) % % HEAT TRANSFER INPUTS % L1=Outdoor glass length (m) % L2=Insulating gap length (m) % L3=Indoor glass length (m) % Kg=Thermal Conductivity of the glass (W/m*K) % Kair=Thermal Conductivity of air (W/m*k) % Hair=Heat transfer coefficient of free convection (W/m^2*K) % Tinf=Temperature of exterior weather (K) % n=number of nodes per wall for room average temperature calculation (unitless) % % ROOM SPECIFICATIONS % Lx=length of the room (m) % Ly=width of the room (m) % Lz=height of the room (m) % % ELECTRICITY RATES % RateW= electricity rate for that location in the winter % RateS= electricity rate for that location in the summer
% Output: % Id=direct surface flux (W/m^2) % Troom=Room Temperature (C)
132
% Define the inputs: % OPTICAL SPECTRA OF COATINGS onData=dlmread('1.5V AZO.txt',',',1); %(nm,Fraction) offData=dlmread('4V AZO.txt',',',1); %(nm,Fraction) Astatic=0.848; % IGDB #2001 dynamic=1; % Unitless (1=Yes, 0=No) static=0; % Unitless (1=Yes, 0=No)
% SOLAR ANGLE INPUTS deltagamma=0; % Degrees from North clockwise (from 0 to 89) e=90; %degrees l=42.35; %degrees (Phoenix) long=-71.06; %(degrees)(Phoenix) Ut=-5; %hours
% HEAT TRANSFER INPUTS L1=4E-3; %(m) L2=1E-3; %(m) L3=3E-3; %(m) Kg=1.4; %W/m*K Kair=0.024; %W/m*K Hair=25; %W/m^2*K Tinf=xlsread('Boston 2010 Weather averages.xls','Data','D2:D13'); %C node=10; %unitless
% ROOM SPECIFICATIONS Lx=6; %m Ly=6; %m Lz=3.04; %m
% ELECTRICITY RATES RateW=0.06267; %kWhr RateS=0.11664; %kWhr
% Calculate the temperature profile in the room for the entire year %Initializing vectors IdnorthTotal=zeros([365,24]); IdwestTotal=zeros([365,24]); IdsouthTotal=zeros([365,24]); IdeastTotal=zeros([365,24]); TroomNtotal=zeros([365,24]); TroomStotal=zeros([365,24]); TroomWtotal=zeros([365,24]); TroomEtotal=zeros([365,24]); TroomAVGtotal=zeros([365,24]); TroomAVGtotal2=zeros([365,24]); TroomTOTALday=zeros([365,24,node,node]); HVACenergy=zeros([365,24]); HVACenergyDAY=zeros([365,1]); TroomDAY=zeros([365,1]); EnergyBILL=zeros([365,24]); EnergyBillDAY=zeros([365,1]); ncount=0;
133 tcount=0; % Initialize Temperature and TinfK=Tinf+273; %K
%Calculate the solar flux at the glazzing film for specific time if dynamic==1
[Aon,Aoff,AMg]=MySolarflux(onData,offData,Astatic,dynamic,static); end if static==1
[Aon,Aoff,AMg]=MySolarflux(onData,offData,Astatic,dynamic,static); end if static==0 && dynamic==0
[Aon,Aoff,AMg]=MySolarflux(onData,offData,Astatic,dynamic,static); end for n=1:365 ncount=ncount+1 tcount=0; for t=1:24 tcount=tcount+1; %% CALCULATE NORH WALL PROPERTIES
%Calculate the solar gain at the glazzing film at the North wall gamma=0+deltagamma; if dynamic==1 if n>59 && n<243 A=Aon; %A=0.5*AMg else A=Aoff; end end if static==1 A=Aon; end if static==0 && dynamic==0 A=AMg; end Idnorth=MySolargain(gamma,e,t,n,l,long,Ut,A); if Idnorth<0 Idnorth=0; end IdnorthTotal(n,t)=Idnorth;
% Calculate room temperature at North Wall TroomN=MyRoomTemperature(L1,L2,L3,Kg,Kair,Hair,Tinf,n,Idnorth); %K TroomNtotal(n,t)=TroomN; %C
%% Calculate EAST WALL PROPERTIES
%Calculate the solar gain at the glazzing film at the East wall
134 gamma=90+deltagamma; if dynamic==1 if n>59 && n<243 A=Aon; %A=0.5*AMg else A=Aoff; end end if static==1 A=Aon; end if static==0 && dynamic==0 A=AMg; end Ideast=MySolargain(gamma,e,t,n,l,long,Ut,A); if Ideast<0 Ideast=0; end IdeastTotal(n,t)=Ideast;
% Calculate room temperature at East Wall TroomE=MyRoomTemperature(L1,L2,L3,Kg,Kair,Hair,Tinf,n,Ideast); %K TroomEtotal(n,t)=TroomE; %C
%% Calculate SOUTH wall properties
%Calculate the solar gain at the glazzing film at the South wall gamma=180+deltagamma; if dynamic==1 if n>59 && n<243 A=Aon; %A=0.5*AMg else A=Aoff; end end if static==1 A=Aon; end if static==0 && dynamic==0 A=AMg; end Idsouth=MySolargain(gamma,e,t,n,l,long,Ut,A); if Idsouth<0 Idsouth=0; end IdsouthTotal(n,t)=Idsouth;
% Calculate room temperature at South Wall TroomS=MyRoomTemperature(L1,L2,L3,Kg,Kair,Hair,Tinf,n,Idsouth); %K TroomStotal(n,t)=TroomS; %C
135 %% Calculate WEST wall properties
%Calculate the solar gain at the glazzing film at the west wall gamma=270+deltagamma; if dynamic==1 if n>59 && n<243 A=Aon; %A=0.5*AMg else A=Aoff; end end if static==1 A=Aon; end if static==0 && dynamic==0 A=AMg; end Idwest=MySolargain(gamma,e,t,n,l,long,Ut,A); if Idwest<0 Idwest=0; end IdwestTotal(n,t)=Idwest;
% Calculate room temperature at West Wall TroomW=MyRoomTemperature(L1,L2,L3,Kg,Kair,Hair,Tinf,n,Idwest); %C TroomWtotal(n,t)=TroomW; %C %% Average Room Temperature TroomTOTAL=MyAvgRoomTemp(TroomN,TroomE,TroomS,TroomW,node); for i=1:node for j=1:node TroomTOTALday(n,t,i,j)=TroomTOTAL(i,j); end end TroomAVGsingle=mean(mean(TroomTOTAL)); TroomAVGtotal(n,t)=TroomAVGsingle; TroomAVGtotal2(n,t)=(TroomN+TroomE+TroomS+TroomW)/4; %% Average Heating and Airconditioning loads if n>59 && n<243 HVAC=MyAirConEnergy(TroomAVGsingle,Lx,Ly,Lz,Tinf,n); %kW else HVAC=MyHeatEnergy(TroomAVGsingle,Lx,Ly,Lz,Tinf,n); %kW end HVACenergy(n,t)=HVAC; %kW %% Estimated Cost for building if n>59 && n<243 EnergyBILL(n,t)=RateS*HVAC;% Dollars else EnergyBILL(n,t)=RateS*HVAC;% Dollars end end TroomDAY(n,1)=mean(TroomAVGtotal(n,:)); %kW HVACenergyDAY(n,1)=sum(HVACenergy(n,:)); %kW EnergyBillDAY(n,1)=sum(EnergyBILL(n,:)); %Dollars end
136 %% Calculate Monthly Energy Use, Room Temperature, Cost TroomAVGMonth=MyTroomAVGmonth(TroomDAY); HVACavgMonth=MyHVACavgmonth(HVACenergyDAY); EnergyBillMonth=MyEnergyBillmonth(EnergyBillDAY); %% Calculate Yearly Energy Use and Tempeature cost EnergybillYEAR=sum(EnergyBillMonth) HVACyear=sum(HVACavgMonth)
%% Generate Mesh Grid [X,Y]=meshgrid(0:Lx/(node-1):Lx,0:Ly/(node-1):Ly); [X1,Y1]=meshgrid(0:Lx/(100*(node-1)):Lx,0:Ly/(100*(node-1)):Ly);
% Plot the temperature distribution at noon for Winter Solistice for i=1:node for j=1:node TWsolistice(i,j)=TroomTOTALday(355,12,i,j); end end figure ('name','Winter Solistice Temperature Profile at Noon','NumberTitle','off') Z=interp2(X,Y,TWsolistice,X1,Y1); surf(X1,Y1,Z,'linestyle','none') colorbar xlabel('X','fontsize',12); ylabel('Y','fontsize',12); zlabel('Room Temperature(C)','fontsize',12); title('Room Temperature on Winter Solistice','fontsize',16)
% Plot the temperature distribution at noon for Summer Solistice for i=1:node for j=1:node TSsolistice(i,j)=TroomTOTALday(172,12,i,j); end end figure ('name','Summer Solistice Temperature Profile at Noon','NumberTitle','off') Z1=interp2(X,Y,TSsolistice,X1,Y1); surf(X1,Y1,Z1,'linestyle','none') colorbar xlabel('X','fontsize',12); ylabel('Y','fontsize',12); zlabel('Room Temperature(C)','fontsize',12); title('Room Temperature on Summer Solistice','fontsize',16)
% Plot the temperature distribution for the Winter Solistice Day xw=[1:24]; yw=TroomAVGtotal(355,:); ys=TroomAVGtotal(172,:); figure ('name','Winter Solistice Room Temperature Profile from Sunlight','NumberTitle','off') bar(xw,yw) xlabel('Time','fontsize',12); ylabel('Temperature (C)','fontsize',12); title('Room Temperature of Winter Solistice from Sunlight','fontsize',16)
137
% Plot the temperature distribution for the Summer Solistice Day figure ('name','Summer Solistice Temperature Profile from Sunlight','NumberTitle','off') bar(xw,ys,'r') xlabel('Time','fontsize',12); ylabel('Temperature (C)','fontsize',12); title('Room Temperature of Summer Solistice from Sunlight','fontsize',16)
% Plot the temperature distribution for each month figure ('name','Monthly Temperature Distribution from Sunlight','NumberTitle','off') xmonth=(1:12); bar(xmonth,TroomAVGMonth,'b') xlabel('Month','fontsize',12); ylabel('Temperature (C)','fontsize',12); title('Monthly Temperature Distribution from Sunlight','fontsize',16)
% Plot the temperature distribution for each month figure ('name','Monthly Power Consumption','NumberTitle','off') bar(xmonth,HVACavgMonth,'b') xlabel('Month','fontsize',12); ylabel('Power Consumption (kW)','fontsize',12); title('Monthly Power Consumption','fontsize',16)
% Plot the temperature distribution for each month figure ('name','Monthly HVAC Expense','NumberTitle','off') bar(xmonth,EnergyBillMonth,'b') xlabel('Month','fontsize',12); ylabel('Energy Expense (Dollar)','fontsize',12); title('Monthly Energy Expense','fontsize',16)
138 function AirConEnergy=MyAirConEnergy(TroomAVGtotal,Lx,Ly,Lz,Tinf,n)
% This function is used to compute the energy use for an airconditioning % unit.
% Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/2011 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % TroomAvgTotal= Average room temperature at time t
% Constants: % COP coefficient of performance (unitless) % Cpair= Specific heat of air at temperature range for room (kJ/Kg*K) % ro=density of air (kg/m^3) % time= time for airconditioning being on (s)
% Assumption: Looking at the SEER rating for an energy star central air % conditioning load 14SEER/11 EER
% Initialize Constants COP=14/3.792; % unitless Cpair=1.005; %kJ/kg*K ro=1.166; %kg/m^3 time=3600; %60s
% Calculate the volume of the room roomVolume=Lx*Ly*Lz;
% Determine outside Temperature if n>=1 && n<=31 Tout=Tinf(1); end if n>=32 && n<=59 Tout=Tinf(2); end if n>=60 && n<=90 Tout=Tinf(3); end if n>=91 && n<=120 Tout=Tinf(4); end if n>=121 && n<=151 Tout=Tinf(5); end if n>=152 && n<=181 Tout=Tinf(6);
139 end if n>=182 && n<=212 Tout=Tinf(7); end if n>=213 && n<=243 Tout=Tinf(8); end if n>=244 && n<=273 Tout=Tinf(9); end if n>=274 && n<=304 Tout=Tinf(10); end if n>=305 && n<=334 Tout=Tinf(11); end if n>=335 && n<=365 Tout=Tinf(12); end
% Calculate the heat load for the room if TroomAVGtotal==21 AirConEnergy=abs(((Cpair*ro*(Tout-21)*roomVolume)/time)/COP); %kW else AirConEnergy=abs(((Cpair*ro*(TroomAVGtotal- Tout)*roomVolume)/time)/COP); %kW end
140 function TroomTOTAL=MyAvgRoomTemp(TroomN,TroomE,TroomS,TroomW,n)
% MyAvgRoomTemp is a function that solves the temperature distrubution of a % glass room using a matrix At=b
% Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 6/3/11 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % TroomN= temperature of the north wall % TroomE= temperature of the north wall % TroomS= temperature of the north wall % TroomW= temperature of the north wall % n= number of nodes % % Output: % t= solution vector of At=b
% Set the A matrix to zeros A=zeros(n^2-(n*2)-2*(n-2)); b=zeros(n^2-(n*2)-2*(n-2),1)';
% Set up Final Room Temperature Matrix TroomTOTAL=zeros(n);
% Store matrix A diagnol for i=1:(n^2-(n*2)-2*(n-2)); A(i,i)=-4; end
% Store '1' pattern to the left of the whole diagnol for i=1:(n^2-(n*2)-2*(n-2))-1 for j=i+1 A(i,j)=1; end end
% Remove 1 at every second point at the left side of diagnol for i=(n-2):(n-2):(n^2-(n*2)-2*(n-2))-1 for j=i+1 A(i,j)=0; end end
% Store '1' pattern to the right of the whole diagnol for i=1:(n^2-(n*2)-2*(n-2))-1 for j=i+1
141 A(j,i)=1; end end
% Remove 1 at every second point at the right side of diagnol for i=(n-2):(n-2):(n^2-(n*2)-2*(n-2))-1 for j=i+1 A(j,i)=0; end end
% Store '1' pattern to the far left of the whole diagnol for i=1:(n^2-(n*2)-2*(n-2))-(n-2) for j=i+(n-2) A(i,j)=1; end end
% Store '1' pattern to the far left of the whole diagnol for i=1:(n^2-(n*2)-2*(n-2))-(n-2) for j=i+(n-2) A(j,1)=1; end end
% Store corner b vector b(1)=-(TroomS+TroomW); b(n^2-(n*2)-2*(n-2))=-(TroomN+TroomE); b(n-2)=-(TroomE+TroomS); b((n^2-(n*2)-2*(n-2))-(n-2)+1)=-(TroomN+TroomW);
% Store South Wall in b vector for i=1:(n-4) b(i+1)=-TroomS; b((n^2-(n*2)-2*(n-2))-i)=-TroomN; end for i=0:(n-4)-1 b((n-1)+i*(n-2))=-TroomW; b((n^2-(n*2)-2*(n-2))-(n-2)-i*(n-2))=-TroomE; end
% Call on MyGEwithPP.m t=MyGEwithPP(A,b);
% Establishing overall matrix with temperature distribution TroomTOTAL(1,1)=(TroomW+TroomN)/2; TroomTOTAL(1,n)=(TroomE+TroomN)/2; TroomTOTAL(n,1)=(TroomW+TroomS)/2; TroomTOTAL(n,n)=(TroomE+TroomS)/2; for i=1:(n-2) TroomTOTAL(1,i+1)=TroomN; TroomTOTAL(i+1,1)=TroomW; TroomTOTAL(i+1,n)=TroomE; TroomTOTAL(n,i+1)=TroomS;
142 end for i=1:(n-2) for j=1:(n-2) TroomTOTAL(i+1,j+1)=t((n-2)*((n-2)-i)+j); end end
143 function EnergyBillMonth=MyEnergyBillmonth(EnergyBillDAY)
% This function is used to compute the HVAC energy per month % Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/2011 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % TroomAVGtotal
% Initialize Troom Average vector EnergyBillMonth=zeros(12,1);
%Calculate Troom Monthly Values EnergyBillMonth(1,1)=sum(EnergyBillDAY(1:31,1)); EnergyBillMonth(2,1)=sum(EnergyBillDAY(32:59,1)); EnergyBillMonth(3,1)=sum(EnergyBillDAY(60:90,1)); EnergyBillMonth(4,1)=sum(EnergyBillDAY(91:120,1)); EnergyBillMonth(5,1)=sum(EnergyBillDAY(121:151,1)); EnergyBillMonth(6,1)=sum(EnergyBillDAY(152:181,1)); EnergyBillMonth(7,1)=sum(EnergyBillDAY(182:212,1)); EnergyBillMonth(8,1)=sum(EnergyBillDAY(213:243,1)); EnergyBillMonth(9,1)=sum(EnergyBillDAY(244:273,1)); EnergyBillMonth(10,1)=sum(EnergyBillDAY(274:304,1)); EnergyBillMonth(11,1)=sum(EnergyBillDAY(305:334,1)); EnergyBillMonth(12,1)=sum(EnergyBillDAY(335:365,1));
144 function x=MyGEwithPP(A,b) % MyGEwithPP solves a system of equations using gauss elimination with % partial pivoting procedure % Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 09/17/06 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % A= n-by-n matrix % b= n-vector % % Output: % x= solution vector of Ax=b
% Assign vector dimensions and initialize x-vector n=length(b);x=zeros(n,1);d=zeros(1,n);
% Test to make sure that it is a n-by-n matrix w=size(A); if w(1)==w(2); else disp('ERROR: Matrix is not n-by-n') error('Must input a n-by-n matrix ') end
% Test to make sure that b is a n-by-1 vector e=size(b); if e(1)==1; else disp('ERROR: Vector is not n-by-1') error('Must input a n-by-1 vector') end
% Set Augmented matrix h=[A,b'];
% Convert to UpperTriangular Form for f=1:n-1; % Look for the Pivot q=abs(h(f,f)); r=f; for k=f+1:n; v=abs(h(k,f)); if v>q; r=k; v=q; end end % Switch the rows if r~=f;
145 d=h(f,:); h(f,:)=h(r,:); h(r,:)=d; end % Check for singularity if abs(h(f,f))<10^-12; disp('ERROR: Matrix A is singular') error('Must input a non-singular matrix') end % Eliminate the rest of the column for j=f+1:n; i=h(j,f)/h(f,f); h(j,f)=0; for ll=f+1:n+1; h(j,ll)=h(j,ll)-i*h(f,ll); end end end
% Call up on MyUpperTri.m to solve for x-vector x=MyUpperTri(h(1:n,1:n),h(1:n,n+1));
146 function HeatEnergy=MyHeatEnergy(TroomAVGtotal,Lx,Ly,Lz,Tinf,n)
% This function is used to compute the energy use for a heater % unit.
% Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/2011 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % TroomAvgTotal= Average room temperature at time t
% Constants: % COP coefficient of performance (unitless) % Cpair= Specific heat of air at temperature range for room (kJ/Kg*K) % ro=density of air (kg/m^3) % time= time for airconditioning being on (s)
% Assumption: Looking at the SEER rating for an energy star central air % conditioning load 14SEER/11 EER
% Initialize Constants COP=14/3.792; % unitless Cpair=1.005; %kJ/kg*K ro=1.166; %kg/m^3 time=3600; %60s
% Calculate the volume of the room roomVolume=Lx*Ly*Lz;
% Determine outside Temperature if n>=1 && n<=31 Tout=Tinf(1); end if n>=32 && n<=59 Tout=Tinf(2); end if n>=60 && n<=90 Tout=Tinf(3); end if n>=91 && n<=120 Tout=Tinf(4); end if n>=121 && n<=151 Tout=Tinf(5); end if n>=152 && n<=181 Tout=Tinf(6); end
147 if n>=182 && n<=212 Tout=Tinf(7); end if n>=213 && n<=243 Tout=Tinf(8); end if n>=244 && n<=273 Tout=Tinf(9); end if n>=274 && n<=304 Tout=Tinf(10); end if n>=305 && n<=334 Tout=Tinf(11); end if n>=335 && n<=365 Tout=Tinf(12); end
% Calculate the heat load for the room if TroomAVGtotal==21 HeatEnergy=abs(((Cpair*ro*(21-Tout)*roomVolume)/time)/(COP-1)); %kW end if TroomAVGtotal<21 HeatEnergy=abs(((Cpair*ro*(TroomAVGtotal- Tout)*roomVolume)/time)/(COP-1)); %kW end if TroomAVGtotal>21 HeatEnergy=0; end
148 function HVACavgMonth=MyHVACavgmonth(HVACenergyDAY)
% This function is used to compute the HVAC energy per month % Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/2011 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % TroomAVGtotal
% Initialize Troom Average vector HVACavgMonth=zeros(12,1);
%Calculate Troom Monthly Values HVACavgMonth(1,1)=sum(HVACenergyDAY(1:31,1)); HVACavgMonth(2,1)=sum(HVACenergyDAY(32:59,1)); HVACavgMonth(3,1)=sum(HVACenergyDAY(60:90,1)); HVACavgMonth(4,1)=sum(HVACenergyDAY(91:120,1)); HVACavgMonth(5,1)=sum(HVACenergyDAY(121:151,1)); HVACavgMonth(6,1)=sum(HVACenergyDAY(152:181,1)); HVACavgMonth(7,1)=sum(HVACenergyDAY(182:212,1)); HVACavgMonth(8,1)=sum(HVACenergyDAY(213:243,1)); HVACavgMonth(9,1)=sum(HVACenergyDAY(244:273,1)); HVACavgMonth(10,1)=sum(HVACenergyDAY(274:304,1)); HVACavgMonth(11,1)=sum(HVACenergyDAY(305:334,1)); HVACavgMonth(12,1)=sum(HVACenergyDAY(335:365,1));
149 function Charge=MyIntegration(f,a,b,M)
% This function will use the Composite Simpson rule to integrate the % current as a function of timefor the charge calculation
% Input -f is the integrand input as a string. In this case the Current % -a and b are the upper and lower limits of the integration. In this % case time 0 to XXX in seconds % -M is the number of sub intervals. In this case the length of the % time vector % Output -Charge is the value of the charge that we recieve h=(b-a)/(2*M); s1=0; s2=0; for k=1:M x=a+h*(2*k-1); s1=s1+feval(f,x); end for k=1:(M-1) x=a+h*2*k; s2=s2+feval(f,x); end Charge=h*(feval(f,a)+feval(f,b)+4*s1+2*s2)/3;
150 function Troom=MyRoomTemperature(L1,L2,L3,Kg,Kair,Hair,Tinf,n,q)
% This function is used to compute the room temperature at the inner wall
% Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/211 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % L1=Outdoor glass length (m) % L2=Insulating gap length (m) % L3=Indoor glass length (m) % Kg=Thermal Conductivity of the glass (W/m*K) % Kair=Thermal Conductivity of air (W/m*k) % Hair=Heat transfer coefficient of free convection (W/m^2*K) % Tinf=Temperature of exterior weather (K)
% Constants: Tguess=50;
% Output: % Troom=Room Temperature (K) if n>=1 && n<=31 Tout=Tinf(1); end if n>=32 && n<=59 Tout=Tinf(2); end if n>=60 && n<=90 Tout=Tinf(3); end if n>=91 && n<=120 Tout=Tinf(4); end if n>=121 && n<=151 Tout=Tinf(5); end if n>=152 && n<=181 Tout=Tinf(6); end if n>=182 && n<=212 Tout=Tinf(7); end if n>=213 && n<=243 Tout=Tinf(8); end if n>=244 && n<=273 Tout=Tinf(9);
151 end if n>=274 && n<=304 Tout=Tinf(10); end if n>=305 && n<=334 Tout=Tinf(11); end if n>=335 && n<=365 Tout=Tinf(12); end qideal=(Tout-21)/(1/Hair+L1/Kg+L2/Kair+L3/Kg+1/Hair); if n>59 && n<243 Troom=Tout-(qideal-q)*(1/Hair+L1/Kg+L2/Kair+L3/Kg+1/Hair); else if q==0 Troom=21; else i=1; count=0;
while i>0.2 % Test stuff for y1 q1=(Tout-Tguess)/(1/Hair+L1/Kg+L2/Kair+L3/Kg+1/Hair); q2=q1+q; q2=abs(q2); Troom=(q2*(1/Hair+L1/Kg+L2/Kair+L3/Kg+1/Hair)+Tout); y1=Troom-Tguess;
%Test stuff for y2 Tguess2=Tguess*1.02; q1n=(Tout-Tguess)/(1/Hair+L1/Kg+L2/Kair+L3/Kg+1/Hair); q2n=q1n+q; q2n=abs(q2n); Troomn=(q2n*(1/Hair+L1/Kg+L2/Kair+L3/Kg+1/Hair)+Tout); y2=Troomn-Tguess2;
% Determine dy/dx dydx=(y2-y1)/0.02;
% Determine TemperaTure, T5 for State 5 Troom1= Tguess -(y1/dydx); i=abs(Tguess-Troom); Tguess=Troom1; count=count+1; end end end
152 function [Aon,Aoff,AMg]=MySolarflux(onData,offData,Astatic, dynamic,static)
% This function is used to compute the solar flux perpendicular of the % surface and independent of the angle.
% Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/1/11 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % onData= Optical transperancy of the ON state (nm,Fraction) % offData= Optical transperancy of the OFF state (nm,Fraction) % Dynamic= whether or not its a dynamic film
% Constants: % B=normal radiation constant (Unitless)
% Output: % Id=direct surface flux (W/m^2)
% Importing the AM 1.5 Spectra AM1_5g=xlsread('ASTMG173.xls','SMARTS2','C143:C1704'); AMlambda=xlsread('ASTMG173.xls','SMARTS2','A143:A1704');
% Integrate the Solar Spectrum
% Establishing a Curve Fitting for TOTAL Solar Spectrum [curve_fit,gof] = fit(AMlambda,AM1_5g,'cubicinterp');
% Integrating Total Spectra M=length(AMlambda); a=AMlambda(1); b=AMlambda(M); AMg=MyIntegration(curve_fit,a,b,M); %(W/m^2) if dynamic==1
% Calculate Tsolar for the Colored State
%Calculate Tsolar from range of 350-400 Tsolprod_ON(1,1)=AM1_5g(1)*onData(1,2); for j=1:48 Tsolprod_ON(j+1,1)=AM1_5g(j+2)*onData(j+1,2); end
%Calculate Tsolar from range of 400-1700 for j=50:1350
153 Tsolprod_ON(j,1)=onData(j+1,2)*AM1_5g(j+51); end
%Calculate Tsolar from range of 1705-2500 g=5; z=0; for j=1351:1510 g=g+z; z=4; Tsolprod_ON(j+1,1)=onData(j+g,2)*AM1_5g(j+52); end
% Make Wavelength Scale for k=1:1351 TsolLambda(k,1)=349+k; end for k=1403:1562 TsolLambda(k-51,1)=AMlambda(k); end
% Integrate Tsolar product as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TsolLambda,Tsolprod_ON,'cubicinterp');
% Integrating M=length(TsolLambda); a=TsolLambda(1); b=TsolLambda(M); Aon=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm)
% Calculating Tsolar for Bleached State
%Calculate Tsolar from range of 350-400 Tsolprod_OFF(1,1)=AM1_5g(1)*offData(1,2); for j=1:48 Tsolprod_OFF(j+1,1)=AM1_5g(j+2)*offData(j+1,2); end
%Calculate Tsolar from range of 400-1700 for j=50:1350 Tsolprod_OFF(j,1)=offData(j+1,2)*AM1_5g(j+51); end
%Calculate Tsolar from range of 1705-2500 g=5; z=0; for j=1351:1510 g=g+z; z=4; Tsolprod_OFF(j+1,1)=offData(j+g,2)*AM1_5g(j+52); end
% Make Wavelength Scale
154 for k=1:1351 TsolLambda(k,1)=349+k; end for k=1403:1562 TsolLambda(k-51,1)=AMlambda(k); end
% Integrate Tsolar product as a function of Lambda
% Establishing a Curve Fitting [curve_fit,gof] = fit(TsolLambda,Tsolprod_OFF,'cubicinterp');
% Integrating M=length(TsolLambda); a=TsolLambda(1); b=TsolLambda(M); Aoff=MyIntegration(curve_fit,a,b,M); %(W/m^2*nm) end if static==1; Aoff=0; Aon=Astatic*AMg; end if static==0 && dynamic==0 Aoff=0; Aon=0; end
155 function Id=MySolargain(gamma,e,t,n,l,long,Ut,A)
% This function is used to compute the solar gain as a function of solar % angles and glazing material
% Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 6/1/11 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % gamma=surface azimuth angle (degrees) % e=surface inclination (degrees) % n=number of days in the year (degrees) % t=solar time in the day (hours) % l=lattitude (Degrees) % A=surface flux constant (W/m^2)
% Constants: % B=normal radiation constant (Unitless)
% Output: % Id=direct surface flux (W/m^2)
% Define constants B=0.18; % unitless
% Compute hour angle (alpha), declination (beta), and Solar Zenith Angle(X) Ddeg=(360/365)*n; %Degrees EOT=-0.017188- 0.42811*cosd(Ddeg)+7.351415*sind(Ddeg)+3.34946*cosd(2*Ddeg)+9.36177*sin d(2*Ddeg); %minutes toffset=(EOT-4*long+60*Ut)/60; %hours ttrue=t+toffset; %hours alpha=(15*(ttrue-12)); % degrees beta=(23.44*sind(360*((n-80)/(365.25)))); % degrees X=acosd(sind(l)*sind(beta)+cosd(l)*cosd(beta)*cosd(alpha)); % degrees
% Compute Solar Azimuth Angle,zeta2 zeta=(sind(alpha))/(sind(l)*cosd(alpha)-cosd(l)*tand(beta)); % degrees if alpha>=0 && zeta>=0 zeta2=180 + atand(zeta); % degrees end if alpha>=0 && zeta<0 zeta2= 360 + atand(zeta); % degrees end if alpha<0 && zeta>=0 zeta2= atand(zeta); % degrees
156 end if alpha<0 && zeta<0 zeta2= 180 + atand(zeta); % degrees end
% Compute the direct normal radiation (Idn), Incident direct solar flux(Id) if X>90 Idn=0; else Idn=A*exp(-B/sind(90-X)); % W/m^2 end Id=Idn*(cosd(X)*cosd(e)+sind(e)*sind(X)*cosd(zeta2-gamma)); %W/m^2
157 function TroomAVGMonth=MyTroomAVGmonth(TroomDAY)
% This function is used to compute the room temperature per month % Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 06/2011 Guillermo Garcia Original Code % % Defined Variables % ======% Input: % TroomAVGtotal
% Initialize Troom Average vector TroomAVGMonth=zeros(12,1);
%Calculate Troom Monthly Values TroomAVGMonth(1,1)=mean(TroomDAY(1:31,1)); TroomAVGMonth(2,1)=mean(TroomDAY(32:59,1)); TroomAVGMonth(3,1)=mean(TroomDAY(60:90,1)); TroomAVGMonth(4,1)=mean(TroomDAY(91:120,1)); TroomAVGMonth(5,1)=mean(TroomDAY(121:151,1)); TroomAVGMonth(6,1)=mean(TroomDAY(152:181,1)); TroomAVGMonth(7,1)=mean(TroomDAY(182:212,1)); TroomAVGMonth(8,1)=mean(TroomDAY(213:243,1)); TroomAVGMonth(9,1)=mean(TroomDAY(244:273,1)); TroomAVGMonth(10,1)=mean(TroomDAY(274:304,1)); TroomAVGMonth(11,1)=mean(TroomDAY(305:334,1)); TroomAVGMonth(12,1)=mean(TroomDAY(335:365,1));
158 function p=MyUpperTri(U,b) % MyUpperTri solves upper triangular systems of n equtions Ux=b % Written by: Guillermo Garcia % Username: garciagu % % Record of Revitions % ======% Date Programer Description of Changes % 08/28/06 Guillermo Garcia Original Code % 08/29/06 Guillermo Garcia Added a test for UT system and Singularity % 08/29/06 Guillermo Garcia Added a test to check % 09/9/06 Guillermo Garcia Changed function variable % % Defined Variables % ======% Input: % U= n-by-n upper triangular matrix % b= n-vector % % Output: % x= solution vector of Ux=b
% Assign vector dimensions, establish case sequence, and initialize x- vector n=length(b);z=true;p=zeros(n,1);
% Test to make sure Matrix is upper triangle m=1; while m % Test for singularity of Matrix if det(U)==0 disp('Error: Matrix is singular ') z=false; end % Checks the case sequence to abort program or make computation if z==true; % solves the last unknown p(n)=b(n)/U(n,n); % solves the rest of the unknowns in the system x(n-1:1) for k=n-1:-1:1 sm=0; for j=k+1:n 159 sm=sm+U(k,j)*p(j); end p(k)=(b(k)-sm)/U(k,k); end else % Prompt Error message to User and End Program error('Must input Upper Triangle, non-singular matrix to complete computation now') end 160 Appendix C: Electrochromic Kinetics Code clear all; close all; % This script is being written to characterize switching times between the % colored and bleached state using a chronoamperometry technique. This is % also a method used to measure the charge that is being injected between % each state. % Input Parameter: % Area= Area of the sample that is being tested (cm^2) % Output Parameter: % Charge_Time= time to charge the sample from Bleached to Colored state (s) % Decharge_Time=time to charge the sample from Colored to Bleached state(s) % Charge=amount of charge measured to go from Bleached to Colored state(mC) % Decharge=amount of charge measured to go from Colored to Bleach state(mC) % Delta_Charge_Time= Charge time standard deveation (s) % Delta_Decharge_Time= Decharge time standard deveation (s) % Delta_Charge= Charge standard deviation (mC) % Delta_Decharge= Decharge standard deviation (mC) % Charge_Area=Charge per unit area (mC/cm^2) % Delta_charge_Area=Delta_charge per unit time (mC/cm^2) % Decharge_Area=Decharge per unit are (mC/cm^2) % Delta_Decharge_Area=Delta_Decharge per unit area (mC/cm^2) % Decharge_Time_Area=Decharge_Time per unit area (s/cm^2) % Delta_Decharge_Time_Area=Delta_Decharge_Time per unit area (s/cm^2) % Charge_Time_Area=Charge_Time per unit area (s/cm^2) % Delta_Charge_Time_Area=Delta_Charge_Time per unit area (s/cm^2) %% INPUT PARAMTERS % Assign the area of the sample tested Area=2.16; %cm^2 % Import Chronoamperometry data ChronoampData=dlmread('5 Cycles 2V to 4V 0.1M LiClO4 1.2cmx1.8cm.mpt','\t',54,0); %% Calculate Charge/decharge Time % Assign vectors to time and charge time=ChronoampData(:,7); %(s) current=ChronoampData(:,10); %(mA) voltage=ChronoampData(:,8); % (V) 161 % Calculate the time for both charge and discharge count=voltage(1); count1=1; count2=1; count3=0; for j=1:length(time) if voltage(j)==count count3=count3+1; Charge_current_vec(count3,count2)=current(j); Charge_current_time_vec(count3,count2)=time(j); else count2=count2+1; count3=0; Time_vec(count1)=time(j); count1=count1+1; count=voltage(j); end end Time_vec(count1)=time(length(time)); % Calculate time for discharge for j=1:length(Time_vec)/2 k=j*2; Decharge_time_vec(j)=Time_vec(k)-Time_vec(k-1); Charge_time_vec(j)=Time_vec(k+1)-Time_vec(k); end Decharge_Time=mean(Decharge_time_vec); %(s) Delta_Decharge_Time=std(Decharge_time_vec); %(s) Charge_Time=mean(Charge_time_vec); %(s) Delta_Charge_Time=std(Charge_time_vec); %(s) %% Calculate the Charge/Decharge count=1; count1=1; k=1; for j=1:length(Time_vec)-1 if k==j xdata=Charge_current_time_vec(:,j+1); ydata=Charge_current_vec(:,j+1); count4=0; for h=1:length(xdata) if xdata(h)==0 else count4=count4+1; end end xdatan=zeros(count4,1); ydatan=zeros(count4,1); 162 for h=1:count4 xdatan(h)=xdata(h); ydatan(h)=ydata(h); end %Fit Curve [curve_fit,gof] = fit(xdatan,ydatan,'cubicinterp'); [curve_fit_exp,gof1,output] = fit(xdatan,ydatan,'exp1'); %Plot Exponential fit figure ('name','Bleaching Charge Profile','NumberTitle','off') plot(curve_fit_exp,'x',xdatan,ydatan,'r') xlabel('time (s)') ylabel('Charge(mA)') legend('Data','Fitting') % Calculate Half Life times b1=confint(curve_fit_exp,0.99); %1/s bavg1=(b1(3)+b1(4))/2; %1/s t_half1=(1/bavg1)*log(2); %s % Integrating Total Spectra M=length(xdatan); a=xdatan(1); b=xdatan(M); Decharge_int_vec(count)=MyIntegration(curve_fit,a,b,M); %(mC) thalf_decharge_vec(count)=t_half1; k=0; count=count+1; else xdata1=Charge_current_time_vec(:,j+1); ydata1=Charge_current_vec(:,j+1); count5=0; for h=1:length(xdata1) if xdata1(h)==0 else count5=count5+1; end end xdatan1=zeros(count5,1); ydatan1=zeros(count5,1); for h=1:count5 xdatan1(h)=xdata1(h); ydatan1(h)=ydata1(h); end %Fit Curve [curve_fit,gof] = fit(xdatan1,ydatan1,'cubicinterp'); [curve_fit_exp1,gof1,output1] = fit(xdatan1,ydatan1,'exp1'); %Plot Exponential fit figure ('name','Coloring Charge Profile','NumberTitle','off') 163 plot(curve_fit_exp1,'x',xdatan1,ydatan1,'r') xlabel('time (s)') ylabel('Charge(mA)') legend('Data','Fitting') % Calculate Half Life times b2=confint(curve_fit_exp1,0.99); %1/s bavg2=(b2(3)+b2(4))/2; %1/s t_half2=(1/bavg2)*log(2); %s % Integrating Total Spectra M=length(xdatan1); a=xdatan1(1); b=xdatan1(M); Charge_int_vec(count1)=MyIntegration(curve_fit,a,b,M); %(mC) thalf_charge_vec(count1)=t_half2; count1=count1+1; k=j+1; end end Charge=mean(Charge_int_vec); %mC Delta_charge=std(Charge_int_vec); %mC Decharge=mean(Decharge_int_vec); %mC Delta_Decharge=std(Decharge_int_vec); %mC Thalf_Decharge=mean(thalf_decharge_vec); %s Delta_Thalf_decharge=std(thalf_decharge_vec); %s Thalf_Charge=mean(thalf_charge_vec); %s Delta_Thalf_charge=std(thalf_charge_vec); %s %% Values Per unit Area Charge_Area=Charge/Area; %mC/cm^2 Delta_charge_Area=Delta_charge/Area; %mC/cm^2 Decharge_Area=Decharge/Area; %mC/cm^2 Delta_Decharge_Area=Delta_Decharge/Area; %mC/cm^2 Decharge_Time_Area=Decharge_Time/Area; %(s/cm^2) Delta_Decharge_Time_Area=Delta_Decharge_Time/Area; %(s/cm^2) Charge_Time_Area=Charge_Time/Area; %(s/cm^2) Delta_Charge_Time_Area=Delta_Charge_Time/Area; %(s/cm^2) %% Summary Summary=dataset(Decharge_Time,Delta_Decharge_Time,Charge_Time,Delta_Cha rge_Time,Thalf_Decharge,Delta_Thalf_decharge,Thalf_Charge,Delta_Thalf_c harge,Charge,Delta_charge,Decharge,Delta_Decharge,Charge_Area,Delta_cha rge_Area,Decharge_Area,Delta_Decharge_Area,Decharge_Time_Area,Delta_Dec harge_Time_Area,Charge_Time_Area,Delta_Charge_Time_Area) export(Summary,'file','i60 0.1M Li on glass 1.2cmx 1.8cm summary.txt') 164