Signature Redacted Signature of Author

Chemical and Biomedical Sensors Using Two Dimensional Materials

by Mantian Xue

B.S. Material Science and Engineering University of Illinois at Urbana-Champaign, 2017

Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of

Master of Science in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Department of Electrical Engineering and Computer Science Signature redacted August 30, 2019 Certified by: ......

Tomis Palacios Professor of Electrical Engineering and Computer Science

1) Thesis Supervisor

Certified by: ...... Signatureredacted.... MASSACHUSETTS INSTITUTE / of rECHNOLOGY > Leslie A. Kolodziejski F;- Engineering and Computer Science OCTOC0301 0 32019 Professor of Electrical Chair, Department Committee on Graduate Students LIBRARIES"

Two Dimensional Materials Based Sensors for Chemical and Biological

Applications by Mantian Xue

Submitted to the Department of Electrical Engineering and Computer Science on August 30, 2019, in Partial Fulfillment of the Requirements for the Degree of Masters of Science in Electrical Engineering and Computer Science

Abstract

We are at the onset of a revolution in chemical and medical sensors. Traditional sensors are bulky and difficult to use. Many researchers have started to build easy-to-use in-home healthcare monitoring system such as wearable sweat sensors. In order to make such system practical, sensors need to combine high sensitivity, high selectivity, fast response time and small signal drift. The sensors also need to cover a wide range of rec- ognizable chemicals and molecules. Two-dimensional materials are perfect candidate as next-generation sensing materials because of their unique electrical, optical, mechanical and chemical properties. In this thesis, the fabrication and device technology of state- of-the-art graphene-based chemical sensor was discussed. A new 2D materials patterning technology and various passivation approaches were also studied. By using these novel technologies, three types of sensing devices that aims to push the development of better healthcare monitoring system were developed. A graphene-based biosensor for ligand detection was made with high sensitivity and a wide span of detection range. Graphene sensor arrays coupled with various types of ion-selective membranes were also developed. High sensitivity, selectivity and reversibility were achieve for detection of ionized calcium, sodium and potassium in electrolyte. FinallyMoS 2 were explored to amplify the signal and achieve high sensitivity at low concentration as well as an easier measurement scheme. All three sensors will serve as building blocks for the realization of next-generation chemical and biomedical sensor systems.

Thesis Supervisor: Tomas Palacios Title: Professor of Electrical Engineering

3 Contents

1 Introduction 11 1.1 Project Motivation ...... 11 1.2 Introduction to Two Dimensional Materials ... .. 12 1.2.1 Introduction to Graphene ...... 1 13

1.2.2 Introduction toMoS 2 ...... 16 1.3 Thesis Outline ...... 19

2 Device Technology 21

2.1 Cleanness of film ...... 21

2.2 Passivation ...... 25 2.3 Conclusion ...... 34

3 Graphene-based Biosensors 36 3.1 Electrolyte-gated graphene field-effect transistors 36 3.2 pH sensing ...... 37 3.3 Ligand Detection with GPCR . .. . 40 3.3.1 Device Structure ...... 42 3.3.2 Sensor Response ...... 43 3.4 Conclusion ...... 46

4 Graphene-based Ion Sensing 47 4.1 Theory of Ion Selective Membrane ... . 47 4.2 Sensor Array Structure and Performance 50 4.3 Ca2+ ion sensor ...... 53 4.4 Na+ ion sensor ...... 56 4.5 K+ ion sensor ...... 58

4 4.6 Integration of multiple ion sensor ...... 60 4.7 C onclusion ...... 62

5 MoS 2-based Sensors 64 5.1 Device Structure ...... 64 5.2 pH Sensing Mechanism ...... 65 5.3 Sensor Performance ...... 68 5.4 C onclusion ...... 69

6 Conclusion and Future Work 70

Bibliography 72

Appendices 83

A Standard Photolithography Recipes 83

B Recipe for graphene-based pH sensor 85

C Recipe for graphene-based ligand sensor 87

D Recipe for graphene-based sensor array 90

E Recipe for back-gated MoS 2 device 93

5 List of Figures

1.1 The family of 2D materials. Figure adopted from [1] ...... 13

1.2 (a)The carbon atomic a and 7r orbitals in thesp 2 honeycomb lattice [14] (b) Electronic dispersion in the honeycomb lattice. Left: energy spectrum. Right: zoom in of the energy bands close to one of the Dirac points [13]. (c)Ambipolar electric field effect in single-layer graphene. The insets show its conical low-energy spectrum [2] ...... 15

1.3 (A) Atomic structure for single layer transition metal dichalcogenides (TMDs) in the 2H, 1T, andIT' phases, (B) periodic table of elements involved lay-

ered TMDs, (C) evolution of the band structure for 2H-MoS 2 with decreasing number of layers, and (D) the schematic representation of the band

structure for 2H-MoS 2 . This TMD overview image is reproduced from M anzeli et al. [46] ...... 17

2.1 Schematics for PMGI/SPR700 bilayer Patterning Process. Green film represents PMGI and orange film represents SPR700 ...... 23

2.2 AFM Images of graphene films on SiO 2 substrate with different photoresist treatm ents ...... 24

2.3 Effect of NMP Treatment with PMMA Processed Graphene film onSiO 2 substrate. (a) PMMA processed graphene, (b) PMMA processed graphene after NMP overnight,(c)microscrope image of patterned graphene film after NM P treatm ent ...... 25

2.4 AFM images of ALD dielectric on (a)graphene and (b) MoS 2 . Figures adopted from [76] and [78] ...... 26

2.5 AFM images of ALD A12 0 3 on (a)graphene and (b)MoS 2 surface with Al as the seeding layer ...... 28

6 2.6 (a)Hysteresis of electrolyte-gated graphene FETs without A1 2 0 3 passiva-

tion (b)hysteresis of electrolyte-gated graphene FETs with A1 2 0 3 passivation 29

2.7 Effect of A1203 passivation on electrolyte-gated graphene FETs' I-V characteristic ...... 30

2.8 Sensor response and structure of device (a) SU-8 passivation and (b) with oxide passivation ...... 32

2.9 I-V Characteristics of MoS 2 Back-gated Transistors.(a) Output characteristics without oxide, (b)output characteristics with oixde, (c) transfer characteristics without oxide, (d) transfer characterstics with oxide .... 33

2.10 Transfer characteristics of MoS 2 based transistors with different types of passivation. Figure adopted from Yu et al. [78]...... 34

3.1 (a)Device structure and measurement setup for electrolyte-gated graphene field-effect transistors. (b)Three most common models to describe electric double layers, figure taken from [36] ...... 37

3.2 Change in Dirac point with respect to pH value ...... 39

3.3 Schematics of 2D lattice of GPCR/S-layer complex. Images taken from R ui Q ing, PhD ...... 41

3.4 AFM images to show surface morphology of S-layer and GPCR/S-layer complex on silicon substrate. Images taken from Rui Qing,PhD...... 41

3.5 (a)Mask file of the graphene-based ligand sensor with GPCR (b) Measure- ment setup and fluid chamber ...... 42

3.6 I-V response and schematics upon exposure of CXCL12 ligands with (a) bare graphene, (b) S-layer and (c) GPCR/S-layer complex. Ids is normalized to its minimum value. Black stars represent ligand, green lines represent S-layer, orange circles with black stars represent ligand bind with G PCR protein...... 44

3.7 Sensor response with respect to ligand concentration demonstrates good linear relation between C/S and C (R2 = 0.992). S is defined as relative change in Dirac point...... 46

7 4.1 (a) Graphene Ca 2+ sensor diagram depicting measurement setup and equilibrium charge distribution. R- represents lipophilic anionic site. (b) schematic diagram showing the electrostatic potential as a function of distance from graphene surface. The dash line indicates the potential distri-

bution when zero Ca2+ concentration gradient is present between ISM and electrolyte. (c) idealized graphene Ca2+ sensor I-V characteristic response. 48

4.2 Mask desgin for graphene-based sensor array. Top-right is a zoom-in picture of the sensing area. Insert shows the mask of an individual graphene sensor. Bottom-right is a microscope image of a graphene sensor on the array chip

after fabrication ...... 51

4.3 (a) I-V characteristics of 244 working electrolyte-gated graphene transistors on one chip with Vd, = 300 mV.1uM NaCl solution was used as the electrolyte. (b)distribution of the Dirac points ...... 52

4.4 ((a) Shift if I-V characteristic of a calcium sensor under different concentration, (b)slope of the average Dirac point as a function of ionized calcium concentration, error bar indicates the standard deviation. The sample size is 196 and all measurements are taken at Vd, = 300 mV...... 54

4.5 Ca2+ sensor behaviors at different period of time. All measurements were taken using the same chip under same bias V,, = - 0.4 to 0.9V, Vd, = 300 m V ...... 55

4.6 Shift of average Dirac point as a function of ionized sodium concentration, error bar indicates the standard deviation. The sample size is 213 and all measurements are taken at Vd, = 300 mV...... 56

4.7 Graphene Na+ sensor conductance transient response to changing concentrations in ionized sodium. The sample size is 213 and the bias conditions are VdS=300 mV, VGS=-0.l V. Data is normalized with respect to the response at 1 mM of each sensor...... 57

8 4.8 (a)Mean sensitivity response showing excellent sensitivity and reversibility over several orders of magnitude change in ionized sodium concentration. The percentage change in conductance is normalized with respect to the data taken at 1 mM and the error bars are given by the standard deviation. (b) Histogram shows reproducibility distribution of 213 individual devices. X-axis is the percentage difference between the fitted slope of increasing sodium concentration and that of decreasing sodium concentration. .... 58 4.9 Graphene K+ sensor response with change in potassium ion concentration.

The sample size is 193 and the bias conditions are Va=300 mV ...... 59 4.10 (a)The non-linearity in change in conductance with different potassium concentration for K+ sensor array. (b)I- V characteristics for the K+ sensors

under 1 M KCl solution. The black dash-line indicates the slice of Id, at

V gs = 0V ...... 60 4.11 Sensor response with change in sodium concentration. Black line is for sensors with calcium ISM and red line is for sensors with sodium ISM. The difference in slope indicates good selectivity ...... 61 4.12 (a) Printed ISM on graphene sensor array chip. (b) Proposed chip design for multiplexed sensor array...... 62

5.1 Device structure and pH sensing mechanism forMoS 2-based pH sensor .. 65

5.2 MoS 2 Transfer characterstic ...... 66

5.3 MoS 2 1BFET for pH sensing with (a)10nm A1 2 03 and (b) 30nm A12 03 .. . 69

9 List of Tables

2.1 Surface roughness of graphene film after different treatment ...... 24

4.1 Performance comparison of Ca2+ sensors based ion-selective membranes .. 54 4.2 Stability of Ca2+ sensor ...... 55 4.3 Summary of Ion Sensors Performance ...... 60

10 Chapter 1

Introduction

1.1 Project Motivation

Traditionally, the information regarding ones health would need to be gathered in test centers through either non-invasive or invasive tests such as X-ray and blood analysis.

However, running such tests are expensive and time-consuming. Recently there has been significant work to develop more cost-efficient and easy-to-use health monitoring systems to replace or complement the traditional tests. These systems normally consist of a set of chemical and biological sensors, connected to a data acquisition and data processing element. With such system, ones can collect crucial information regarding their health in real time. This is important because the trend in healthcare is shifting toward active disease prevention instead of passive [29, 45]. There is a great interest on recognition of body vital signs and biomarker for such early disease diagnosis. This includes prevention of disease before it occurs and controlling progression of disease that already happened

[35, 21]. By having an in-home healthcare monitor system with sensors dedicated to specific biomarkers, measurements can be taken more frequently thus providing better chance for early diagnosis.

In addition, personalization of medical information can also be important for effective early diagnosis. Each individual has a unique profile regarding health status [29]. With the data collected by in-home monitoring system, a specific baseline could be generated for each individual in terms of human physiological values and important biomarkers.

11 Through continuous monitoring it is thus easier to detect abnormal signals and improve the efficiency and accuracy of early diagnosis [29, 45]. It would also help to better identity treatment needs and provide data on the body's response to a treatment thus helping to evaluate the effectiveness of the treatment, leading to improvement of ones well-being.

An important aspect of building a practical in-home easy-to-use healthcare monitoring system is to have high-performance chemical and biological sensors. These sensors need to have high accuracy, high selectivity, fast response time and high stability over time.

Two-dimensional (2D) materials are particularly promising candidates for chemical and biological sensing applications due to their high surface-to-volume ratio. This allows a large portion of their bulk material properties to be modulated in response to chemical changes occurring at or near the surface, generally leading to enhanced sensitivity. The mechanical flexibility of 2D materials is another advantage for applications like flexible and wearable electronics. Some 2D materials also have unique optical and mechanical properties that can be used for optical or strain sensors.

This Master's thesis aims to develop next-generation chemical and biological sensors by utilizing the unique properties of 2D materials. In this project, a novel ligand sensor is developed using graphene as the signal transducer. To reduce device variation and achieve higher accuracy, a sensor array is also developed using graphene and ion-selective membranes to monitor the balance of important electrolyte in body fluid. Furthermore,

MoS 2 is explored as an alternative sensing material to achieve easier measurement schemes and better amplified the electrical signal. By demonstrating highly sensitive, selective and accurate sensors, this project takes one step further in the process of developing the next- generation health monitoring system.

1.2 Introduction to Two Dimensional Materials

This section introduces select 2D materials that exhibit high potential in chemical and bio-sensing application. Shown in Figure 1.1 is a summary of the 2D materials family categorized according to their electrical properties. For sensing application, the detection of analytes is achieved by monitoring the electrical signal change in the channel material.

12 2D insulators such as h-BN are not appropriate for such application. Graphene and MoS 2 are the representative materials for 2D semi-metals and 2D semiconductors, which will be focused in section below.

Insulator h-BN or -0000-

BP family Graphenefamily HI-VJ family TMD(MX,)family

2',,WSe,SflS,42 ,S=

Figure 1.1: The family of 2D materials. Figure adopted from [1]

1.2.1 Introduction to Graphene

Graphene, a single layer of graphite, is the most studied and mature semi-metal material in the 2D family. The first successful isolation of graphene film was obtained in An- dre Geim's group in 2004 by mechanical exfoliation of highly oriented pyrolitic graphite

(HOPG) [49]. Graphene consists of a 2D plane of carbon atoms arranged in a hexagonal lattice. The carbon atoms are connected with one ir-bond and three a-bond via sp 2 hybridization. The in-plane a-bonds provide mechanical strength in graphene and their electrons are localized. The ir-bonds are delocalized and the charge transport within in ir-bonds largely determines the electrical property of graphene. The ir-bonds can be influenced by environmental changes and the electrical proprieties in graphene will then be altered. In this way, chemical signals can be transformed into electrical signals in graphene. Many researches have demonstrated graphene's innate sensitivity towards a variety of gas molecules [58, 62, 26].

13 Graphene's excellent electrical properties find their origin in its pronounced ambipolar electric field effect. Charge carriers can be tuned between electrons and holes continuously with the carrier concentration as high as 1013 cm-2 under ambient conditions [50, 51, 2].

The mobility of graphene at room temperature can exceed 15,000 cm 2 /Vs [2]. Moreover, research has shown that the mobility of graphene shows weak dependence on temperature.

This means mobility is still limited by impurity scattering at room temperature, and by increasing the distance to these impurities the mobility can be increased up to 100,000 cm 2 /Vs [2]. The ballistic transport on sub-micron scale in graphene makes the mobility remains high even at high carrier concentration (>101 2 cm- 2 ) in doped graphene [2]. Even though graphene mobility in practical layers are typically reported in the range of 500

2 - 10,000 cm /Vs, it is still better than that most conventional materials such as silicon.

High mobility in graphene makes it more responsive to environmental changes and thus increase the sensor sensitivity. It also provides better frequency response and higher signal-to-noise ratio in chemical sensors [15, 62, 56].

Graphene also has a unique band structure and charge carriers. Different from traditional materials whose electronic properties can be described through the Schrodinger equation, the charge transport in graphene is better described by the relativistic Dirac equation [2]. Shown in Figure 1.2 (b) is the low-energy band structure of graphene. The conical-shape of the band structure makes graphene a zero-band gap or semi-metal material. The charge neutrality point, where the conduction band and valence band meets, is referred as the Dirac point (DP). The Dirac equation is a direct consequence of graphene's unique crystal lattice. The periodic potential of the honeycomb lattice gives rise to quasiparticles, called massless Dirac fermions, at low energy state. These quasiparticles have an effective speed of VF - 106 m/s, which is roughly 1/300 of speed of light [2]. The large value of VF allow the charge carries in graphene to move at a high speed, which can be advantageous for high sensitive and fast response time.

In additional to its electrical properties, the mechanical capabilities of graphene also make this material a strong candidate in next-generation sensing. The Young's modulus for graphene can be close to 1 TPa [70]. The high mechanical strength and high flexibility

14 6 1K t 46 #

4 2;

(a) (b) (c)

Figure 1.2: (a)The carbon atomic a and ir orbitals in the sp2 honeycomb lattice [14] (b) Electronic dispersion in the honeycomb lattice. Left: energy spectrum. Right: zoom in of the energy bands close to one of the Dirac points [13]. (c)Ambipolar electric field effect in single-layer graphene. The insets show its conical low-energy spectrum [2]

of graphene make it ideal for flexible and wearable electronics. It is also promising for

biomedical implants since it is able to accommodate on the surrounding biological tissue

without experiencing stress or fatigue [58]. In addition, graphene's sp2 carbon surface

allow strong but non-destructive interactions at cellular level [58]. The high chemical

stability of graphene, also makes it intrinsically biocompatible, which is very important

for biomedical devices.

A critical step in developing high-performance graphene-based sensor device is to

be able to synthesize graphene films with high quality and repeatability. Mechanical

exfoliation from Highly oriented pyrolytic graphite (HOPG) has been the simplest method

to obtain graphene since 2004 [49]. This method is easy and low-cost thus is perfect

for quick demonstration of ideas. However, graphene flakes obtained form mechanical

exfoliation usually have irregular shapes and orientation. The size of flakes is typically limited to a few microns. Another approach to synthesize graphene is to anneal hexagonal

SiC crystal. At high temperature, the top layers of SiC crystal decompose in to Si and

C atoms. The Si atoms desorb and the carbon atoms remaining on the surface rearrange

and re-bond to form epitaxial graphene layers [5, 7]. Graphene formation on SiC has

fast kinetics, which makes it difficult to control the growth thickness. The resulting film

is usually multilayered and not uniform across the wafer [5]. Chemical vapor deposition

(CVD) is the most commonly used technique to synthesis high-quality, large-area graphene

films. In general, CVD process involves activation of carbon-containing precursors to

15 desired vaporous phase, transport of reacting gaseous species and heterogeneous surface reaction on the substrate [65]. Depending on the growth substrate and growth condition, carbon atoms can directly form a graphene layer on the surface of the substrate or dissolve inside the substrate and then precipitate to the surface forming a graphite film [65].

CVD graphene exhibits outstanding electrical and mechanical proprieties similar to the exfoliated graphene flakes [5]. Large-area, high quality CVD graphene grown on copper film is the most commonly used for graphene-based devices in literature. Graphene film can be transferred onto various substrates by standard wet transfer techniques [68, 65].

1.2.2 Introduction to MoS 2

The second category of materials are 2D semiconductors, and more specifically transition metal dichalcogenides (TMDs). Bulk TMDs have been studied for decades and possess a variety of compositions [16]. Like graphene, bulk TMDs are layered materials with strong in-plane bonds and weak out-of-plane van der Waals bonds. The first discovery of layered TMDs was by Linus Pauling in 1923 [54] and the first production of monolayer

MoS 2 suspensions were performed by Per Joensen in 1986 [31]. TMD materials consist of a transition metal layer, typically from groups IV-VII, sandwiched between chalcogenide layers. Most TMDs have an atomic ratio of 1:2 with chemical formulaMX 2 , where M is a transition metal (e.g. Mo, W) and X is chalcogenide (e.g. S, Se, Te) [46, 17, 21].

Some special cases include 2:3 quintuple layers (M 2 X3 ) and 1:1 metal chalcogenides (MX) [85, 83, 17].

Depending on the different coordination of the metal atoms, 2D TMDs exhibit poly- typic structures including trigonal prismatic (2H phase), octahedral (iT phase) and distorted octahedral (iT' phase) [19]. iT and IT' phase are metastable and tend to aggre- gate and transform into the more thermodynamically stable 2H phase [19, 67]. iT and

IT' phase show metallic behavior due degenerated d.,y2,x2 orbitals, which form a single, partially-filled band. With the trigonal prismatic structure, a bandgap is present between the filled d22 band and empty dX2_ 2,XY band thus making the 2H phase semiconducting

[9]. Figure 1.3 shows the band structure evolution calculated by density functional theory

16 A C Btik 4ilavr layers MoroIdw, 2H IT IT'

A Transitlonmetal dclone(T s in M K I nd K paI K I prdI K I B D IV V

Vf AK X 1 MH SIT1 K' k ~ dls Distorted

I Insulating CDW Charge density wae SC Superconductivm

K K'

Figure 1.3: (A) Atomic structure for single layer transitionmetal dichalcogenides (TMDs) in the 2H,i1T,andT'phases, (B)periodictabfleo lments involved layered TMDs, (C) evolution of the band structure for 2H-MoS 2 with decreasing number of layers, and (D) the schematic representation of the band structure for 2H-MoS 2 . This TMD overview image is reproduced from Manzeli et al. [46]

of 2HMoS 2 from a bulk indirect bandgap semiconductor to a monolayer direct bandgap

semiconductor [46]. The bandgaps of 2D TMDs span over large range, including the entire

visible spectrum and near infrared [65]. For monolayerMoS 2 , the experimental value for

its bandgap is 2.16eV [23]. Unlike graphene, the natural bandgap inMoS 2 can be used

to provide higher on/off ratios (i.e. switching behavior), greater signal amplification, and

subthreshold operation. The ability to switch TMDs into a non-conductive state enables

the integration of 2D digital electronics alongside the 2D sensors all using the same material. Basic logic gates, the basis for more complex digital electronics, have already been

demonstrated using several 2D TMDs [73, 79, 80, 81]. This in turn may provide better

scalability in terms of array design since individual sensors can be turned on and off for

readout through access transistors. Operation in the subthreshold regime can also lead to

enhance sensitivity through the conventional exponential dependence of current on gate

voltage in field-effect transistors (FETs) [48].

In addition to providing a natural bandgap, most 2D TMDs also exhibit high car-

rier mobility. The theoretical mobility of monolayerMoS 2 at room temperature ranges

from 10 to 1,000 cm2 V-is~1 [39, 32]. In practice, however, the mobility of 2D TMDs

17 is strongly dependent on the cleanliness and maturity of the fabrication process as well as environmental factors such as surface absorbents and defects in surrounding dialectic

[46]. In order to mitigate mobility degradation, high temperature annealing in vacuum is often used to help remove surface absorbents and high-k dielectric encapsulation (e.g.

Hf0 2 )may be used to screen Coulomb scattering [81, 57]. Through dielectric environment engineering, the experimental mobility of monolayerMoS 2 at room temperature has been reported close to 150 cm2 V-8-1 [81].

MoS 2 also exhibit excellent mechanical properties making them potential candidates for flexible, wearable sensor systems [53]. The Young's modulus of few layer, freely sus- pendedMoS 2 nanosheets have been reported as high as 0.33 ± 0.07 TPa [12]. Bertolazzi et al. reported high in-plane stiffness and Young's modulus for single-layerMoS 2 of 180 i 60

1 Nm- and - 270 + 100 GPa, respectively [12]. The high strengthof MoS 2 can withstand strains up to 10% [8]. Calculations further suggest that tensile strains in semiconducting

TMDs can be used to tune the band structure, charge carrier effective masses, thermal conductivity, and other properties [46]. The piezoresistive coefficient for monolayer and a few layerMoS 2 is also two orders of magnitude higher than that of graphene. With a much higher fracture strain (~ 11%) than silicon (~ 0.7%) and comparable piezoresistive

coefficient, MoS 2 and other 2D TMDs are the exemplary candidates for the development of flexible sensors for non-planar surfaces and highly distorted objects such as biological tissue [46].

Similar to graphene, most 2D TMDs can be mechanically peeled off from their layered bulk crystals with good electrical, mechanical, and optical properties. This method, however, is not scalable or reliable for large-area sensor array fabrication. Some more scalable and practical approaches are molecular beam epitaxy (MBE), chemical vapor deposition (CVD), and metal-organic CVD (MOCVD). MBE generally requires the use of ultrahigh vacuum where molecular beams of the source material are deposited onto a heated substrate while carefully monitoring film thickness through electron diffraction

[46, 3]. The quality of MBE films is highly dependent on the underlying substrate, with poor lattice matching resulting in polycrystalline films with high dislocation and defect

18 densities [46]. CVD TMDs are most commonly synthesized by evaporating metal oxide and chalcogen precursors, which undergo a two-step chemical reaction that results in the formation of a stable TMD film on the surface of a substrate. This method does not require ultrahigh vacuum and tends to be more economical in achieving large-area synthesis of 2D TMDs [17, 651. Point defects and multilayer sites, however, are commonly present in CVD-grown films and often result in a carrier mobility below tens of cm2 V-1 s-' [32].

MOCVD uses gaseous metal-organic or organic sources in which target atoms along with complex organic molecules are flowed into a chamber through mass flow controllers at a precise ratio. While the molecules decompose inside the chamber, target atoms may be deposited onto the substrate atom by atom [30, 33]. MOCVD can provide atomic scale deposition with morphological homogeneity of domain sizes and thicknesses but at a relatively slow growth rate and higher production cost [65, 17]. Overall the synthesis technology of large-areaMoS 2 with high yield, consistent electrical properties, and uniformity is still relatively immature. When compared to their mechanically exfoliated counterparts, most synthesized films have lower carrier mobility, substantial doping, and higher inhomogeneity in film thickness. This may lead to sensors with reduced sensitivity, reproducibility, and reliability. In addition, the growth of MoS 2 , typically makes use of a seeding layer such as perylene-3,4,9,10-tetracarboxylic acid tetrapotassium salt (PTAS)

[42]. This results in some synthesized TMDs being incompatible with water, a critical solvent in both microfabrication processes and electrolytic sensing environments. This motivates advances in MoS 2 synthesis without the use of water soluble seeds. It is also worth noting that unlike graphene, TMDs tend to have poor Ohmic contacts. Advances in this area have been made through the use of phase-change contacts, which slightly complicate fabrication processes relative to graphene [18, 52].

1.3 Thesis Outline

This thesis aim to develop novel chemical and biological sensors for next-generation healthcare monitoring system. 2D materials like graphene and MoS 2 can provide unique direc- tion for sensor development and optimization. To achieve this goal, device technology,

19 fabrication processes, surface functionalization and sensing mechanisms are studied and co-optimized.

Chapter 2 focus on the device technology for 2D materials fabrication. The importance and methods of keeping the 2D materials free from residues are discussed. A new patterning technique using standard photolithography resists is introduced here. We also studies various methods to passivate of 2D materials in order to reduce hysteresis, protect the film form contamination as well as preserve the sensitivity of the film.

Chapter 3 presents graphene-based sensors for biological application. In particular, the graphene-based electrolyte-gated FETs are presented as a effective platform for sensing applications in liquid phase. pH value, as an important parameter for biological environment, is chosen as the first sensing target. The intrinsic sensitivity of graphene towards pH will be studied and analyzed here. In addition, a ligand sensor with graphene as the signal transducer and protein as receptors is developed in this chapter. The sensor behavior and sensing mechanism are also discussed.

In chapter 4, we scale up from individual sensors to large-area sensor arrays. The array structure and array performance are discussed and analyzed. And ion selective membrane is used to functionalize the graphene arrays to provide sensitivity and selectivity. The operation principle and sensing mechanism are studied in detail. We experimented with three different ion selective membranes and the resulting sensor arrays all exhibit excellent performance. All three membranes were integrated on a single array by using 3D printer.

A new chip design is proposed to achieve easy-to-use and high performance multiplex ion sensor array.

Chapter 5 discussed the possibility of usingMoS 2 as the channel material for sensors with highly amplified signals. A pH sensor is demonstrated using a back-gated MoS 2 FETs. The sensor performance is studied in order to optimize the device structure. We also discussed how to further utilize the unique properties of semiconducting 2D materials and compared the graphene-based sensor devices withMoS 2 based devices.

In chapter 6, we summarize and highlight the results in this thesis. A future outlook of chemical and biological sensing application using 2D materials are provided.

20 Chapter 2

Device Technology

This chapter will focus on the essential device technology for sensor fabrication and design.

All the sensors are made in a cleanroom using microfabrication technology in order to achieve small feature size and push for mass production. We will first talk about how to maintain the cleanness of the 2D films during the fabrication process. The second section will focus on device passivation of graphene andMoS 2 devices using oxides and/or polymers.

2.1 Cleanness of film

Graphene has attracted much attention due to its exceptional properties such as high carrier mobility ranging from 50,000 - 200,000 cm2V-Is-1 [10, 24]. However, these value typically only exist under extreme conditions, such as low temperature and specialized substrates. A more realistic mobility for fabricated graphene devices is between 5000 to

10,000 cm2V-Is- 1 [ 66]. Surface contamination is one of the major factors that can hinder graphene quality and degrade device performance. The source of surface contamination can be from the high-temperature CVD growth [41], transfer process [40, 66] as well as fabrication process. This section will be focusing on maintaining the cleanness of the film during fabrication.

Most of the graphene devices in literature are patterned by E-beam lithography (EBL) using E-beam resist like Poly(methyl methacrylate) (PMMA) and methyl methacrylate

21 (MMA) to keep the surface as clean as possible. However, EBL process is expensive and slow. To make the graphene technology more practical, it is highly desired to develop a cheaper process while still keeping the film away from residues. There has been some previous attempt to use bi-layer patterning process with an sacrificial film (MMA) and standard photolithography resist[44]. With same area of exposure, EBL would need 20 minutes to even one hour, depending on different dose and bean current. Using a standard photolithography tool such as mask aligner, it would only take a couple seconds.

Mackin et al. was able to fabricate graphene-based devices with much lower cost and less time by switching from EBL to photolithography. However, they fail to'evaluate the graphene film quality after such process. Here a similar process is used with Polymethyl- glutarimide (PMGI) resist as the sacrificial film and SPR700 as the photo-sensitive resist.

The schematics of the process flow is shown in the figure 2.1. After graphene film is transferred onto the the desired substrate, the sample is spun with PMGI SF5 (thickness

~ 400nm) and SPR700 (thickness ~ 1pm). SPR700 is then patterned with 375nm laser using standard photolithography method. PMGI is designed to dissolve in various de- velopers including that of SPR700. Once exposed SPR700 is dissolved in MICROPOSIT

CD-26 developer, the underlying PMGI will also be removed hence patterned as shown in the third step in Figure 2.1. Excess graphene can then be removed using oxygen plasma.

Next, PMGI/SPR700 films can be removed using organic solvent or developer. Additional high temperature annealing in forming gas can further clean the graphene surface.

PMGI is chosen instead of PMMA because it results in a cleaner film. The AFM images in Figure 2.2 show the resulting film after each resist treatment. Four pieces of

Si/SiO2 /Graphene samples were purchased from ACS Material. One piece was used as the control set where no resist was applied. The remaining tree samples were spin-coated with

PMMA, MMA and PMGI respectively. Then samples coated with PMMA and MMA were exposed under 220nm deep UV light to make the film dissoluble in developer and then immersed in diluted Methyl isobutyl ketone (MIBK) solution. The sample with PMGI was immersed in MICROPOSIT CD-26 developer. All samples were rinsed with Acetone and Isopropyl alcohol and dried with nitrogen gun. Shown in Figure 2.2 is the resulting

22 Figure 2.1: Schematics for PMGI/SPR700 bilayer Patterning Process. Green film represents PMGI and orange film represents SPR700

AFM images of the samples after their treatments. The circled dots are large particles on graphene surface. Since there are such particles on the bare graphene sample, it is possible that those particles are originated from the source of the graphene film instead of the resist treatments. From the Phase images it is easy to see that that PMGI treatment gives the cleanest film while PMMA treatment ends up with lots of contamination. Table

2.1 lists the surface roughness of the graphene samples after their treatment. Sample with

PMGI has the lowest average roughness while PMMA has the highest. It also seems like

PMGI treatment may be able to clean the graphene surface, since the roughness is even lower than that of bare graphene.

Even though PMMA tends to leave residues on graphene surface, it is still the most common supporting film for 2D materials wet transfer methods. Typically acetone is used to remove PMMA after transfer but there are still many particles left according to the

AFM images. N-Methyl-2-Pyrrolidone (NMP) is a much stronger solvent comparing to acetone. Figure 2.3(a) shows the graphene film after exposure to PMMA and Figure 2.3(b) shows the same film after an overnight immersion in room temperature NMP. It is clear to see that the film was much cleaner after the NMP treatment. The surface roughness is even smaller than that of PMGI treatment. However, NMP can be too aggressive and actually damage graphene. NMP heated at 600 C will tear the graphene film completely.

If there are large particles prior to NMP treatment, it is possible that room temperature

23 I! ,

Treatment Bare PMMA MMA PMGI

Height

Phase

Figure 2.2: AFM Images of graphene films onSiO 2 substrate with different photoresist treatments

NMP will lift up the particles too quickly and lead to a local tear on the graphene film.

Figure 2.3(c) is a microscope image showing the damaged graphene patterns after NMP treatment. To keep the graphene film clean and undamaged, acetone or developer can be used to first remove large portion of the polymer film. NMP treatment can be added in the last step to clean small residues.

Table 2.1: Surface roughness of graphene film after different treatment

Ra [nm] Rq [nm] Bare 0.583 0.769 PMMA 0.921 1.26 MMA 0.520 0.764 PMGI 0.425 0.268 PMMA + NMP 0.395 0.695

In summary, PMGI/SPR700 bilayer stack was developed to pattern graphene using standard photolithography. PMGI is chosen as the protection layer to keep graphene as clean as possible. In addition, room temperature NMP bath can help remove small residues from PMMA, MMA and PMGI films during fabrication process. This method

also works for patterning and maintaining the cleanness and integrityof MoS 2 films.

24 :::__~~~- - __ - --

60 pm

0.D hase gpm (a) (b)

Figure 2.3: Effect of NMP Treatment with PMMA Processed Graphene film on SiO2 substrate. (a) PMMA processed graphene, (b) PMMA processed graphene after NMP overnight, (c)microscrope image of patterned graphene film after NMP treatment

2.2 Passivation

The high surface-to-volume ratio of 2D materials make them very sensitive to environmental changes. It is one of the qualities that makes them ideal as sensing materials. However, it also means the device behavior can change upon exposure to unwanted but common molecules in ambient environment such as water and oxygen. Such changes will make the device non-predictable and it is non-ideal for sensor applications. One solution to this issue is to passivate the 2D material surface from direct exposure to the environment.

High-k dielectric thin films like aluminum oxide (Al203) and hafnium oxide (HfO2) have already been used as passivation layer in silicon technology. The standard technology to deposit such films is atomic layer deposition (ALD). ALD consists of two self-limiting surface reactions. The growth substrate is exposed to alternating pulses of precursor and oxidant with a purge step in-between. Each cycle deposit a monolayer of the material with high uniformity. The film thickness is easily controlled by the number of cycles.

25 -j

Comparing to other deposition methods involving high energy species such as sputtering,

ALD is less aggressive and can potentially reduce the damage on the 2D material films during deposition.

In spite of the great potential of ALD films to passivate 2D materials, the absence of out-of-plane dangling bonds in 2D materials make it difficult to initiate ALD growth

[71]. As shown in Figure 2.4, direct ALD growth on grpahene andMoS 2 resulted in islands of dielectric dots instead of a continuous film. The roughness of such films are normally larger than 1nm [78, 76]. The preferential growth of dielectric film mostly happens on defect sites, grain boundaries and wrinkles on the 2D film. This is due to the lack of functional dangling bonds on the basal plane. Density functional theory (DFT) calculation shows that various ALD precursors tend to absorb physically on the graphene basal plane [71]. Chemical adsorption of precursor molecules on graphene is unlikely due to the high activation barriers. The low probability of chemical absorption makes

ALD nucleation process more difficult[78, 71]. Defect sites, grain boundaries and winkles however, are more active and easier for precursors to absorb chemically. Similar behavior

was observed with 2D TMD materials such asMoS 2 . XPS shows weak interaction between

MoS 2 surface and hafnium precursor during standard Hf02ALD process [47]. To enable

(a) (b)

Figure 2.4: AFM images of ALD dielectric on (a)graphene and (b)MoS 2 . Figures adopted from [76] and [78] uniform ALD deposition, nucleation sites need to be added on 2D film surface intentionally to promote chemical absorption of precursors. This could be done by directly modifying the film surface or by depositing a seed-layer prior to the ALD process. Oxygen, ozone and nitrogen plasma treatment can convert part of the sp 2 bonds of graphene to out-of-

26 plane sp3 bonds to create oxygen or nitrogen containing surface groups [71].This method however, can result in degradation of charge carrier mobility due to the disruption of the sp2 backbone of graphene. By depositing a thin layer of polymer or metal film as the seeding layer can avoid the damage of 2D materials. Perylene tetracarboxylic acid coating was firstly used by Wang et al. to achieve uniform A12 03 ALD deposition on graphene [74]. The polymer seeding method typically results in a slightly reduction in carrier mobility due to additional phonon scattering introduced by the polymer layer. In addition, a 1.5-42.5V right shift of Dirac point was observed in some of the graphene

FETs using polymers seeding layer[71]. A metal seed-layer can be used to avoid p-doping the graphene channel. Kim et al. demonstrated an uniform deposition of 15nm A1 20 3 on graphene using a thin Al seeding layer. High mobility (8600 cm2V-Is-1) and near-zero

Dirac point (0.08V) was achieved after the deposition, indicating minimal damage and doping on graphene film [36].

In this project, we chose to use Al as the seeding material and ALD A1 2 03 as the passivation layer to avoid doping of the channel. Specifically, 2nm Al thin film is deposited on the device using electron beam evaporation. The device is then baked at 150°C for 10 minutes in ambient air to fully oxidize the Al film. 150 cycles of ALD process at 120°C with trimethylaluminum(TAM) and water as precursors are used to deposit roughly 15nm of A12 03 . The results is shown in Figure 2.5. The oxide film is uniform on both graphene and MoS 2 . The average roughness is 0.37nm for graphene and 0.42nm MoS 2 over 5 micrometers.

One of the reasons to have a passivation layer is to mitigate the hysteresis in measurement. Shown in Figure 2.6(a) is a typical I- V characteristic for electrolyte-gated graphene field-effect transistors (FETs). The source-drain current (Ids) is measured with respect to a double-sweep of gate voltage (Vg,).There are generally two mechanisms that may cause the hysteresis: charge transfer and capacitive gating [72]. When a total charge of Aq is transferred in/from graphene, it will cause a positive shift in conductance AV = Aq/C, where C is the capacitance of the dielectric [72]. The capacitive gating effect will increase the carrier density in graphene due to the enhancement of local electrical field [72]. This

27 (a) (b)

Figure 2.5: AFM images of ALD A1203 on (a)graphene and (b)MoS 2 surface with Al as the seeding layer

acts as an additional gating thus leading to an negative shift. In electrolyte-gated configuration, the capacitive gating is generally more dominant. As shown in Figure 2.6(a), an slightly negative shift in the Dirac point is present between the forward-sweep and backward-sweep. There is also a right-shift of the I-V curve between two consecutive measurements. This could be from the adsorbed water molecule on graphene surface,where electrons are transferred from graphene to the adsorbed water layer, resulting in hole doping (right shift) of graphene [55]. By having an oxide passivation layer, the hysteresis can be suppressed. As shown in Figure 2.6(b), no hysteresis is present between forward and backward sweep as well as between consecutive measurements in samples passivated

with ALD A12 03 . A possible explanation could be that the oxide layer protects graphene from water molecule and other surface absorbents. By having a high-quality dielectric film with the help of seeding layer, the interface between the oxide and graphene could be more stable and less likely to have interface traps. It is also important to note that hysteresis is depended on the voltage range, sweeping rate and surrounding condition

[72]. The data in Figure 2.6 were measured under the same range (-.7V to 0.7V), rate

(lOmV/s) and environment for fair compassion.

To further investigate the effect of oxide passivation on electrolyte-gated graphene

FETs, the I-V curves from before and after oxide deposition are plotted on the same axis as shown in Figure 2.7. The source-drain current decreased by roughly a factor of 1.5 at the

28 3.5p 1156p Sweep 1 1Sweep 1 Sweep 2 Swee 2 3.0Op 1.43p --

2.5p - 1.30p-

2.0 1.17P

1.5p1.04pi

1.011 910.001" ______-0.8 -0.8 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 -0.8 -0.6 -0.4 -0.2 0.0 02 0.4 0.8 0.8 Vgs(v) Vgs(V)

(a) (b)

Figure 2.6: (a)Hysteresis of electrolyte-gated graphene FETs without A1 2 03 passivation

(b)hysteresis of electrolyte-gated graphene FETs with A1 2 03 passivation

Dirac point after the oxide is deposited. If we consider the capacitance between graphene and the gate, there are mainly two contributions for devices without oxide: quantum capacitance of graphene due to its density of state and the double-layer capacitance at the electrolyte-graphene interface. With the additional dielectric passivation layer, there is a third component: oxide capacitance. The double-layer capacitance is roughly 10-20 pF/cm2 for common salt electrolyte [75, 43]. The quantum capacitance of graphene is given by the following equations [43]:

C = 2q2 (InG - n 1)1/2 (2.1) hvF#

qVch )2(2) hvFf where q is the electron charge, h is the reduced Plank constant, VF is the Fermi velocity, nG is the carrier concentration induced by gate voltage, n* is the effective charged impurity concentration and Vch is the electrical potential on graphene channel. Since the quantum capacitance depends on the electrical potential, it has a minimum value at the Dirac point.

The theoretical CQ,inis roughly 0.8 pF/cm2 but the experimental value is in the range of 1-10 tF/cm2 depending on the graphene quality and electrolyte condition[75, 43]. For

2 15nm of A12 0 3 , the capacitance is about 0.5 pF/cm . By having an additional small capacitor in series, the total interface capacitance of oxide-passivated graphene FETs

29 should be lower. Since the Ida, is proportional to square-root of total capacitance [43], the oxide-passivated device will have an decrease in channel current. The measurement in Figure 2.7 was taken using water as the solution gate. Taking into account the double-

layer capacitance of water (- 20pF/cm2 ) and CQ,, (~ 1 pF/cm2 for diluted electrolyte

[22]) as well as oxide capacitance (~ 0.5 pF/cm2 ), the ratio of Ids,min between devices with and without oxide is about 1.7.

1 00n

3.Op - ids no oxide - 10n in-Ids with oxide -- Igs no oxide riOn Igs with oxide

in -r - - 2.Op

V/ lop - 10p.P

1.0p 1 p

*lOOf -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 Vgs (V)

Figure 2.7: Effect of A1203 passivation on electrolyte-gated graphene FETs' I-V characteristic

The leakage current (gate current Ig,), however, was 3 orders of magnitude smaller for oxide-passivated device. Low leakage current is good for avoiding reaction in the electrolyte. The Dirac point also changed from near-zero to a more negative value, indicating a slight n-doping effect from A12 03 on graphene film. Another issue to note is that the V-shaped curve is less "sharp" with the oxide passivation. The channel conductance is less "responsive" to the change in electrical potential, which is non-ideal for a chem-resist type of sensors. In fact, the oxide film could also introduce additional charge traps that hinder the device performance if the oxide quality is low due to undesired chamber pressure or temperature. In a latter section we will talk about the importance of surface

30 functionalization. Some functionalization can cover the sensing surface in a uniform and insulating way so that the functionalization can act like a passivation layer. In this case, it is not necessary to add an extra layer of oxide.

Even for sensing applications that does not require oxide at the sensing area, passivation at the contacts is still needed in order to protect the contacts from corrosive solution.

Polymers such as SU-8 can be used to passivate the device while leaving the sensing area open the electrolyte. The hydrophobicity of such polymer can also promote analyte in solution to reach the sensing material. Previous research has shown that a recessed SU-8 passivation on graphene electrolyte-gated field-effect transistors can improve transconductance performance [43]. It also shows SU-8 passivated device may be biased over a wider range of V, values while still having near-optimal transconductance [43]. The maximum leakage current can also be reduced to 10s of nA. Shown in Figure 2.8 is the sensor re- sponses of two devices. The two devices went through the same fabrication process except for the passivation layer. The device in Figure 2.8(a) is passivated with 500nm of SU-8 with the sensing area open. The device in Figure 2.8(b) is passivated with 15nm of A 2 0 3 . Two consecutive measurements were taken at each concentration for both devices. The black dots indicates first measurements and the red dots are the second measurement.

It is clear that the device oxide passivation has a large variation between two measurement while the device with no oxide passivation has almost no difference. In this case the functionalization is a thick polymer film that already provides enough passivation for graphene from the solution. Adding a oxide layer not only reduces the current reading due to the oxide capacitance, but could also induced additional impurities that eventually increases the hysteresis of the device. Therefore, for sensors similar to Figure 2.8, SU-8 passivation is more suitable than oxide passivation.

Comparing to graphene,MoS 2 has shown more severe hysteresis in unprotected films.

Shown in Figure 2.9(a) is the output characteristics of a back-gatedMoS 2 FET without oxide passivation. The hysteresis between forward and backward sweep is very large.The

Ids starts to decrease after device reaches the saturation region. This indicate severe degradation of theMoS 2 in ambient environment.Shown in Figure 2.9 (b) is the output

31 0.66 A10041719 0.64- A041119 02. • 2ndsweep 0.62 1 e2ndsweep 0.60- 0,20 - 0.58 I 0.56 _0,15 . 00.54T 0.2 0. 10 -

0.52 -

0.46 -0 - -6 -5 -4 -3 - -1Log(concentration) Log(concentraion)

FuncFunctionaation SU-8 S U-8 A203

(a) (b)

Figure 2.8: Sensor response and structure of device (a) SU-8 passivation and (b) with oxide passivation

characteristic of oxide-passivatedMoS 2 . The hysteresis is drastically reduced and no degradation is observed at high voltage. The transfer characteristic of non-passivated

MoS 2 devices is shown in Figure 2.9(c). The hysteresis between forward and backward sweep is roughly 0.5V. The hysteresis between two sweeps is about 0.25V. The on/off

3 ratio is 10 , which is much lower than that of typical MoS 2 based transistors.

With oxide passivation, the hysteresis of forward and backward sweep is still present but the hysteresis between two sweeps is suppressed. The clockwise hysteresis in the transfer characteristic can be explained by the trap-filling phenomenon [34]. In forward sweep, negative bias pushes electrons trapped at the interface into the MoS 2 channel. The interface traps are then positively charged, leading to a negative shift of the curve. When a more positive bias is applied at the backward sweep, the electrons injected into the trap states and the traps become neutral, shifting the curve to a more positive position.

Since an oxide below a top gate will not mitigate the charge traps in the back-gate dielectric, it is expected that same type of hysteresis is still present in passivated device.

The on/off ratio of passivated device increases to 106, which is comparable to the values in literature. However, the sub-threshold swing (SS) decreased from 190 mV/dec to

32 250.On - 100.01 - -Vgs = -1V --- VgsOV <1.5x 200.0n - 5x -SO 01.5op - ~.

100.0n - -- Vgs = V 40.011 -Vgs = 2V 50.0n - -Vgs - 3V 20.Op -

0.0 . 0.0- '-

-0.1 0 0.1 0. 0.3 0.4 0.5 0.6 0.7 0.8 0 1 2 3 4 Vds (V) Vds (V)

(a) (b)

•Sweep 1I Sweep 1 10n - eSweep 2 1p - Sweep 2 ilon

1On ino

100p.p

lp - 100p

-0.5 0.0 0.5 1.0 -7.5 -7.0 -8.5 -6.0 -5.5 -5.0 -4.5 -4.0 Vgs () Vgs ()

Figure 2.9: I- V Characteristics of MoS 2 Back-gated Transistors.(a) Output characteristics without oxide, (b)output characteristics with oixde, (c) transfer characteristics without oxide, (d) transfer characterstics with oxide

442 mV/dec. Also, the threshold voltage (VT) changed from near OV to below -7V.

This is highly undesirable for sensing application. Ideally, MoS 2 FETs should have a sub-threshold region near V with a sharp SS. In this way, a slight change in electrical potential due to analyte will result in a large change in source-drain current.

We showed that ALD A1203 on MoS 2 could protect the film from ambient environment thus reducing device degradation and hysteresis. It is also important to investigate other passivation methods to reduce hysteresis while keep the sub-threshold region to near OV with a sharp sub-threshold swing. Shown in Figure 2.10 is the transfer characteristics of

et al. [78]. For MoS 2 transistors with different types of passivation layer conducted by Yu future work, we could try NFC buffer layer before ALD A12 0 3 since Yu et al. has shown

33 - 1

300 nO ADO,+ ALD A,0, 200 ma 10 10 niol150 0 315FNl 101 100 A10 1 10

-2 0 2 4 6 • 1 2 3 4 5 -- 140 101

601

so- 10. .1 .L .A.10.. 20 101 2 0 4-2 0 2 4 6 1 3 4 S 6 40

a, , ,1 5 . .., , 10t 7 TCNQ+NPC 060 40 6 + ALD A,03 10 /

010 3 1-0 1 ~40 0L 10'0 9 0 0 ~~~0.1 . . 20 -0' -- 2 0 2 4 6 -3-2-1 0 1 2 3 4 5 6 - V -2 0 2 4 1 4 6 vTG(v) vG(v) vT6(v) vTG(v)

Figure 2.10: Transfer characteristics of MoS 2 based transistors with different types of passivation. Figure adopted from Yu et al. [78].

that it could mitigate the n-doping effect of A1 2 0 3 layer well still keep the current and SS as high as possible [78].

2.3 Conclusion

In this chapter, we introduced a new method to pattern 2D materials with cheap and faster photolithography instead of expensive and slow electron beam lithography. PMGI was determined to be the best resist to keep the graphene free of residues. Bilayer stacks of PMGI/SPR700 can be used as the etch mask for graphene patterning process. Room temperature NMP can effectively clean the graphene surface after fabrication.

The second part of the chapter discussed the passivation technology for 2D films. A thin layer of Al metal needs to be deposited on the 2D film prior to ALD deposition.

A high-quality and uniform A1 2 03 can successfully passivate both graphene and MoS 2 surface from contamination in the ambient environment. We demonstrated that A1 2 03 passivation layer can effectively reduce hysteresis and reduce leakage. However, oxide

34 film can also induce additional charge traps or doping effect on graphene andMoS 2 . In practice, the passivation material needs to chosen carefully with full consideration of the actual requirement of the application.

35 Chapter 3

Graphene-based Biosensors

3.1 Electrolyte-gated graphene field-effect transistors

Our body generates many types of fluids such as sweat, tears, urine and blood. These

body fluids contains numerous molecules that indicates the status of our health. Many

chemical and biological sensors that have been developed over the years are aimed at

detecting those biomarkers in their original fluid state. Therefore, electrochemical sensing

is a large field within the sensor family. However, these sensors typically require bulky

measurements instrument such as potentiostats and experienced professionals to run and

analyze the collected data. An FET-based sensor approach is cheaper and less bulky.

With a simplified readout system, it is more suitable for realization of easy-to-use health

monitoring system. Graphene is well-suited for electrochemical measurement due to its

wide electrochemical potential window of 2.5V in O.1M Phosphate-buffered saline solution

(PBS) [84].

Graphene electrolyte-gated field-effects consist of a sheet of graphene channel with two

metal contact:source contact and drain contact. The sensing area is directly exposed to the electrolyte environment. An Ag/AgCl reference electrode is typically used to apply

stable gate voltage. Passivation layers is normally used to protect the contact while leaving the graphene exposed to the electrolyte. The device structure and measurement setup of a typical electrolyte-gated graphene FET is shown in Figure 3.1(a). When the gate- electrode is interfaced with the electrolyte, an electrical double layer is formed. Cations

36 or anions in the electrolyte migrate to the surface of the electrode due to the potential

applied by the gate. In equilibrium, the ionic charge is screened by an equal and opposite

amount of charges in the electrode and thus charge neutrality is achieved. Since the charge

separation occurs over only a few nanometers, the electric double layer capacitance is fairly

large. Large gate capacitance combining with graphene's high mobility promises graphene

electrolyte-gated FETs with large-transconductance and high sensitivity towards change

in electrolyte environment.

s s (a) (b)

Figure 3.1: (a)Device structure and measurement setup for electrolyte-gated graphene field-effect transistors. (b)Three most common models to describe electric double layers, figure taken from [36]

3.2 pH sensing

To demonstrate the sensing ability of graphene electrolyte-gated FETs, the device is

tested with buffer solution with different pH values. Specifically, the fabrication process

starts with a silicon substrate with 300nm of Si0 2 . A layer of Ti/Au (5nm/100nm) metal

stack is deposited using electron beam deposition as the source and drain measurement pads. The metal pads are kept away from the actual sensing area in order to leave a

clean and flat area for graphene transfer. Oxygen plasma is used to clean and prepare

the surface for transfer. Graphene film is obtained by ACS Material. ACS Material

provides a type of graphene product called Trivial Transfer Graphene. Instead of the

typical copper/graphene/PMMA structure of most commercial CVD graphene product,

Trivial Transfer Graphene has a polymer/graphene/PMMA structure. Water can easily

37 get into the polymer/graphene interface thus delaminate the graphene/PMMA from the polymer substrate. This product eliminates the step of copper etching and makes the transfer process easier and less time-consuming. The transfer process starts by dipping the Trivial Transfer Graphene into DI water so that graphene/PMMA film is floating on top of the wafer. Graphene film is then scooped out using the pattern substrate onto the sensing area. Excess water underneath the film is blow away gently with a nitrogen gun.

The device is baked at 50°C for 1 hour to remove the remaining water. The device is then baked at 150°C for 30 minutes for PMMA to reflow and flatten out the graphene film.

The PMMA is latter removed using acetone or NMP. The graphene is patterned using

PGMI/SPR700 stack as mentioned before. A second layer of Ti/Au (5nm/100nm) is deposited on the pattern graphene to form source and drain contact. In the end, 500nm of SU-8 is used to passivate the contact while leave the graphene channel open to the electrolyte. The detailed fabrication process is attached in the appendix.

The pH solutions were prepared using buffer solution pH = 4, 7 and 10. IM NaOH was used to to adjust the pH to desired value. The source-drain current Id, is measured with respect to a gate voltage sweep (Vg,) while keeping the drain voltage (V,) unchanged.

The relative shift of Dirac point with respect to change in pH is shown in Figure 3.2.

When the pH value increases, the Dirac point shifts to more negative value. The change in Dirac point shows a near-perfect linear relationship with a slope of 58.8mV/decade.

This demonstrate the intrinsic sensitivity of graphene toward pH values. A hypothesis of the sensing mechanism is the adsorption of hydroxyl (OH-) and hydroxonium (H30+) ion on graphene surface changes the doping level of the channel [59]. The change in surface potential due to adsorption of charged surface group can be written as the following [61):

Aq#8 kBT = -2.3a (3.1) ApH q

kBT GS(32 a = (2 .3 C)-1 (3.2)

where #, is the surface potential on graphene, kB is the Boltzmann constant, T is

38 I I I I I I 0.30-

0.25 - -

0.20-

CL 0.15 -

0.10 - - Slope 58.8mV/decade 0.05 - .

0.00-

I I I I I| 4 5 6 7 8 9 pH

Figure 3.2: Change in Dirac point with respect to pH value temperature, q is electronic charge, Cs is electrical surface capacitance and #s is intrinsic buffer capacity, which represents the change in the surface charge with the change in pH of the solution near the surface. The ideal sensitivity at room temperature is 59.6 mV/pH.

Our device shows excellent agreement with this theoretical sensitivity.

According to the surface charge adsorption hypothesis, a base solution will leave negative capacitive charge groups on top of graphene, which lead to a n-doping effect. In this way, the Dirac point should shift to more positive value with high pH. Our device however, experiences opposite shift of Dirac point. In fact, many researches have tried to use intrinsic graphene directly as the sensing material for pH testing. Different groups obtained very different results with similar device structure [59]. Lee et al. investigated the variation of graphene sensitivity towards pH With exposure to NaOH, HCl and KOH.

The pH sensitivities were -78,-38 and -7mV/pH in buffer solution containing interference ions of Na+, Cl- and K+ respectively [25, 59]. In addition, the sensitivity with 0.05M reference pH buffer solutions is +69mV/pH [25]. Lee et al. suggested that the shift of the Dirac point is a consequence of ionic charge screening. The charge screening effect depends on ion size, degree of hydration and affinity towards the graphene surface [25, 59.

39 By testing with more diluted solution, which is less affected by charge screening, the slope can change from negative to positive [25].

The large surface-to-volume ratio and inert 7r-bond in graphene gives it intrinsic sensitivity to environmental changes. However, the absent of selectivity makes graphene non-ideal for sensing technology, as the signal can be easily interfered, resulting in large noise and untrustworthy data. Luckily, the graphene surface can be functionalized with variety of receptors and membrane which provide selectivity to the graphene-based sensor technology.

3.3 Ligand Detection with GPCR

Bioelectronic is an emerging field of study which combines biomolecules with electronics to mimic biological architectures. One important aspect of bioelectronics is to fabricate sensors for detection of small biomolecules, i.e. ligands. Recent efforts are devoted on directly connecting biological receptors with electronic systems. G protein-coupled receptors (GPCR), are the largest family of membrane receptors that detects information

(molecules and lights) and transduces them into cell internal signals to regulate body functions. There are nearly 1000 types of GPCR proteins in human body, each one being highly specific to a particular signal, which make them suitable candidates as functionalization for graphene-based sensor technology.

Previous research has been done by our collaborator Dr.Qing on the effect of modifying

GPCR proteins to obtain water soluble receptors active to their natural ligands without adding any detergent. Recombinant SbpA S-layer proteins can reproduce ordered 2- dimensional crystalline monolayer in vivo and were employed as the intermediate layer between bio-molecules and electronic substrates. Shown in Figure 3.3 is the schematics of the 2D lattice. This structure takes the most efficient usage of the graphene surface and form a uniform coating of the bio-molecules. The intermediate layer helps to immobilize and guide the orientation of attached bio-molecules. S-layer proteins form the outermost cell crystalline component in many bacteria and archaea. They are porous monomolecular layer with unit cell size in tens of nanometers[60]. S-layer proteins exhibit self-assembly

40 -4

capability in vitro on a variety of solid surfaces including silicon wafer, silicon oxide,

ITO, etc. By fusing Fc region of human IGg protein to GPCR and Protein GG onto

SbpA, a GPCR/S-layer complex can be formed. Each unit cell contain four cross-linked

S-layer proteins as shown in the left-side of Figure 3.3. Assuming each "arm" of S-layer

can attach 1-2 GPCR proteins, the density of the aligned receptors is 2.37 - 4.73 * 101

molecule/cm2 . We are able to anchor them onto the electronic active surface such as

silicon and graphene.Shown in Figure 3.4 is the AFM image of GPCR/S-layer complex

on silicon surface. The self-assembled S-layer shows the 2D crystalline structure and the

GPCR/S-layer complex exhibits high uniformity.

N -f- -TG,-c "- N

13nm Electrical or electrochemical active substrate

Figure 3.3: Schematics of 2D lattice of GPCR/S-layer complex. Images taken from Rui Qing, PhD.

~~CIO

-7 a e -- 0 2 3 06 a8 1D 0 103 20 330 400 SCM Bare SI Wafer Si coated with SbpA ZZ SbpA ZZ-CXCR4-Fc

Figure 3.4: AFM images to show surface morphology of S-layer and GPCR/S-layer complex on silicon substrate. Images taken from Rui Qing,PhD.

41 3.3.1 Device Structure

To produce a graphene sensor capable of detecting ligands, we used a device structure similar to the graphene pH sensor discussed in the previous section. The mask design and measurement setup is shown in Figure 3.5. An additional liquid chamber is designed to help prevent evaporation. There are two openings on the liquid chamber. The smaller opening is for adding/removing solution into/from the chamber. The other one is for the Ag/AgCl reference electrode. The graphene channels come with 3 sizes: 30Am *

30pm, 30pm*150pm and 30pm*300pm. For the following section, all measurements are conducted with the 30pm*150pm configuration. Roughly 108 receptors can be expected on each device. Specifically, CXCR4 is used as the GPCR receptor. Its native ligand

CXCL12 is the target analyte.

The deposition of GPCR/S-layer complex consist of 2 steps: the buffer solution containing S-layer is dropped on top of the sensing area. S-layer will then self-assemble onto the graphene surface with the amino-terminus as indicated in the top of Figure 3.3. The green wavy lines in the bottom of 3.6 (b) represents the deposited S-layer on graphene surface. The device is then soaked in DI water overnight and rinsed multiple times to get rid of excess S-layer. GPCR solution is then dropped on the device to form GPCR/S-layer complex. The Orange circles in Figure 3.6 (c) represent GPCR protein linked to S-layer on graphene surface.

GPCR Sensor

(a) (b)

Figure 3.5: (a)Mask file of the graphene-based ligand sensor with GPCR (b) Measurement setup and fluid chamber

42 3.3.2 Sensor Response

We first tested the electrical response of bare graphene with exposure to CXCL12 ligand.

As shown in Figure 3.6(a), the I-V curve shifts to the left after ligand is added to the

liquid chamber. As discussed before, graphene itself already has high sensitivity towards

changes in the environment. The shift of the curve could due to the physical absorption of

ligand molecules directly on graphene surface. The black stars in Figure 3.6(a) illustrate

the ligand absorption with bare graphene. Even though bare graphene can give signal

change upon exposure to anaylte, it has no selectivity. The signal can also be easily

disturbed by other environmental changes. Shown in Figure 3.6(b) is the response of S-

layer coated device with CXCL12 ligand. A smaller left-shift was observed. Since S-layer

cannot react or bind with CXCL12 ligand, the origin of the curve shift should be similar

to that with the bare graphene, i.e., ligand physisorption. Referring back to the geometry

of S-layer lattice in Figure 3.3, a large portion of the graphene surface is covered with

the S-layer protein, making it more difficult for the ligand to reach the graphene surface.

Therefore, ligand physisorption probability is smaller, leading to a smaller left-shift in the

I-V curve.

Figure 3.6 (c) shows the device response of GPCR/S-layer complex on graphene chan-

nel. Different from the other two cases, ligand induces a right-shift in the I-V curve.

This indicates that the ligand binding process is present. Compare to S-layler, GPCR

protein is considerably smaller in size. Therefore, it is still possible to have physisorption

of ligand even with the GPCR receptors deposited. In fact, a slight left-shift of the I-V

curve was observed 30 minutes after the ligand is added, indicating a slow physisorption

of ligand on graphene surface. It is important to note that amount of ligand used for

the measurements in Figure 3.6 was larger than the total amount of GPCR receptors.

The GPCR receptors were saturated with CXCL12 ligands. Once all the GPCR available receptors bind with ligand, the rest of free-flowing ligand can be physically absorbed onto the graphene surface. Therefore, the amount of GPCR receptors will determine the upper bound of ligand detection range. Another interesting behavior to notes is that both the self-assembly of S-layer and GPCR/S-layer complex induce left-shift in the I-V curves.

43 7 5 , . Bare Graphene S-layer 6 Graphene+ Ligand 4- 5

3 -- .N 4 --

3 - 20

2 -

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0'2 04 0.6 0.8 1 0 Vgs(V) Vgs(V)

(a) (b) 3.5 GPCR 3.0- GPCR+Lgand

-- 2.5-

2.0 - ~ 0

1.5-

1.0 -

-14 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 Vgs (V

(c) Figure 3.6: I-V response and schematics upon exposure of CXCL12 ligands with (a) bare graphene, (b) S-layer and (c) GPCR/S-layer complex. Id, is normalized to its minimum value. Black stars represent ligand, green lines represent S-layer, orange circles with black stars represent ligand bind with GPCR protein.

That shift allowed us to check if the deposition of receptors is successful.

Once we demonstrated that GPCR/S-layer complex on graphene is able to produce reliable signal shift indicating the present of CXCL12 ligand, we tried to calibrate the device with different concentration of ligands. A serial dilution of CXCL12 ligand solution ranging from 10- 4 M to 10- 7 M was prepared. The I-V characteristics were measurement from low concentration to high concentration in order to minimize contamination from previous measurement. After the coating of GPCR/S-layer receptors onto graphene surface, the Dirac point shifted from 0.39V to 0.19V. This left-shift behavior agrees with

44 previous experiments. We define the sensor response S as the relative shift of Dirac point induced by the charge brought to the receptors when binding occurs. The equations for sensor response and Langmuir isotherm are shown [48, 38]:

S= VDP - V)P (3.3)

C C Kd (34) S Sma+ Smax

where C is the ligand concentration, Kd is the affinity equilibrium (or dissociation) constant of protein-ligand pair, S is the sensor response. According to Equation 3.4, one can expect a linear relationship between jS and C. Shown in Figure 3.7 the plot of vs. C from the graphene-based ligand sensor. By fitting to the Langmuir isotherm, the affinity equilibrium of binding Kd can be extracted as 2.35nM and the maximum sensor-response

Smax to be 135.9mV. The binding affinity Kd of CXCR4/CXCL12 pair in literature is reported in the range of 4 - 44nM depending on different solution environment [82, 77].

No one has reported the binding affinity of CXCR4/CXCL12 pair in DI water because the receptors tend to precipice. Our result (Kd = 2.34nM) indicates an improvement in binding affinity comparing to that in standard buffer solutions.

Additional tests should be done to further understand the sensor behavior. It would be useful to perform a negative control test with a ligand that would not bind to the GPCR receptors. Also, a larger span of the ligand concentration should be used to determine the upper and lower bond of detection limit. In addition, the sensor behavior shows excellent agreement with Langmuir isotherm equation. A more detailed model could be developed to better extract parameters. With such model, the graphene-based ligand sensor can be an innovative way to study the binding kinetic and determine affinity equilibrium constant

Kd.

45 1 E-5

1E-6

1 E-7

U 1 E-8 -

1E-9 - M 1E-10

1E-11

1E-12

1E-13-.....,...... ,...... ,...... ,...... ,...... 1E-15 1E-14 1E-13 1E-12 1E-11 1E-10 1E-9 1E-8 1E-7 1E-6 Ligand concentration C (M)

Figure 3.7: Sensor response with respect to ligand concentration demonstrates good linear relation between C/S and C (R2 = 0.992). S is defined as relative change in Dirac point.

3.4 Conclusion

This chapter demonstrated graphene-based field-effect transistor as an effective configuration for liquid-phase sensing. A pH-sensitive graphene sensor was fabricated and tested,

The sensitivity is 58.8mV/pH, which is very close to the maximum theoretical value of

59.6 mV/pH. The intrinsic sensitive could be from the absorption of hydroxyl and hydroxonium ion on graphene surface. The intrinsic sensitivity however, is easily disturbed by interfering ions in the solution. This highlights the lack of selectivity in intrinsic graphene.

In the second portion of the chapter, a novel ligand sensor was developed using graphene as the transducer. GPCR receptors were used to provide selectivity to the graphene-based sensors. The ligand binding process induces a right-shift of the Dirac point following the

Langmuir isotherm theory. The extracted binding affinity is 2.35nM, which is better for the GPCR receptors in buffer solution. Additional tests should be performed in order to fully understand the ligand sensing mechanism.

46 Chapter 4

Graphene-based Ion Sensing

There are many ions presents in humans body fluids. Some of the most common elec- trolytes are sodium, potassium, calcium, chloride etc. The balance of electrolyte in human body is essential for the normal function of cells and organs. For example, an excess of sodium concentration could be indication of kidney disease while a decreased in sodium concentration could be from patients with congestive heart failure. Therefore, it is important to be able to detect concentration of these ions in electrolyte. In this chapter, different types of ion selective membranes are used as the functionalization on graphene in order to detect multiple types of ions in electrolyte.

4.1 Theory of Ion Selective Membrane

Generally speaking, an ion selective membrane (ISM) is a polyvinyl chloride (PVC) main- brane containing ionophores. Ionophores are lipophilic molecules that selectively bind to an ion of interest. Ionophores are especially useful in sensing applications because they provide both sensitivity and selectivity. In addition, because ionophores are lipophilic, they possess a high affinity for the membrane phase over the solution phase and may be assumed confined to the membrane. When a neutral ionophore is used, lipophilic ion sites with opposite charge of the analyte ion, in this case anionic sites, have to be added in order to suppress the extraction of chlorite into the membrane [11, 6, 69]. Previous research also shows that these ionic sites in the membrane with an optimized ratio can

47 I

effectively reduce response time, lower the electrical membrane resistance and increase selectivity [69]. Figure 4.1(a) shows a schematics of calcium ISM on a graphene sensor device. The lipophilic anionic sites are marked as R- in Figure 4.1(a).

CIIF C0- Ca2+ CI- C Ca2+ Cl-.

C1- Ca2+ cl-

0i-C Ci- Ci-c Ci- C- Ci- Ci- C[- J R R R-

R- 4W R

Vs Au -A AU T~1'. - A V VDS [ AO =Au Graphene SU-8 [ ISM = Electrolyte

neutral lonophore complexed onophore R- anionic site

(a)

Potential DS A ISM electrolyte VM-

2 VGS \ Ca +

0- \ /

Distance from graphene Vos

(b) (c)

Figure 4.1: (a) Graphene Ca2 + sensor diagram depicting measurement setup and equilibrium charge distribution. R- represents lipophilic anionic site. (b) schematic diagram showing the electrostatic potential as a function of distance from graphene surface. The dash line indicates the potential distribution when zero Ca2 + concentration gradient is present between ISM and electrolyte. (c) idealized graphene Ca2 + sensor I-V characteristic response.

48 In order to obtain electroneutrality inside the membrane, the concentration of cation

must balance the total charges of the anionic sites. Since the amount of anionic sites

are fixed when preparing the membrane, concentration of target ion is constant within

the membrane phase and is independent of concentration of the electrolyte outside of the

membrane. This translates into an electrolyte-interface potential that is solely a function

of the target analyte concentration. Because of this, the interface potential can be related

back to the target analyte concentration in the electrolyte phase. The potential at the

membrane-electrolyte is governed by the Nernst equation as given by Equation 4.1:

0 VME = VM- VE= V +2.3lo Ca2) (4.1) zF Cam

where VME is the potential difference between the membrane and electrolyte, VE is the

electrolyte potential, which equals to VGS in Figure 4.1 (a), VM is the membrane potential,

Vo represents all electrolyte-independent potential contributions. R is the gas constant,

T is the temperature, z is the charge number of the analyte, F is the Faraday constant, Ca+is the concentration of calcium ion in the electrolyte phase, and Ca+ is the calcium

ion concentration present in the membrane phase. When Ca2+ remains constant, the

potential can be related directly to the concentration of Ca2 as shown in Equation 4.2:

RT VME = V+ +2.3 log([Ca2E+)zF (4.2)

where V' has been renamed Vo to include the constant term resulting from the

log([Ca2+]) or a bivalent ion such as Ca2+ at room temperature, the slope is theoreti-

cally approximately 30 mV/decade. According to equation 4.2, potential VME increases

with increasing Ca2+ concentration. Because the polarity of VME is aligned with the po-

larity of VGS , an additional potential drop is added on the graphene channel as shown in

Figure 4.1 (b). In the presence of higher Ca 2 + concentration, a further accumulation of

electrons occurs in graphene channel and a higher electron current (lower hole current) is

achieved for the same VGS. Hence, increasing Ca2+ concentration induces a leftward shift of the graphene I-V characteristicdepicted in Figure 4.1(c)resulting in a more n-doped

49 channel. This translates directly to the minimum conduction point, or Dirac point, which shifts by the same amount according to equation 4.3

RT VDirac = V ac- 2.3 log([Ca2+]) (4.3) z F

Because graphene EGFET have a V-shaped and approximately linear current-voltage

(I-V) characteristic away from the minimum conduction point, there exists a direct linear relationship between the shift in voltage and change in current. Therefore, it is possible to relate the change in current to the change in analyte concentration as given by equation 4.4

IDs = IS + k log([Ca +]) (4.4)

where IS is some constant baseline source-drain current and k is the slope of the voltage shift multiplied by the slope of the graphene I-V curve at that particular point.

4.2 Sensor Array Structure and Performance

In the previous chapter we have demonstrated graphene's excellent sensing capabilities.

However, this high sensitivity also makes graphene-based devices very sensitive to fabrication process non-uniformities. Small differences in graphene quality, such as surface contamination and defects, can lead to large variation in device behavior. Therefore, it is important to develop sensor arrays with redundant data sets and utilize statistical analysis to extract and classify information with higher accuracy.

Shown in Figure 4.2 is the mask layout of the 16-by-16 graphene-based sensor array chip. The chip can be connected to a customized printed-circuit-board to acquire data.

Shown on the right-side is the zoom-in picture of the sensing area as well as a microscope image of one of the sensors. Fabrication of the array begins with piranha cleaning a

300 m thick glass substrate. The substrate was coated with 30 nm aluminum oxide using atomic layer deposition to enhance adhesion in subsequent photolithography steps.

A layer of Ti/Au (5 nm / 150 nm) was deposited using electron beam deposition to

50 form the row contacts of the sensor array. A 30 nm layer of aluminum oxide was then deposited as interlayer dielectric using atomic layer deposition. Openings were etched into the interlayer dielectric using a BC13 plasma to allow contact between the first and second metal layers in the array where appropriate. A second metal layer of Ti/Au

(5nm / 150 nm) was then deposited using electron beam deposition. Graphene coated with Poly(methyl methacrylate) (PMMA) was transferred on the substrate to cover the sensing area of the array. The chip was baked at 50C for 1 hour and 150C for 30 minutes.

This allows PMMA to reflow and enhances adhesion between the graphene and substrate.

The sensor array chip was then immersed in acetone overnight to remove the PMMA.

Graphene sensors were isolated from each other by using an oxygen plasma. A patterned

PMGI/SPR700 resist stack was used as the etch mask as discussed in Chapter 2.1. The white-dotted box indicates the outline of patterned graphene. The graphene channel dimensions is W/L = 30 /mu m / 30 /mu m. SU-8 is used to passivate the whole chip except the graphene channel and the contact on the very top. Each chip has 254 devices in total and the yield is typically higher than 80%. The detailed process flow is listed in the appendix.

------i MetaI2 Metal1 U 1 l 33J Oode etch I I II I ~3j metal a

- -i .-*----

Figure 4.2: Mask desgin for graphene-based sensor array. Top-right is azoom-in picture of the sensing area. Insert shows the mask ofan individual graphene sensor. Bottom-right is amicroscope image of agraphene sensor on the array chip after fabrication

The typical I-V curves of the array chip are shown in Figure 4.3 (a). The sensing area

51 of the chip was immersed in luM NaCl electrolyte and the I-V curves were measured with

Vd, = 300mV. The gate was swept from -0.9 mV to 0.9 mV using a Ag/AgC1 reference electrode. There are 224 working device in this chip. The average Dirac point is 0.16V with a standard deviation of 0.02. The Dirac points are calculated as accurately as possible by polynomial fitting the discretized I-V characteristics and finding the minimum of the continuous polynomial fit.The distribution of Dirac points are plotted in Figure 4.3(b).

Even though the 244 devices were patterned from the same sheet of graphene (under

5mm*5mm) and went through the same fabrication process, they still exhibit different

I-V characteristics in terms of location of Dirac point, on/off ratio, source-drain current magnitude etc. This again demonstrated how difficult it is to make graphene-based sensors with identical behaviors and emphasized the importance of having an array of devices and study the collective behavior of sensors.

09 30 08

0.7 25

03 -10

0 0 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 04 06 08 1 01 0.12 014 0.16 0.18 02 022 0,24 Vgs [V] (a) (b)

Figure 4.3: (a) I-V characteristics of 244 working electrolyte-gated graphene transistors on one chip with Vd, = 300 mV.1uM NaCl solution was used as the electrolyte. (b)distribution of the Dirac points

Once the chip is fabricated, we can deposit ion-selective membrane. on top of the sensing region. ISM typically contain 30-33 wt% of PVC, 60-66% of plastizer, less than

1% of ionophores and less than 1% of anionic site. The mixture can be dissolved in

Tetrahydrofuran (THF) and the resulting ISM solution can be spin-coated or drop-cast onto the sensor surface. In the following session we will talk about 3 types of ISMs we have demonstrated with the graphene sensor array: calcium, sodium and potassium.

52 4.3 Ca2+ ion sensor

The calcium ISM was made by mixing 0.656 g of 2-nitrophenyl octyl ether (oNPOE),

0.328 g of high molecular weight (PVC), 0.01 g of calcium ionophore II (ETH 129), and

0.06 g of potassium tetrakis(4-chlorophenyl) borate. The mixture was dissolved in 6 mL of tetrahydrofuran (THF), which is approximately 85% by weight. The solution was then spin coated over the array at 1500 rpm for 120 seconds and allowed to air dry.

IM aqueous CaCl2 solution was diluted over several orders of magnitude to provide a variety of concentrations: 10 nM, 100 nM, 1IM, 10 pM, 100 pM,1 mM, 10 mM, 100mM.

The chip was pre-conditioned in 100 mM Ca2 + solution for 2 hours before measurement.

I-V characterization was performed for all devices at each Ca2+ concentration. The drain- source voltage Vd, was held constant at 300 mV and the gate-source voltage Vg was swept from -0.4 V to 0.9 V in 10 mV increments. A 10-second hold time was used before the gate- source voltage Vg.swas swept at a rate of 10 mV / 500 ms. This provides adequate time for charged species to migrate and reach steady-state before measurement. All measurements were conducted under ambient conditions at room temperature. Solution volumes were large enough (approximately 30 mL) so as not to evaporate appreciably over the course of the experiment. Solution volumes were also large enough so that contamination when moving from lower concentrations to higher concentrations was negligible and could not appreciably alter the calcium concentrations. Shifts in I-V characterization as a function of Ca2+ concentrations of one example device are reported in Figure 4.4 (a).

More than 190 devices were responsive to change of calcium concentration. When calcium concentration become large, the I- V curves shift more to the left. The average slope of -31.7 mV/dec is in excellent agreement with the theoretical Nernstian slope for a bivalent ion as derived in Equation 4.2. The distribution of sensitivity of all working devices is quite narrow. The standard deviation is 1.4 mV/decade with almost all sensitivities falling within the 26 to 34 mV/decade range. As shown in the table below, graphene

EGFETs array shows excellent sensitivity with larger detection range comparing to other calcium ion sensors in the literature.

Stability is another importance figure of merit for sensor behavior. The chip was

53 100.0p I

800 1I j 0.50 -

40.Op - M a0.45 luM c 400 - -- 1mM Sope =31.7mV/decade

- 100mM 0.35 20.OV0.301

-. -04 -0.2 00 0.2 0.4 0' 0.8 10 -8 -7 -0 -5 -4 3 -2 -1 0 Vgs (V) Log([Ca2+])

(a) (b)

Figure 4.4: ((a) Shift if I-V characteristic of a calcium sensor under different concentration, (b)slope of the average Dirac point as a function of ionized calcium concentration, error bar indicates the standard deviation. The sample size is 196 and all measurements are taken at Va, = 300 mV.

Table 4.1: Performance comparison of Ca2 + sensors based ion-selective membranes

Materials Detection Range Sensitivity #of Ref [mV/decade] PEDOT:PSS/Au 0.25mM - 2mM 32.7 i 0.931 6 PEDOT:PSS/Au 1mM - 10mM 18.3 i 1.7 8

Si/SiO 2 /Si3 N4 10uM - 100mM 27 t 1 - Ag/ZnO 100nM - 10mM 29.7 - Graphene/Au 100nM - 100mM 31.7 i 1.4 210 our work

measure under same parameters couple days after the initial test. The average Dirac points was plotted again concentration as shown in Figure 4.5. It is interesting that the Dirac points drift to more negative value over time. Most graphene-based devices tend to shift to the right because water molecule or other surface absorbents in the ambient environment may p-dope the graphene. PVC matrix is fairly hydrophobic thus preventing graphene from interact with water molecule directly. It seems like there is little or no correlation between the magnitude of device drift with respect to time. The error bar is roughly the same, meaning the distribution of the I-V is unchanged. The slopes vary slightly as listed in Table 4.2. The number of working devices also changed in each measurement. This could be due to the poor connection at the top of the chip. The

Au metal was scratched after inserting the chip into the data acquisition system.

54 0.7 a Al 041119 a Al_041619 0.6- A Al_050519 S Al051019

0.5- { 0 0~ 0.4- U (U C 0.3- 4~~ ~t-

0.2-

0.1 i , . , -9 -8 -7 -6 -5 -4 -3 -2 -1 Log(concentration)

Figure 4.5: Ca2+ sensor behaviors at different period of time. All measurements were taken using the same chip under same bias Vg, = - 0.4 to 0.9V, Vds = 300 mV

Table 4.2: Stability of Ca2+ sensor

Time #of devices Sensitivity Conc. Range [mV/decade] Day 1 210 -31.8 i 1.73 5 Day 5 196 -31.7 i 1.38 7 Day 24 190 -39.0 i 3.51 6 Day 30 198 -30.4 + 1.71 8

Device drift is a common issue even with the most sophisticated sensors. Some of the reasons include temperature variations, ion migration within the gate insulator, electrochemical non-equilibrium at interface, slow surface effects and so on [28, 63, 27]. In practice, a calibration process similar to Figure 4.4(a) needs to be performed prior to any measurement. Concentrations are measured by characterizing the entirety of the I-

V characteristic and relating the shift to changes in concentration. The voltage sweeps required for full I-V characterization, however, are very slow (e.g. 10 mV / 500 ms) in order to provide adequate time for ions to migrate and for the sensor to reach steady state. In future work, it is important to come up with innovative ways to mitigate the

55 drift issue or to improve the calibration scheme.

4.4 Na+ ion sensor

The sodium ISM was made by mixing 0.661g of 2-nitrophenyl octyl ether (oNPOE), 0.33 g of high molecular weight (PVC), 0.007 g of Na X ionosphere, and 0.002 g of potassium tetrakis(4-chlorophenyl) borate. The mixture was dissolved in 6 mL of tetrahydrofuran

(THF). Similar to the calcium ISM, The solution was then spin coated over the array at

1500 rpm for 120 seconds and allowed to air dry. Similar measurements were performed with different concentration of NaCl solution. A shown in Figure 4.6, the sensitivity of sodium sensor is -56.7 mV/decade with a standard deviation of 5.87. The sensitivity is comparable to other sodium ISM based sensor in literature [20, 37] but still lower than the theoretical sensitivity for such ion (-60 mV/decade). This sub-Nernstian behavior could be due to the un-optimized ionophore/anionic site ratio or the unstable interface between graphene and the membrane. In total 213 sensors are functional but the spread of sensitivity is larger than that of the calcium sensor array.

250-

200-

=-56.8mV/dec E .j Slope

C 5 100 --

50-

-50-

-5 -4 -3 -2 -1 Log([Na+])

Figure 4.6: Shift of average Dirac point as a function of ionized sodium concentration, error bar indicates the standard deviation. The sample size is 213 and all measurements are taken at Vd, = 300 mV.

Transient response was investigated here by dipping the graphene Na+ sensor array

56 in dilutions of ionized sodium spanning several orders of magnitude. The graphene Na+ sensor array was immersed and then measured in each dilution for approximately 20-30 seconds. The experiment started with the lowest ionized calcium concentration to reduce the potential of altering the solution concentrations due to cross contamination. Then the ionized calcium concentration was increased by an order of magnitude for each step. Once the maximum concentration was reached, the sensor array was repeatedly exposed to lower concentrations to demonstrate reversibility. Exposure to decreasing concentrations poses greater risk of cross contamination. The graphene Na+ transient response is depicted in

Figure 4.7 over 5 orders of magnitude change in ionized sodium concentration. Spikes in data represent transition times during which the sensors were transferred from one solution to the next. The measure source-drain current Ids was converted into conductance G and then normalized with respect to the mid-range concentration (1 mM). The source-drain voltage Vd, was kept unchanged throughout the transient measurement (300 mV). The gate was biased at -0.1V, for which the I- V curves are mostly linear for all concentration.

-02 0

-03

0 100 200 300 400 500 600 700 Time (s)

Figure 4.7: Graphene Na+ sensor conductance transient response to changing concentrations in ionized sodium. The sample size is 213 and the bias conditions are Vs=300 mV, VGS=-O.l V. Data is normalized with respect to the response at 1 mM of each sensor.

The transient response experiment shows the sodium sensors exhibit excellent re-

ii versibility. This is a key feature because it enables sensors to be used to continually monitor varying concentrations. The percentage difference between forward (increasing

Na+ concentration) and backward slope (decreasing Na+ concentration)is used to quantify reversibility. The averaged sensitivity and reversibility of the sensor current response is depicted in Figure 4.8(a) and the reversibility of each individual devices is shown in Figure

4.8(b). The average Aslope is 4.66%. By averaging over 213 devices, the reversibility can be increased from less than 80% to almost 94%.

20- forwad * 0 backward

101-

-20-

-30

-405

Log([Na+]) -30 X -10 0 1-2 X ri

(a) (b)

Figure 4.8: (a)Mean sensitivity response showing excellent sensitivity and reversibility over several orders of magnitude change in ionized sodium concentration. The percentage change in conductance is normalized with respect to the data taken at 1 mM and the error bars are given by the standard deviation. (b) Histogram shows reproducibility distribution of 213 individual devices. X-axis is the percentage difference between the fitted slope of increasing sodium concentration and that of decreasing sodium concentration.

4.5 K+ ion sensor

The potassium ISM was made by mixing 0.661g of 2-nitrophenyl octyl ether (oNPOE),

0.33 g of high molecular weight PVC, 0.01 g of valinomycin, and 0.005 g of potassium tetrakis(4-chlorophenyl) borate. The mixture was dissolved in 6 mL of tetrahydrofuran

(THF). The solution was then spin coated over the array at 1500 rpm for 120 seconds and allowed to air dry. We have 193 working sensors for this particular chip. Similar measurements were performed with different concentration of KC1 solution. As shown in Figure 4.9, the sensitivity of potassium sensor is -52.8 mV/decade with a standard

58 deviation of 2.91. The theoretical sensitivity for potassium ion is -60 mV/decade. Other researchers have demonstrated ISM based potassium sensor with sensitivty range from 54 to 58 mV/decade [37, 64, 4]. One reason for the variation could be that valinomycin is a temperature-sensitive molecule. It should be kept under 8 °C in order to be active. The device was kept in room temperature before and after measurements. Device stability test also confirm this hypothesis. A week after the initial test, the sensitivity decreased to ~

30 mV/decade. Two weeks after, the device become insensitive to changes in potassium concentration.

500-

450-

400 - E 350-

300 slope =-52.78mV/dec 250 --

200 -

150-

-7 -6 -5 -4 -3 -2 -1 Log([K+])

Figure 4.9: Graphene K+ sensor response with change in potassium ion concentration. The sample size is 193 and the bias conditions are Vda=300 mV

The conductance sensitivity and reversibility of potassium sensor array is shown in

Figure 4.10 (a). The source-drain voltage VDS was kept at 300 mV throughout the transient measurement and the gate was biased at 0 V. In Figure 4.8(a), the conductance changes linearly with sodium concentration. For potassium however, the relationship between conductance and concentration is non-linear. This is because the linear region of I-V curves in this case is smaller. At low concentration, the Dirac points are further away from 0V as shown in 4.10 (b). If we take a slice at VGS = OV, it is out of the linear

59 region for the some of the devices. As the concentration increases, the I-V curves shift to the left. The slice at VGS = OV is more likely to be in the linear region. This could explains the non-linearity in the reversibility curve. If the transient measurements were done with VGS = 0.1V, the conductance sensitivity should be more linear.

'1 0 -

16 10- 1 Backwardl 14 ~ u 0 - 12j

'.10

0.8 -20-

06 -30 6

04 -40

.06 -04 -02 0 0804 18 Log([K+]) (a) (b)

Figure 4.10: (a)The non-linearity in change in conductance with different potassium concentration for K+ sensor array. (b)I-V characteristics for the K+ sensors under 1 pM KCl solution. The black dash-line indicates the slice of Id, at V,, = OV.

4.6 Integration of multiple ion sensor

Table 4.3: Summary of Ion Sensors Performance

Ca2+ Sensor Na+ Sensor K+ Sensor Sensitivity [mV/decade] -31.7 t 1.4 -56.8 ± 5.87 -52.8 2.91 Detection Range 100nM - 100mM 10pM - 100mM 10pM - 100mM Reversibility 10.0% 4.66% 17.6%

We have demonstrated graphene-based sensor arrays with excellent performance for ionized calcium, sodium and potassium detection. A summary of sensor behavior is listed in Table 4.3. The next step is to integrate all three ISMs onto one chip in order to achieve multiplex sensing. We first tested the selectivity of the membrane. Shown in Figure 4.11 is the sensor response of two chips with change in ionized sodium concentration. One chip has sodium ISM and the other one has calcium ISM. Na ISM shows a near-Nernstian sensitivity while Ca ISM has a much lower slope (<25mV/decade). This indicates ISM

60 4

has good selectivity and different types of ISM will behave differently in the same solution, which makes it possible for a classification analysis.

300 -W.-Na Ism 250 -e-Ca ISMI 2011

>50 E

0 0L

-0

-5 -4 -3 -2 -1 0 Log([Na+])

Figure 4.11: Sensor response with change in sodium concentration. Black line is for sensors with calcium ISM and red line is for sensors with sodium ISM. The difference in slope indicates good selectivity

One challenge is to deposit three types of ISM with in a small sensing area (<5mm x 5mm). We tried to use PIPO Pplse Jetting 3D printer to print the membranes. Un- like other inkjet-based 3D printer, this tool is able to print medium- to high- viscosity materials with high precision. The deposit volumes can range from 3nL to 200nL with a dispensing frequencies up to 150Hz. The shape and size of printed ISM can be controlled by parameters like pressure, stroke, pulse, valve open and close time, voltage and frequency etc. By carefully tuning the parameters of the printer, we are able to print ISM dots as small as 200 pm. We decided to print three lines across the array instead of dots on each devices. Shown in Figure 4.12 (a) is the membrane printed into three lines on the sensing area. The thickness of each line is roughly 700 pm. The control parameters are: pressure 20 psi, cycle 2ms, count 250, stroke 35%, open time 0.25ms, close 0.2ms,voltage

100V.

61 1 p

p1 I I 0I p ~rr P1 p. p p- (a I I ':5 ri rl V 11 r12200r

I 'U j p p. p I

(b)

Figure 4.12: (a) Printed ISM on graphene sensor array chip. (b) Proposed chip design for multiplexed sensor array.

The spacing between each printed line is about 300 pm. This leaves more than 35% of the device un-functionalized. In order to maximize the usage of sensor devices, a new layout is proposed as shown in Figure 4.12 (b). The array is divided into 3 separate region. Each region have 80 devices with the spacing in-between is 450 Pim. Three types of ISMs can be printed on each designated region. It is important to make sure that

ISMs are well-separated since the ionophores are mobile inside the PVC matrix. Suppose

Ca ionophores migrate into the Na ISM region, the Na ISM would have an increase in calcium sensitivity thus loses its selectivity toward ionized sodium.

4.7 Conclusion

We have developed a novel sensor platform that can simultaneously measure over 200 graphene-based multiplexed sensors to characterize the concentration of sodium, calcium and potassium ions in aqueous phase. This technology could have many applications in sports training and health monitoring. We have demonstrated working sensors for detection of calcium, sodium and potassium with sensitivities of 31.7, 56.8 and 52.8 mV/dec,

62 respectively. The sensors also showed high selectivity and reversibility. We experimented with jetting 3D printer to deposit different types of ISMs within 100s of pm. A new chip design was fabricated to integrate different ISMs on one chip to realize multiplexed sensing of ions in electrolyte.

63 Chapter 5

MoS 2-based Sensors

In the previous chapter we studied graphene-based sensor technology. In this chapter, we would like to apply MoS 2 's semiconducting properties to develop sensors with bet-

ter performance. The natural bandgap of MoS 2 gives it the ability to operate in the subthreshold region thus improving sensor sensitivity. In addition, if the subthreshold region is near OV, the change in current modulation will be exponential upon change in analyte concentration. In this case, a two-terminal device configuration is sufficient for detection, as opposed to the normal three-terminal device as discussed before for the case of graphene. By only measuring current (or resistance) instead of doing a full I-V sweep, the data acquisition time can be greatly reduced. This measurement scheme is easier to implement into in-home healthcare monitoring system. In the following section, we will describe pH sensing device which was developed usingMoS 2.

5.1 Device Structure

The device structure for the proposedMoS 2-based sensor is shown in Figure 5.1. The silicon substrate is cleaned with Nanostrip and diluted HCl. A layer of Ti/Au (5 nm

/ 90 nm) was deposited using electron beam deposition as the gate contact. 20 nm of

HfO2 was deposited at 150 °C as back-gate dieletric using atomic layer deposition. The oxide was patterned using standard photolithography process and then etched with BC13 plasma. CVDMoS 2 coated with Poly(methyl methacrylate) (PMMA) was immersed in

64 -j

DI water in order to delaminate the film from the growth substrate. The film was then scooped onto the target substrate and dried carefully with nitrogen gun. The device was baked at 50°C for 1 hour and 150°C for 30 minutes to further dehydrate the transferred film. The device was then immersed in acetone overnight to remove the PMMA. MoS 2 film was patterned using PMGI/SPR700 resist stack as discussed in Section 2.1 and then etched with low power oxygen plasma. Source and drain metal was also deposited using electron beam deposition using a Ni/Au stack (20nm/80nm). A thin layer of aluminum oxide passivation layer was deposited on top of the device using an Al seeding layer as mentioned before. Part of the passivation layer was etched using BC13 plasma to expose the metal pads for contact. The detailed process flow is listed in the appendix.

AcidpH BasepH

QH+OH. Q+1H0- 0- 0- 0- 0- 0- oH 2 H2 1H2 0H 2 OH, Hss AlOx

T Ii

VsGs VC'S

Figure 5.1: Device structure and pH sensing mechanism for MoS 2-based pH sensor

5.2 pH Sensing Mechanism

The operation of MoS 2-based pH sensor is also illustrated in Figure 5.1. Different from the graphene pH sensor mentioned before, where a reference electrode was used to gate the electrolyte, the MoS 2-based sensor here utilizes the back-gate. A typical transfer characteristic is shown in Figure 5.2. The sensing area is exposed to the analyte solution.

The device will be gated at a fixed bias while the current of the MoS 2 channel is measured by sweeping Vd. Depending on the pH value of electrolyte on top, the resistance of the channel will change.

The pH sensing mechanism is based on the protonation and deprotonation of the OH

65 6. Op 100p) Linear Subthreshold Quadratic region 5.0p - region region

4.Op -

~3.Op

2.Op

1.0p

0.0-

-4 -2 0 Vgs [V]

Figure 5.2: MoS 2 Transfer characterstic

groups on the passivation oxide. A solution with acid pH will tend to protonate the OH groups thus leaving positive OH+ surface charges on the oxide. On the other hand, a base pH value will tend to deprotonate the OH groups on oxide surface and generate negative

0- surface charges [61]. The pH-depending surface charge leads to an additional surface potential. Together with the top dielectric, the effective potential on MoS 2 channel due to different pH in the solution can be written as:

== AQ qN Coxide CA1 2 03

where At is the effective potential on MoS 2 channel due to the pH value in electrolyte,

Q is the total surface charge, q is the electron charge, N is number of charged surface group,

willlead to a shift of CA 2 03 is the capacitance of the top oxide. This effective potential threshold voltage and the transfer characteristic. The change in threshold voltage can be expressed as Equation 5.2. Referring back to Equation 3.1, the theoretical value for the

66 effective potential due to pH change is 59.6 mV/decade at room temperature.

AVT AQ _ A (5.2) Coxide CHfO 2

The shift of transfer curve due to change pH can also be quantified by the shift of Ids at a fixed gate bias. Here we define the sensitivity as: S = (Ids-Id,)/Id,. As shown in Figure

5.2, there are three regions in the I- V curves. If we keep the gate bias in the linear region, the I-V characteristic can expressed in Equation 5.3, where gm is the transconductance of the device. The sensitivity can be rearranged as shown in Equation 5.4. It is clear to see that the sensitivity is linearly proportional to the change in threshold voltage. If we bias the device in the quadratic region, the current will change quadratically with respect to 6 VT. The sensitivity S will be modulated quadratically as well. Hence, the sensitivity in quadratic region is expected to be higher than that in the linear region.

Id 9s=(V gS -VT - ") 2 (5.3)

1___-___ -AVT SV IdsV"dVA 2 (5.4) Ids, Vs - VT - Vas/ 2

The Ids - Vg, relationship in subthreshold region can be expressed approximately in

Equation 5.5, where IT is the drain current measured at Vgs = VT under a given V, and SS is the subthreshold swing. If we assume SS is constant, the sensitivity of Ids is modulated exponentially in subthreshold region. Therefore, the sensor response from the subthreshold regime is expected to have higher sensitivity in comparison with those from the linear regimes and quadratic region.

I,= IT * 10 (5.5)

Ids 'dos S ==10 - 1 (5.6)

67 5.3 Sensor Performance

As discussed in Chapter 2, passivation of MoS 2 is necessary to protect contact from electrolyte solution as well as to protectMoS 2 from degradation. One chip containing multipleMoS 2 FETs was cleaved into two pieces to deposit different thickness of oxide passivation. Since the two samples were from the same MoS 2 film and went though the same fabrication process, it is reasonable to assume the two samples should have similar behavior prior to oxide deposition. The pH solutions were prepared using buffer solution pH = 4, 7 and 10. IM NaOH was used to to adjust the pH to desired value. Same pH solutions ranging from pH = 4 to pH = 10 were used for the following test. OV of gate voltage was applied to the devices in order to avoid charge-trap induced hysteresis in

MoS 2 FETs. Vd, was swept from -5mV to 5mV with a sweeping rate of 0.5mV/s. The drain current at Vd, = 2mV was used to analyze the sensitivity. Measurements at pH=

7were used as I' to normalize the data.

Shown in Figure 5.3 (a) is the sensitivity plot of the device with 10nm of A1 20 3 passivation. The change in drain current shows a fairly linear relationship towards change in pH. The fitted slope of the curve is -13% per pH. The sensor is not response below pH = 5. With higher values of pH, more negative surface charge is generated, leading to a reduction of electron concentration in the MoS 2 channel. Therefore, the source-drain current decreases with increase in pH value. Figure 5.3 (b) is the behavior of the device with 30nm of A12 03 passivation. Similar linear relationship is present in this device and the fitted slope is -6.9% per pH. Despite the reduction in sensitivity, detection range for devices with thick oxide is also smaller than that of device with thinner oxide. This is expected since the shift of threshold voltage is related to the oxide capacitance as indicated in Equation 5.2. Since capacitance is inversely proportional the oxide thickness, a thicker

A12 03 passivation means a smaller capacitance hence a smaller VT shift due to pH induced surface potential.

It is also important to note with Vg, = OV, the device is in linear region as shown in Figure 5.2. The sensitivity is lower than literature value of MoS 2 based pH sensors

[38]. If the device is biased in subthreshold region, the sensitivity should increase dras-

68 40- I |

30-

20- 10-

R. 10-

0-0 -10-,0

-20 -5

30-

10 4 5 6 7 8 9 10 pH pH

(a) (b)

Figure 5.3: MoS 2 BFET for pH sensing with (a)10nm A1 2 0 3 and (b) 30nm Al 2O 3 tically. In this work we intentionally avoid adding a back-gate bias during measurement because hysteresis in threshold voltage was observed under those conditions in the transfer characteristic. This hysteresis could be from the poor quality of back-gate oxide as

well as questionable qualityof MoS 2 film. If the subthreshold region is close to zero, we could amplify the sensitivity exponential by adding a gate-bias. On the other hand, if the hysteresis can be reduced, we could apply a negative gate voltage to bias the device into subthreshold region and achieve high sensitivity. However, it would hinder the purpose of building a two-terminal sensors with amplified sensitivity.

5.4 Conclusion

In this chapter we demonstrated a working pH sensor usingMoS 2. Oxide passivation was necessary to preventMoS 2 degradation. However, too thick of an oxide will reduce the device sensitivity as well as the detection range. TheMoS 2 sensor was measured a OV back-gate in order to avoid charged traps in the back-oxide. Additional device optimization needs to be done in order to move the threshold voltage closer to zero.

69 Chapter 6

Conclusion and Future Work

This thesis started with an introduction to 2D materials and how their properties are suitable for development of next-generation sensors. The thesis then moved on to dis- cuss the essential fabrication technology for 2D materials. We examined the cleanness of graphene surface after exposure to common resists as well as the residues removing ability of organic solvents. In addition, a new method to pattern 2D materials was introduced as a faster and more cost-efficient alternative to electron-beam lithography. Various passivation materials and methods including atomic-layer deposited oxide and polymer were also investigated. A passivation layer serves to minimize hysteresis, reduce leakage as well as to protect contact area. In order to improve sensor performance, passivation layer should be tailored and optimize for specific applications.

Next, we discussed the potential of graphene-based electrolyte field-effect transistors for chemical and biological sensing applications. The intrinsic sensitivity of graphene was explored by developing a pH sensor using a graphene electrolyte-gated FET configuration.

A high sensitivity of -58.8 mV/pH was achieved, proving graphene's excellent sensitivity towards environmental changes. To improve the selectivity of graphene-base sensor devices, we immobilized GPCR protein receptors onto the graphene surface and developed a novel CXCL12 ligand sensor. The binding between GPCR and ligand induces a right shift of the I-V characteristics. The ligand sensor response was fitted to Langmuir isotherm and a maximum sensitivity close to 140 mV/dec was obtained. In additional to detection of ligand in solution, the ligand sensor offers an alternative way to study the

70 binding kinetics and extract importance parameters such as binding equilibrium constant

Kd. This technology can be generalized to all the GPCR protein-ligand binding pairs in human body.

Device variability and reproductivity has always been an ongoing issue for graphene- based technology. In this thesis, we developed a graphene sensor array featuring more than 200 working graphene FETs. The response of all the sensors can be measured simultaneously. By utilizing statistical analysis, the accuracy of sensor output can be greatly improved. We used an ion-selective membrane as the functionalization for the graphene sensor array to achieve detection of ionized species in body fluids. Calcium, sodium and potassium sensor arrays were fabricated and calibrated. The sensor arrays showed near-ideal sensitivity, high reversibility and selective. A new chip design was also proposed in order to integrate all three membranes on the same chip to realize multiplexed ion detection.

Last but not the least, MoS 2 was explored as a semiconducting sensing material.

Different thicknesses of oxide passivation were studied in order to determine the optimal passivation layer for MoS 2 -based sensor. A sensitivity of 13% per pH was achieved using back-gated MoS 2 as a pH meter. The reason for low sensitivity was studied and methods to improve sensitivity was also proposed.

Future work in graphene-based ligand sensor should involve a control study of non- binding ligand. In addition, a more detailed model should be constructed to better explain and predict sensor behavior. Other types of sensing mechanism, such as strain due to the structural change of GPCR protein when bind with ligand, should also be analyzed and compared.

More work needs to be done to achieve multiplex ion sensing using the proposed chip layout. More rigorous analysis should be performed to determine the selectivity of each type of ISMs under the presence of interfering ions and under more complex solution background. It would also be beneficial to add some ligand sensors onto the chip.

An on-chip calibration scheme would also be important since the device drift is still an unsolvable issue. In general, the sensor array should be able to detect multiple analytes

71 and biomarkers in complex body fluid such as sweat, tear and saliva.

MoS 2 showed great potential in amplifying a sensor signal. The device structure as well as fabrication process needs to be optimized so that the subthreshold region is close to zero with a sharp subthreshold swing. Once a high performanceMoS 2 FET is successfully demonstrated, we would try to develop anMoS 2-based sensor array similar to the graphene one. By only requiring two-terminals while proving high sensitivity, MoS 2 based sensor array has the potential to be a faster, easier and more accurate sensor system.

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82 Appendix A

Standard Photolithography Recipes

• SPR700

- Spin SPR700 at 3000rpm for 30s

- Bake at 95C for 1 min

- Expose on MLA-150 (dose 130)

- Postbake at 115C for1min

- Develop in MF-CD 26 for 75s

- Rinse with DI water and blow dry

* PMGI +SPR700

- Spin PMGI SF5 at 3000rpm for 30 s

- Bake at 160C for 1 min(set to 180)

- Spin PMGI SF5 at 3000rpm for 30sec

- Bake at 160C for 6 mins (set to 180)

- Spin SPR700 and follow SPR700 recipe

" SU-8

- Spread SU-8 2003 at 750rpm for 3s and spin at 3000rpm for 30s

- Bake at 95C for for 3 min

83 - Expose on MLA-150 (1500 dose)

- Postbake at 95C for 5 min

- evelop for 25 sec

- Rinse in IPA and dry

- Hardbake at 180C for 5mins

84 Appendix B

Recipe for graphene-based pH sensor

1. Diesaw wafer into pieces

" Spin SPR700 at 3000rpm for 30s onto 6inch Si/SiO 2 wafer

" Bake at 95C for 60s

" Diesaw into 2cm*2cm pieces

2. Sample cleaning

" Remove PR with acetone, IPA and DI water, blow dy with N2

" Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

3. Metal 1

• PGMI+SPR700 recipe to define liftoff mask.

" Evaporate 5nmTi +100nm Au using electron-beam evaporator

" Immerse in NMP for 3hrs to liftoff, rinse with IPA and blow dry

4. Sample cleaning

" Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

* 02plasma for 30 mins

5. Graphene transfer

85 " Dip the transfer film into DI water slower so that graphene film float on top of

the water

" Scoop out graphene film with sample substrate

" Blow dry carefully with N2 gun to remove water between the film and substrate

" Bake sample at 60C on hotplate for 40 mins

" Anneal sample at 150C on hotplate for 1hour

" Put sample in acetone overnight to remove sacrificial layer

" Rinse with acetone, IPA and blow dry with N2 gun

6. Define graphene channel

o PMGI+SPR700 recipe to define etch mask

• 02 plasma 30W for 30s to etch Graphene

• UV flood exposure for 65s

o Bake at 115C for 1min

o Develop in MF-CD26 for 70s to remove resist

o Clean sample in NMP overnight

7. Metal 2

" PGMI+SPR700 recipe to define liftoff mask

" Evaporate 5nmTi +150nm Au

" Immerse in NMP for 3hrs to liftoff, rinse with IPA and blow dry

8. SU-8 passivation

o SU-8 recipe to define openings for graphene channel

86 Appendix C

Recipe for graphene-based ligand sensor

1. Diesaw wafer into pieces

* Spin SPR700 at 3000rpm for 30s onto 6inch Si/SiO 2 wafer

" Bake at 95C for 60s

" Diesaw into 2cm*2cm pieces

2. Sample cleaning

" Remove PR with acetone, IPA and DI water, blow dy with N2

" Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

3. Metal 1

" PGMI+SPR700 recipe to define liftoff mask

" Evaporate 5nmTi +100nm Au using electron-beam evaporator

" Immerse in NMP for 3hrs to liftoff, rinse with IPA and blow dry

4. Sample cleaning

9 Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

* 02 plasma for 30 mins

87 5. Graphene transfer

" Dip the transfer film into DI water slower so that graphene film float on top of

the water

• Scoop out graphene film with sample substrate

" Blow dry carefully with N2 gun to remove water between the film and substrate

" Bake sample at 60C on hotplate for 40 mins

" Anneal sample at 150C on hotplate for lhour

" Put sample in acetone overnight to remove sacrificial layer

" Rinse with acetone, IPA and blow dry with N2 gun

6. Define graphene channel

o PMGI+SPR700 recipe to define etch mask

• 02plasma 30W for 30s to etch Graphene

o UV flood exposure for 65s

o Bake at 115C for 1min

o Develop in MF-CD26 for 70s to remove resist

o Clean sample in NMP overnight

7. Metal 2

" PGMI+SPR700 recipe to define liftoff mask

" Evaporate 5nmTi +150nm Au

" Immerse in NMP for 3hrs to liftoff, rinse with IPA and blow dry

8. SU-8 passivation

o SU-8 recipe to define openings for graphene channel

9. Self-assembly of slayer

88 " immerse device into slayer containing solution overnight

" rinse device with DI water and then immerse device in DI water overnight

" drop GPCR containing solution onto the sensing area and wait 3 hour in humid

environment

89 Appendix D

Recipe for graphene-based sensor array

1. Sample cleaning

" clean glass substrate with acetone, IPA and DI water, blow dy with N2

" Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

2. Metal 1

* PGMI+SPR700 recipe to define liftoff mask

" Evaporate 5nmTi +150nm Au using electron-beam evaporator

" Immerse in NMP for 3hrs to liftoff, rinse with IPA and blow dry

3. Sample cleaning

" Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

" 02plasma for 30 mins

4. Inter-layer oxide

* ALDA1 2 0 3 process at 120C for 300 cycles

* define oxide etch pattern with SPR700 recipe

" etch oxide with BC13plasma for 400s

90 " remove oxide etch with acetone, IPA and DI water, dry with N-2 gun

" clean sample with02 plasma

5. Metal 2

" PGMI+SPR700 recipe to define liftoff mask

" Evaporate 5nmTi +100nm Au using electron-beam evaporator

" Immerse in NMP for 3hrs to liftoff, rinse with IPA and blow dry

6. Graphene transfer

" Dip the transfer film into DI water slower so that graphene film float on top of

the water

" Scoop out graphene film with sample substrate

" Blow dry carefully with N2 gun to remove water between the film and substrate

" Bake sample at 60C on hotplate for 40 mins

" Anneal sample at 150C on hotplate for 1hour

" Put sample in acetone overnight to remove sacrificial layer

" Rinse with acetone, IPA and blow dry with N2 gun

7. Define graphene channel

" PMGI+SPR700 recipe to define etch mask

" 02plasma 30W for 30s to etch Graphene

" UV flood exposure for 65s

" Bake at 115C for1min

" Develop in MF-CD26 for 70s to remove resist

" Clean sample in NMP overnight

8. SU-8 passivation

o SU-8 recipe to define openings for graphene channel

91 9. Deposition of ISM

" prepare ISM solution

" spin ISM solution at 1500rpm for 2mins

" dry ISM in N 2 box for 24hrs

" pre-condition device in IM electrolyte before measurement

92 Appendix E

Recipe for back-gated MoS 2 device

1. Diesaw wafer into pieces

" Spin SPR700 at 3000rpm for 30s onto 6inchSi/SiO2wafer

* Bake at 95C for 60s

" Diesaw into linch x linch pieces

2. Sample cleaning

* Remove PR with acetone, IPA and DI water, blow dy with N2

" Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

3. Gate metal

o PGMI+SPR700 recipe to define liftoff mask

o Evaporate 5nmTi +45nm Au using electron-beam evaporator

o Immerse in NMP for 3hrs to liftoff, rinse with IPA and blow dry

o Clean sample with nanostrip (5mins) ,1:3 HCl (5mins) and DI water

* 02 plasma for 30 mins

4. Back-gate dieletric

o Deposit 20 nm ALD HfO2, (HFO 150C, water, 200 cycle)

93 " SPR700 recipe to define etch mask

* Etch HfO2 (BCl3 200s, NGANETCH)

" Remove resist with acetone, IPA and DI water, blow dry

5. MoS 2 transfer

" Spin PMMA A6 onto MoS2/SiO2 sample at 2000rpm for 30s

" Clean edges with Q-tip and acetone

" Delaminate MoS2/PMMA on water

" Pick up PMMA/MoS2 membrane with chip

" Blow dry and bake at 50C for 1hr

" Bake at 150C for 5 mins

6. DefineMoS 2 channel

" PMGI+SPR700 recipe to define etch mask

" Asher 800W for 3mins to etch MoS2

* UV flood expose for 65s

" Bake at 115C for1min

" Develop in MF-CD26 for 70s to remove resist

" Clean sample in room temperature NMP for 3hrs

7. S/D metal

* PMGI+SPR700 recipe to define liftoff mask

" Evaporate 20nmNi +80nm Au

• Immerse in NMP for 2hrs to liftoff, rinse with IPA and blow dry

8. Oxide passivation

o Evaporate 2nm of Al (0.1A/s)

94 " Bake at 150C for 10mins on a hotplate

* Deposited HfO2 (HFO 150C, water, 200 cycle)

" SPR700 recipe to define etch mask

" Etch Hf02 (BCl3 300s, NGANETCH)

* Remove resist with acetone, IPA, blow dry