LIFETIME AND DEGRADATION STUDIES OF POLY (METHYL METHACRYLATE) (PMMA) VIA DATA-DRIVEN METHODS
by
DONGHUI LI
Submitted in partial fulfillment of the requirements
For the degree of Doctor of Philosophy
Department of Materials Science and Engineering
CASE WESTERN RESERVE UNIVERSITY
May, 2020 Lifetime and Degradation Studies of Poly (Methyl Methacrylate)
(PMMA) via Data-driven Methods
Case Western Reserve University Case School of Graduate Studies
We hereby approve the thesis1 of
DONGHUI LI
for the degree of
Doctor of Philosophy
Dr. Roger H. French
Committee Chair, Adviser 03/17/2020 Department of Materials Science and Engineering
Dr. Laura S. Bruckman
Committee Member 03/17/2020 Department of Materials Science and Engineering
Dr. Mark R. De Guire
Committee Member 03/17/2020 Department of Materials Science and Engineering
Dr. Michael J. A. Hore
Committee Member 03/17/2020 Department of Macromolecular Science and Engineering
1We certify that written approval has been obtained for any proprietary material contained therein. To my parents, Jinxing Li & Xuemiao Chen, and my sister, Dongjie Li. Without them, none of this would’ve been possible. Table of Contents
List of Tables vi
List of Figures vii
Acknowledgements xvi
Abstract xvii
Abstract xvii
Chapter 1. Introduction1
PMMA: Applications and Degradation1
Lifetime and Degradation Science: Applicability to Polymers2
Thesis Overview3
Chapter 2. Literature Review5
PMMA and Its Applications5
Long-term Durability of Polymers: PMMA6
How to study polymer degradation scientifically 16
Photostabilization of Polymers: PMMA 21
Data-driven Lifetime Study 22
Statistical Learning Methods in Weathering Studies: State of Art Modeling 29
Chapter 3. Experimental 1 38
PMMA formulations 38
Exposures 38
Evaluations 41
Urbach analysis of absorption edges and Lorentzian fitting 42
iv Chapter 4. Results 45
Stabilizers in baseline samples of the 6 grades of PMMA 46
Yellowness Index and Haze 49
Absorbance and Induced Absorbance to Dose 57
Urbach fit results for 6 grades of PMMA 71
Self-organizing map as a tool to explore degradation patterns in 6 grades of PMMA 74
Chapter 5. Discussion 83
Urbach edge analysis 83
The effect of exposure types on degradation rate 85
Comparison of the durability of the 6 grades of PMMA 86
Degradation pathway models of PMMA 87
Chapter 6. Conclusion 90
Appendix A. Preparation of this document 92
Appendix B. Figures 93
Absorbance data for highly stabilized formulations 93
GC-MS data of the 6 grades of PMMA 94
Pairs plots for UVT and FF1 sample exposed in Hot QUV. 98
Appendix. Complete References 100
v List of Tables
2.1 General properties of unfilled PMMA.1 6
3.1 Exposure based on ASTM G154 and ASTM G155 standards 40
3.2 Spectral characteristics 40
4.1 Additives information for PMMA in Hot QUV at 0 hour. The ’-’ stands
for that the stabilizer was not detected and the ’?’ stands for the
stabilizer should be in formulation based on UV-Vis spectroscopy but
was not detected by GC/MS. 46
4.2 Additives information for PMMA in Hot QUV at 0 hour with brand
name and label for self-organizing map. The ’-’ stands for that the
stabilizer was not detected and the ’?’ stands for the stabilizer should
be in formulation based on UV-Vis spectroscopy but was not detected
by GC/MS. 75
5.1 Urbach edge fit parameters for PMMA in Hot QUV at 0 hour 84
5.2 Urbach edge fit parameters for UVT PMMA in Hot QUV 84
5.3 Urbach edge fit parameters for FF1 PMMA in Hot QUV 85
vi List of Figures
2.1 Poly(methyl methacrylate) (PMMA) and monomer methyl
methacrylate (MMA) structure5
2.2 The cause-and-effect diagram of PMMA degradation: the diagram
is like a fish’s skeleton with the problem, the performance loss of
PMMA, at the head and the causes for the performance loss feeding
into the spine. General performance loss includes discoloration,
embrittlement and haze formation.8
2.3 Decomposition of the ester group: ester groups on the side chain of
PMMA absorbing light and the degradation of ester group involves
three reaction. Reaction (1) is the most important process/2. 10
2.4 Main chain scission: a β-scission with formation of tertiary alkyl
radical 11
2.5 PMMA:Disproportion reaction by hydrogen abstraction 11
2.6 Mechanisms for the generation of the secondary alkyl radical by
the hydrogen abstraction reaction of backbone with methyl formate
radical 11
2.7 PMMA main chain degradation: structure (1) and (2) are the results of
homolytic main chain scission. The primary radical (2) can abstract
hydrogen from the main chain to form a secondary radical (3). 12
2.8 Main chain radical termination 12
2.9 Degradation of PMMA in the presence of oxygen: the three types of
alkyl readicals react rapidly with oxygen to generate peroxyl radicals.
vii The CH , secondary and tertiary alkyl radicals react with oxygen to · 3 yield peroxyl radicals3 13
2.10 Termination of peroxyl radical happens when at least one of the
peroxyl radical is primary or secondary. The termination undergoes a
Russell-type reaction to generate a ketone or aldehyde with an alcohol 14
2.11 Alkoxyl radicals generated by non-terminating reaction of two tertiary
peroxyl radicals 14
2.12 The β-scission of Alkoxyl radicals 14
2.13 Monomer reactions: 3 pathways to degradation, where I is the free ∗ radical initiator generated from the degradation of the monomer
and M and M represent the propagating radical and monomer ∗ respectively. 15
2.14 The monomer residual’s effect on degradation: reaction to generate
tertiary alkyl radical 15
2.15 Spectral power distributions (280-4000nm) of The ASTM G173-03 AM
1.5, fluorescent UVA-340 and xenon arc full spectrum light sources 19
2.16 Spectral power distributions (280-400nm) of The ASTM G173-03 AM
1.5, fluorescent UVA-340 and xenon arc full spectrum light sources 20
2.17 Photostabilizations in photooxidative process 21
2.18 Data-driven method to study the durability of polymer materials 22
2.19 FTIR spectra for extruded PMMA sample. 24
2.20 Block diagram for Gas chromatography–mass spectrometry using
electron ionization for collection of mass spectrum. 25
viii 2.21 CIE color space, top view 27
2.22 Spectrophotometer for haze measurement. 28
2.23 The Jablonski diagram for fluorescence. 30
2.24 Computational environment for data analysis: several open source
software tools and R packages for visualization and spectral analysis. 30
2.25 An artificial network with 3 layers: input layer, hidden layer, and
output layer 32
2.26 Training process of a self-organizing map 32
2.27 An example of structural equation model. Boxes contain variables
that can be measured in the data. Circles contain variables that
cannot be measured. Residuals and variances are drawn as double
headed arrows pointing into an object. 36
2.28 An example of path modeling 36
2.29 An example of network modeling for yellowness index prediction with
induced absorbance to dose as mechanism variable. 37
3.1 The exposure routes for the degradation study of the 6 grades of
PMMA. 41
4.1 Stabilizers in the 6 grades of PMMA detected by GC/MS 48
4.2 The optical absorbance for UV absorbers4. 49
4.3 Yellowness index data for the stablilizer-free PMMA (UVT). 50
4.4 Yellowness index data for the 6 grades of PMMA. 50
4.5 Yellowness index data for the 5 grades of PMMA without UVT. 52
ix 4.6 Haze data for the 6 grades of PMMA. 52
4.7 Haze data for the 6 grades of PMMA exposed in QSUN. 53
4.8 Haze data for the 6 grades of PMMA in Hot QUV and Cyclic QUV. 53
4.9 Surface morphology of UVT samples at 0 hour 55
4.10 Surface morphology of UVT samples at 3200 hours exposure in QSUN. 55
4.11 Surface roughness of irradiated side (front) and non-irradiated side
(back) in QSUN 56
4.12 ∆E data for the 6 grades of PMMA. 56
4.13 The panel plots for the baseline sample without any exposure.
Each panel has 8 UV-Vis spectral data of samples. At baseline
measurement, all the panel plots in one column should have similar
results, indicating each grade of PMMA has low samplt-to-sample
variation. 58
4.14 The process of quantification of degradation rate by induced
absorbance to dose calculation: UVT exposed in Hot QUV as an
example. 59
4.15 Abs/cm spectra for UVT under Hot QUV exposure. 60
4.16 Induced absorbance to dose (IAD) spectra for UVT under Hot QUV
exposure. 60
4.17 Abs/cm spectra for UVT under Cyclic QUV exposure. 61
4.18 Induced absorbance to dose (IAD) spectra for UVT under Cyclic QUV
exposure. 61
4.19 Abs/cm spectra for UVT under QSUN exposure. 62
x 4.20 Induced absorbance to dose (IAD) spectra for UVT under QSUN
exposure. 63
4.21 Abs/cm spectra for FF1 under Hot QUV exposure. 65
4.22 Induced absorbance to dose (IAD) spectra for FF1 under Hot QUV
exposure. 65
4.23 Abs/cm spectra for FF1 under Cyclic QUV exposure. 66
4.24 Induced absorbance to dose (IAD) spectra for FF1 under Cyclic QUV
exposure. 66
4.25 Abs/cm spectra for FF1 under QSUN exposure. 67
4.26 Induced absorbance to dose (IAD) spectra for FF1 under QSUN
exposure. 67
4.27 Abs/cm spectra for UVO under Hot QUV exposure. 68
4.28 Induced absorbance to dose (IAD) spectra for UVO under Hot QUV
exposure. 68
4.29 Abs/cm spectra for UVO under Cyclic QUV exposure. 69
4.30 Induced absorbance to dose (IAD) spectra for UVO under Cyclic QUV
exposure. 69
4.31 Abs/cm spectra for UVO under QSUN exposure. 70
4.32 Induced absorbance to dose (IAD) spectra for UVO under QSUN
exposure. 70
4.33 Urbach fit analysis of neat samples without exposure 71
4.34 Urbach fit analysis of UVT without exposure 72
xi 4.35 Lorenzian fit of the absorbance of residual monomer. 72
4.36 Urbach fit analysis of UVT in Hot QUV 73
4.37 Urbach fit analysis of FF1 in Hot QUV 73
4.38 The excitation-emission matrix fluorescence spectra of samples from
brand A. 76
4.39 The excitation-emission matrix fluorescence spectra of samples from
brand B. 77
4.40 The removal of scatter from the excitation-emission matrix
fluorescence spectra 77
4.41 The U-matrix (left) and self-organized map (right) of 30 excitation-
emission matrix fluorescence spectra of Hot QUV samples. Sample
distribution was labled on the SOM map. Labelling used (e.g. 1a0 -
UVT sample without any exposure at step 0 (0 hour exposure); 1d5 -
UVT sample at step 5 (3200 hours exposure)) 78
4.42 Hit histograms: single hit histogram (a), multiple hit histograms for
UVT (b), FF1 (C), and UVA(d) from brand A. Red color - samples
exposed 3200 hours in Hot QUV; Green color - baseline samples
without any exposure; Yellow color - samples exposed at 800 hours,
1200 hours, 2200 hours. 78
4.43 Hit histograms: single hit histogram (a), multiple hit histograms for
Optix (b), UV0 (C), and UVF(d) from brand B. Red color - samples
exposed 3200 hours in Hot QUV; Green color - baseline samples
xii without any exposure; Yellow color - samples exposed at 800 hours,
1200 hours, 2200 hours. 79
4.44 Pathway model with mechanistic variables: IAD at 275nm
(corresponding the change of fundamental absorption edge)
and 400nm (corresponding the yellowing chromophores). The
fitting models (Mod) and the adjusted R2 values (adj-R-sqr) for
the relationship of each pair of variables are labeled along the
connection lines. The models for fitting different realtionships are
SL (simple linear), SQuad (simple quadratic), Quad(quadratic),
Exp (exponential), Log (logarithmic), CP (change point), and nls
(non-linear least squares regression) 81
4.45 Pathway model with mechanistic variables: IAD at 275nm
(corresponding the change of fundamental absorption edge),
339nm (corresponding the photobleaching of Tinuvin P) and 400nm
(corresponding the yellowing chromophores). The fitting models
(Mod) and the adjusted R2 values (adj-R-sqr) for the relationship
of each pair of variables are labeled along the connection lines.
The models for fitting different realtionships are SL (simple linear),
SQuad (simple quadratic), Quad(quadratic), Exp (exponential), Log
(logarithmic), CP (change point), and nls (non-linear least squares
regression) 82
5.1 Induced absorbance to dose for UVT and FF1 under 3 exposure
conditions at 275nm, 298nm, 339nm, and 400nm. 86
xiii 5.2 Induced absorbance to dose for UVP, UVO, UVF, and UVA under 3
exposure conditions. 87
5.3 Degradation pathways for UVT and FF1 exposed in Hot QUV.
Regression lines are obtained from pathway modes from netSEM
analysis. The best fit for each relationship is determined based on
adjusted R2 values and domain knowledge. 89
B.1 Abs/cm spectra for UVA under Hot QUV exposure. 93
B.2 Induced absorbance to dose (IAD) spectra for UVA under Hot QUV
exposure. 94
B.3 GC-MS data for UVT sample at baseline and at 3200 hours: sample
18021 is the UVT sample at baseline and sample 17438 si the UVT
sample at 2200 hours exposed in Hot QUV. 95
B.4 GC-MS analysis for UVT sample at baseline and at 3200 hours: sample
18021 is the UVT sample at baseline and sample 17438 si the UVT
sample at 2200 hours exposed in Hot QUV. 95
B.5 GC-MS data for UVT (18021), FF1 (18005), and UVA (18013) samples
at baseline. 96
B.6 GC-MS analysis for UVT (18021, in process), FF1 (18005), and UVA
(18013) samples at baseline. 96
B.7 GC-MS data for UVO (1775), UVP (17643) and UVF (17830) samples at
baseline. 97
B.8 GC-MS analysis for UVO (1775), UVP (17643) and UVF (17830)
samples at baseline. 97
xiv B.9 Pairs plot for UVT. 98
B.10 Pairs plot for FF1. 99
xv Acknowledgements
First, I wish to acknowledge the support and great love of my father, mother, and sister. They kept me going and none of this would’ve been possible without them.
Thanks to my coworkers Chris Bowley, Jessica Powell, Jessica Zhou, Keith
Greenawalt, Kristine Ma and Sheila Velagapudi (from BD) for their support on the monthly trip from Boston to Cleveland.
Thanks to the collaborators Chiara Barretta, Christöfl Petra, and Gernot Oreski (from
Polymer Competence Center Leoben GmbH, Austria) for the measurements of GC/MS and nanoidentation.
Special thanks to my thesis adviser, Roger H. French and thesis committee members
Laura S. Bruckman, Mark R. De Guire, and Michael J. A. Hore, for their valuable guidance in the research.
Additional thanks to my friends Jiqi Liu, Menghong Wang, Wei-Heng Huang, Yu
Wang and Zhe Ren, for all of the support and encouragement.
The research made use of the resources in Solar Durability and Lifetime Extension
(SDLE) Research Center and Sears Think[box] Center for Innovation and Entrepreneur- ship at Case Western Reserve University.
xvi Abstract
Lifetime and Degradation Studies of Poly (Methyl Methacrylate) (PMMA) via Data-driven Methods
Abstract
by
DONGHUI LI
0.1 Abstract
Poly(methyl methacrylate), also know as acrylic, has excellent optical properties, light weight, good mechanical properties, and weatherability. Due to the balance of these outstanding properties with cost efficiency, poly(methyl methacrylate)s are widely used in architecture, medicine, electronics, agriculture, paints, aircraft, and automotive in- dustries. However, the lifetime of poly(methyl methacrylate)(PMMA) is reduced in out- door applications because of exposure to solar radiation, temperature, and moisture.
During the polymer degradation process, a variety of environmental stressors act on the polymer leading to degradation of its properties. Although standardized durability and weathering tests are widely used to collect failure information and evaluate durability of materials based on the typical pass/fail criteria, degradation modes, mechanisms, and rates are not clearly understood. Therefore, a better understanding of degradation modes, mechanisms, and rates is critical.
xvii To optimize and extend the service life of polymer materials to more than 25 years in the outdoor environment, a domain knowledge-based and data-driven approach has been utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms and rates under various stepwise exposure conditions. Six grades of PMMA were studied, including one unstabilized and five stabilized PMMAs exposed 3200 hours in three weathering conditions. The unstabilized PMMA showed a significant YI increase of over 25, whereas a highly-stabilized PMMA showed a slight YI increase only between
0.5 to 0.7. The degradation of unstabilized acrylic, revealed by Urbach edge analysis, arises from the presence of residual MMA monomer, with a shift of absorption edge from 4.35 eV to 3.11 eV under UVA-340 irradiation.
For unstabilized and partially-stabilized PMMA formulations, quantitative degrada- tion rates (the Induced absorbance to dose (IAD)) indicates that the UVA-340 irradi- ation (Hot QUV) shows 3-6 times higher degradation rate than the full spectrum ex- posure (Q-SUN). Photobleaching and photodarkening were both observed during the degradation of the UV-stabilized formulation. Degradation pathway models of unsta- bilized and partially-stabilized in the Stressor Mechani sm Response framework < | | > both demonstrate that IAD275, the tracking variable for the fundamental absorption edge of PMMA, has a strong relationship with both UV dose (Adjusted R2=0.82-0.94)
and YI (Adjusted R2=0.87-0.99). Use of self-organizing map analysis on UV fluorescence
data detected that there are 3 different degradation categories: unstabilized, partially-
stabilized, and highly-stabilized, each with characteristic behaviors.
xviii 1
1 Introduction
1.1 PMMA: Applications and Degradation
Polymer materials are ubiquitous in modern life. Polymers for industrial applications
should have durable performance for versatile applications and be economically friendly1–3,5.
In this regard, one of the most important industrial polymers is the class of resins known
as acrylics. For the most important part of acrylics, the poly(methylmethacrylate)(PMMA)
has the widest application. During 1930s, PMMA was commercialized, and since then
PMMAs have been considered promising materials for industrial applications6. PMMA- based Fresnel lenses are used in the concentrating photovoltaic systems to increase elec- trical output per unit area7–10. PMMA-based coating was used as a protective layer in the building industry as well as other industries, for optical transparency, absence of colour, ease of applicability in a thin coating11,12. In the field of conservation of arts,
Paraloid B72, an acrylic polymer, is extensively used as a coating to protect different sur- faces of arts in museums, but also for stone monuments in outdoor environment13. In the electronics industry, Poly (methyl methacrylate) (PMMA) is used as a positive resist for advanced microlithography14–17. Irradiation of PMMA under electron beams, pro- ton beams, X-rays, or ultraviolet light leads to main chain scission (MCS). This is desired because of the increased solubility in the exposed areas on the PMMA surface. Introduction 2
However, the lifetime of poly(methyl methacrylate)(PMMA) is reduced in outdoor applications because of exposure to solar radiation, temperature, and moisture. Degra- dation of polymers is a complex phenomenon in which many processes can take place successively during the course of decomposition under light, temperature, and mois- ture. Fundamental studies of polymer degradation have been investigated for the devel- opment of more durable polymers and estimation of the lifetime of polymers2,3,18–24.
Although the photo and thermal degradation of PMMA has received considerable attention, little research has explored the relationship bewteen performance loss mech- anisms and different exposure conditions. For PMMA with UV absorbers, durability ex- amination is not publicly available due to proprietary reason for manufactures of PMMA products.
1.2 Lifetime and Degradation Science: Applicability to Poly- mers
To discover the degradation mechanisms from weathering of PMMA, the data-driven methods from data science, an interdisciplinary field of scientific methods to extract knowledge from data, were used. Lifetime and degradation science (L&DS), based on a stressor, mechanism and response framework, has been used to quantify the relation- ship between the environmental stresses to which materials are exposed and the re- sponses observed in the process of degradation accumulation caused by different degra- dation mechanisms in the weathering process. The weathering data records the phys- ical and chemical changes of PMMA under exposure conditions that vary substantially in irradiation, temperature, and moisture. The accelerated exposures are artificially- controlled conditions of temperature, humidity, and solar irradiance as prescribed by Introduction 3
ASTM G154 and G155, with the aim to correlate the results of the different accelerated exposures with the real situations. The physical and chemical degradation of PMMA were monitored specifically by non-destructive meansurements, such as Fourier-transform infrared spectroscopy (FTIR), colorimetry, and UV-Vis spectroscopy.
1.3 Thesis Overview
The focus of this study is the degradation mechanisms, rate, and pathways of PMMA.
In the study, six grades of PMMA with different amounts of UV absorbers were exposed under three indoor accelerated exposures. The physical and chemical property changes of PMMA under aggressive environments are measured by non-destructive measure- ments, such as UV Transmittance, FTIR, and Colorimetry. Degradation pathway models are developed for service life prediction. The predictive model, netSEM (network struc- tural equation modeling) model, was constructed from the colorimetry response. The model is able to predict the lifetime of PMMA through the understanding of the effect of residual monomers and UV stabilizers on degradation by six grades of PMMA.
In this research, a literature review on PMMA degradation and service life predic- tion is presented in Chapter 2. Key studies that are related to the synthesis of PMMA, photodegradtion, thermal degradation, Optical durability, and stabilization of PMMA will be included in the review. In chapter 3, experimental methods including material preparation, exposure conditions, and evaluations are described. Analytical methods including the pathway models are presented after experimental methods. In chapter 4, Introduction 4
results from different evaluations of the 6 grades of PMMA under three accelerated ex- posures are visualized using R language. In chapter 5, discussions on degradation mech- anisms, rate and pathway based on the results in chapter 4 are included. In chapter 6, the overview of the lifetime and degradation studies of PMMA is presented. 5
2 Literature Review
2.1 PMMA and Its Applications
Poly(methyl methacrylate), also know as acrylic, has excellent optical properties, light weight, good mechanical properties, and weatherability. Due to the balance of the out-
standing properties with cost efficiency, poly(methyl methacrylate)s are widely used in
architecture, medicine, electronics, agriculture, paints, aircraft, and automotive indus-
tries25–38 since it was first brought to market in the beginning of the 1930s. Hard, clear
PMMA sheets are made from methyl methacrylate monomer as shown in Figure 2.1.
Methyl methacrylate (MMA) monomer is mainly manufactured by a two step process
Figure 2.1. Poly(methyl methacrylate) (PMMA) and monomer methyl methacrylate (MMA) structure Literature Review 6
in which acetone cyanhydrin (ACH) is produced first by a reaction of acetone and hy- drogen cyanide. Then ACH is heated in the presence of concentrated sulfuric acid and yields MMA monomer after methanolysis. PMMA is made by free radical polymeriza- tion of MMA monomers initiated by peroxides.
To perform the function of PMMA products for which they were designed, PMMA must have the proper molecular weight, composition, thickness, modulus, absorbance and other important characteristics to the particular applications. Key characteristics of
PMMA are provided in Table 2.1. The representative values in the table apply to new and unexposed material.
Type of polymer Amorphous Specific gravity 1.17 Tensile modulus at 22◦C (Mpsi) 0.38 Tensile strength at yield (kpsi) 7.50 Notch Izod impact at 22◦C (ft-lb/in) 0.30-0.50 Thermal limits service temp. (◦C) 87 (short) - 65 (long) Shrinkage (%) 0.3 - 0.6 Glass trans. temperature Tg (◦C) 110 Vicat point temperature (◦C) 84 Melt glow rate (g/10 min) 0.8 - 2.0 Process temperature (◦C) 210 - 300 Mold temperature (◦C) 60 - 87 Drying temperature (◦C) 73 Drying time (h) 2 - 4 Table 2.1. General properties of unfilled PMMA.1
2.2 Long-term Durability of Polymers: PMMA
Polymers are macromolecules composed of many repeated subunits linked by chemi-
cal bonds. Degradation happens during every part of the lifetime of polymers. Polymer Literature Review 7
degradation is a collective name to describe various processes in which a polymer un- dergoes a change in the properties under one or more environmental stresses such as light, heat, or chemicals including water, acid, and some salts. Degradation may occur when polymers are synthesized, engineered, and utilized. Although the different appli- cations require different degrees of durability, there is always a demand for long-term durability. In order to achieve long-term durability, we must understand the degrada- tion mechanism and identify the stresses causing it in an efficient and scientific way.
Polymer materials with impurities and additives (including unpolymerized monomers, oligomers, UV absorbers, light stabilizers, and antioxidants) are subject to degradation under terrestrial solar spectral irradiance with a wavelength longer than 290nm. The
UV-enabled photolysis is the primary degradation mechanism for PMMA products. The temperature and moisture also have an effect on the degradation. Synergy ocurring among the photolysis, thermal degradation and hydrolysis is explicitly identified in the past39–41.
2.2.1 Structure affecting degradation
First and foremost, the properties and lifetime of a polymer depends on its structure.
The essential structure of a polymer is formed by combining monomers that can un- dergo polymerization. In the process of polymerization, monomers are joined by chem- ical bonds to form the structure of polymer chains. The backbone chain of a polymer is the longest series of covalently bonded atoms that together create the continuous chain of the molecule. The side chain extends from the backbone chain of a polymer.
Side chains can have noteworthy influence on the properties of a polymer, mainly its crystallinity and density. The energy of a photon with wavelength = 300nm is around
95 kcal/mol, which is higher enough to dissociate most bonds in polymer chains. All Literature Review 8
Figure 2.2. The cause-and-effect diagram of PMMA degradation: the di- agram is like a fish’s skeleton with the problem, the performance loss of PMMA, at the head, and the causes for the performance loss feeding into the spine. General performance loss includes discoloration, embrittle- ment and haze formation.
the chemical bonds on the backbone chain and sides chains could be the reactive sites where degradation happens under environmental stresses. Among the different atoms or groups linked by chemical bonds, there are always weak parts which will later lead to polymer deterioration. The strength of the bond depends on the connected atoms and the physical and chemical environment of the bond.
2.2.2 Processing initiates degradation
During processing of a polymer, thermal and mechanical degradation may have already happened, and a variety of defects as potential sites of degradation are introduced into polymer structure under high thermal and mechanical stress42,43. Extruded PMMA sheets are manufactured by a continuous production process. The extruder melts and mixes Literature Review 9
the PMMA pellets and transports the melted PMMA into the die machine. Then the die will shape the melted PMMA to a specific thickness.
2.2.3 Environmental stressors leading to degradation
The aim of this section is to improve the understand of degradation processes by degra- dation mechanisms of PMMA exposed to environmental stresses. On the basis of dom- inating features, the most important environment stressors in the use of polymers that cause degradation are radiation, heat, and moisture. Photodegradation is degradation process facilitated by radiation, especially high-energy UV light. Thermal degradation refers to the degradation process of polymer at elevated temperature. Hydrolytic degra- dation concerns the physical and chemical changes facilitated by moisture or humidity.
The complex inter-relationship between different types of degradation processes should be emphasized. In most of cases, different degradation processes happen si- multaneously in outdoor or indoor environments. For example, photodegradation at elevated temperature, i.e., above 150 ◦C., occurs by photochemically-initiation and is followed by extensive depolymerization that is generally observed in the mode of ther- mal degradation18,44,45. Another typical example of inter-relationship between different degradation modes is that the oxidation process happens along with thermal or pho- tolytic degradation. It was reported that thermal oxidation in air reduces the molecular weight of PMMA much faster than thermal degradation in nitrogen atmosphere20,21,46.
2.2.4 The photodegradation process
Decomposition of the side chain of PMMA plays an essential role in the initial stage of degradation2,45. Decompositions of the ester group on the side chain are illustrated in
Figure 2.3. Reaction (1) is the most important mechanism because the bond-cleavage Literature Review 10
Figure 2.3. Decomposition of the ester group: ester groups on the side chain of PMMA absorb light, and the degradation of the ester group in- volves three reactions. Reaction (1) is the most important process2.
process that generates the COOCH is the most efficient one based on quantum yields · 3 data2,47. Reaction (2) and (3) that generate the OCH and CH are relatively slower. · 3 · 3
The polymer radicals generated from Reaction (2) and (3) can eliminate CO or CO2 then generate the tertiary alkyl radical as in Reaction (1). The tertiary alkyl radical can undergo either a β-scission which gives the propagating radical (Figure 2.4) or a dispro- portion reaction (Figure 2.5) which removes the radical48.
If the COOCH and OCH are mobile enough in the glassy matrix, they can abstract · 3 · 3 H from the backbone of polymer chains to yield a secondary alkyl radical as shown in
Figure 2.6. Literature Review 11
Figure 2.4. Main chain scission: a β-scission with formation of tertiary alkyl radical
Figure 2.5. Disproportion reaction by hydrogen abstraction
Figure 2.6. Mechanisms for the generation of the secondary alkyl radical by the hydrogen abstraction reaction of backbone with methyl formate radical.
Random main chain scission could yield three types of radicals as shown in Figure
2.749. The interaction of main chain radicals can lead to termination by hydrogen ab- straction as shown in Figure 2.8. Electron spin resonance has provided evidence on the identity of the radicals generated by main chain degradation.
When PMMA is exposed in the presence of oxygen, tertiary and secondary alkyl rad- icals react with oxygen to yield peroxyl radicals3. The CH can also be peroxidized by · 3 this reaction with oxygen in Figure 2.9.
The interactions between tow peroxyl radicals of different forms have been reported
for both secondary peroxyl radical and tertiary peroxyl radical as shown in Figure 2.9. Literature Review 12
Figure 2.7. PMMA main chain degradation: structure (1) and (2) are the results of homolytic main chain scission. The primary radical (2) can ab- stract hydrogen from the main chain to form a secondary radical (3).
Figure 2.8. Main chain radical termination: disproportionation by hydro- gen abstraction
In this process, tow peroxy readicals terminate without chain scission by a Russell-type interaction.
The interaction between two peroxyl radicals of the same tertiary form occurs in a different non-terminating way to generate an alkoxy radical as shown in Figure 2.10.
This type of reaction plays an important role in accelerating the degradation rate of
PMMA in the presence of oxygen because of the generation of more reactive alkoxyl rad- ical (Figure 2.11). The alkoxy radical can undergo a β-scission and generate a primary alkyl radical (Figure 2.12), which will rapidly become peroxide3. The photooxidation in this way will increase the degradation rate of PMMA. Literature Review 13
Figure 2.9. Degradation of PMMA in the presence of oxygen: the three types of alkyl readicals react rapidly with oxygen to generate peroxyl rad- icals. The CH , secondary and tertiary alkyl radicals react with oxygen to · 3 yield peroxyl radicals3.
The effect of monomer residual on photodegradation has been investigated3,50–52.
The residual monomers in PMMA product have an important effect on the rate of chain scissions in degradation process. Monomers participate in the degradation in several pathways as given in Figure 2.13 and 2.14. It can participate the degradation process and increase the degradation rate by addition to a propagating radical, conversion to Literature Review 14
Figure 2.10. Termination of peroxyl radical happens when at least one of the peroxyl radical is primary or secondary. The termination undergoes a Russell-type reaction to generate a ketone or aldehyde with an alcohol
Figure 2.11. Alkoxyl radicals generated by the non-terminating reaction of two tertiary peroxyl radicals
Figure 2.12. The β-scission of Alkoxyl radicals peroxyl radical by reaction with oxygen, and addition to a peroxyl radical. It is known that conversion to peroxyl radical by reaction with oxygen is has the highest kinetic con- stant53,54. Literature Review 15
Figure 2.13. Monomer reactions: 3 pathways to degradation, where I is ∗ the free radical initiator generated from the degradation of the monomer and M and M represent the propagating radical and monomer respec- ∗ tively.
Figure 2.14. Monomer residual’s effect on degradation: reaction to gen- erate the tertiary alkyl radical
2.2.5 Thermal degradation
Thermal degradation has been subject to investigation for a long time55–57. The initia- tion of thermal degradation is mainly caused by the double-bonded chain-end during the random chain scission. Initiated by the random chain scission, the process of ther- mal degradation consists of the unzipping reaction with the formation of monomers.
Unzipping reaction is a successive process of release of monomer from the polymer chain once the random chain scission has been initiated58,59. Termination reaction may occur when the unzipping radical interacts with surrounding radicals or species to form a more stable end-group. Literature Review 16
The generally used methods to study the thermal degradation are thermalgravimet- ric analysis and thermal volatilization analysis. Several studies have demonstrated that there are multiple components in PMMA that may affects the thermal degradation pro- cess20,21,60–66. Degradation at different elevated temperature can be initiated mainly by residual monomer3,50, residual initiator50,67, unsaturated end-groups65,68 and random chain scission59.
2.3 How to study polymer degradation scientifically
To optimize and extend the service life of polymer materials to more than 25 years in the outdoor environment, a better understanding of degradation modes, mechanisms, and rates is critical. During the polymer degradation process, a variety of environmen- tal stressors, such as irradiance, moisture, and temperature, act on polymer structures and deteriorate their functionality of interest. Although standardized durability tests are widely used to collect failure information and evaluate durability of materials based on the typical pass/fail criteria, degradation modes, mechanism, and rate are not clearly understood. A domain knowledge-based and statistical modeling approach is neces- sary to quantitively investigate the termporal evolution of degradation mechanisms and cross-correlation of material response and degradation rate under various exposure con- ditions.
Lifetime and degradation science (L&DS), based on a stressor, mechanism and re- sponse framework, has been developed to investigate the relationship between the en- vironmental stresses to which materials are exposed and the responses observed in the process of degradation accumulation caused by different degradation mechanisms over lifetime69–72. The
approach, enables the prediction of lifetime based on multi-factor environmental stres- sors including irradiance, temperature, and humidity. The
The focus of
To assess the weathering performance of PMMA, weathering data are collected dur- ing the degradation process based on outdoor exposure or laboratory accelerated expo- sure. The outdoor exposure can be used to study the effects of environmental stresses on different intended functionality in the long term. Although the durability of polymers is best demonstrated by outdoor exposures, this method is time-consuming because the changes of physical and chemical properties occur too slowly. The prolonged periods of durability test in outdoor environment make it not efficient for work concerned with new product developments. To overcome this issue, accelerated exposure is designed to reproduce the outdoor weathering as closely as possible in the short term by inten- sive irradiance, elevated temperature, and moisture. Due to the difference in exposure conditions, cross-correlation of various exposure conditions remains a challenging task.
The limitations and problems encountered during outdoor and indoor accelerated ex- posures of polymers will be discused in the following section. Literature Review 18
2.3.1 Outdoor weathering
To investigate polymer degradation, the natural outdoor weathering can be used to study the effects of environmental stresses on different functional and decorative parameters of interest.
Outdoor weathering: light source and spectral power distribution. There are various factors that cause polymer degradation in an outdoor environment. Natural sunlight is the major cause of damage, and its intensity and spectral power distribution varies at different locations, seasons, and hours. The ultraviolet part of sunlight, spectral region from 290-400 nm, is the most damaging to polymer materials. There are a variety of solar spectral power distributions in the natural environment. In order to compare the out- door degradation results, AM 1.5 (Figure 2.15), a reference standard solar spectral power distribution, is introduced in the ASTM G177 standard73. In additional to seasonal vari- ation of irradiance, other factors such as the effect of dust, pollutants and fog may also have influence on the polymer degradation in outdoor environment74–76.
2.3.2 Accelerated weathering
The problem of natural outdoor weathering is that the natural degradation process is too slow and the long-term degradation, and in some cases impractical, cannot meet the industrial marketing timeline. In order to obtain service life data in a timely manner, accelerated weathering is widely used for research and development, quality control, and material certification. In most cases, accelerated weathering methods are used to reproduce the weathering effects that occur when materials are under sunlight exposure Literature Review 19
Figure 2.15. Spectral power distributions (280-4000nm) of The ASTM G173-03 AM 1.5, fluorescent UVA-340 and xenon arc full spectrum light sources
in the short term. In order to reproduce outdoor weathering in a reliable way, acceler- ated exposure should be set up with caution to induce the property changes consistent with outdoor conditions, such as the UV portions of sunlight, moisture and heat.
Accelerated weathering: light source and spectral power distribution. Artificial light sources in accelerated weathering should have a similar spectral distribution as sunlight to avoid unreal degradations and pathways of property changes.
UVA-340 lamps and xenon arc lamps with daylight filter are recommended for out- door simulation (Figure 2.16). To simulate the direct solar UV radiation, UVA-340 lamps provide an excellent spectral match with the UV portion of sunlight from wavelength around 370 nm to the solar cut-off of 295 nm77,78. Literature Review 20
Figure 2.16. Spectral power distributions (280-400nm) of The ASTM G173-03 AM 1.5, fluorescent UVA-340 and xenon arc full spectrum light sources
The full spectrum xenon arc lamp with daylight filter allows to test for damage, such as coloring and fading, not only caused by short-wave UV of sunlight, but also by longer wavelength79. The effect of filters and irradiance control are two main factors to con- sider when testing with a xenon arc lamp. The xenon arc lamp with a daylight filter that has a nominal cut-on of 295 nm can provide the best simulation of direct sunlight. The irradiance control system monitors the irradiance level of the lamp and compensates for the light output decay due to the lamp and filter aging by increasing the wattage to the xenon burner.
In order to compare the two light sources, the spectral irradiance is integrated over a specific wavelength range from 280 to 360nm (UVA360), as shown in the equation where Literature Review 21
Figure 2.17. Photostabilization chart88
2 UVA360 is the integrated irradiance (J/m ) between 280 and 360n, Eλ is the irradiance
(W /m2), λ is the wavelength, and t is the time under exposure80.
Z 360 Ee Eλdλ (2.1) = 280
Z t Z 360 UVA360 Eλdλdt (2.2) = 0 280
2.4 Photostabilization of Polymers: PMMA
Polymer degradation can be inhibited or retarded by different stabilization techniques.
In order to protect polymers, different formulations of UV absorbers81–83, hindered amine light stabilizers84,85 and antioxidants86,87 have been utilized to slow down the degrada- tion process as shown in Figure 2.17. Literature Review 22
A class of UV absorbers such as Tinuvin by BASF82 can be added into a polymer matrix to protect the polymer by sacrificial degradation of themselves. As an example, hydroxyphenyl benzotriazole compounds are generally used at 0.1 to 1 wt. % for UV stabilized formulations.
2.5 Data-driven Lifetime Study
In order to obtain reliable service life prediction, stepwise measurements at different time points through the lifetime of polymer are necessary for statistical analysis. With enough measurement data, statistical learning, the data-driven analysis, is a powerful tool to understand diferent degradation mechanisms and find a predictive function of the process of polymer degradation based on weathering data (Figure 2.18).
Figure 2.18. Data-driven method to study the durability of polymer materials Literature Review 23
2.5.1 Data Acquisition: physical and chemical characterization of degra- dation
FTIR Spectroscopy for Lifetime Study. Fourier Transform Infrared (FTIR) spectroscopy is a type of infrared spectroscopy for the study of the interaction of infrared light with matter. FTIR spectrometer quantifies the interaction of light with matter by generat- ing a spectrum, a plot of measured light intensity versus wavenumber or wavelength.
Analysis of FTIR spectra can uncover the molecules information in a sample at what concentration.
A FTIR spectrum is a plot of measured light intensity versus wavenumber. Con- ventionally, the x-axis is wavenumber range with high wavenumber to the left and low wavenumber to the right. The y-axis is the amount of light absorbed by a sample. A peak can be observed at wavenumbers at which significant amount of light was absorbed by a sample. The absorbance can be can be calculated by the following equation:
I0 A log( ) (2.3) = I where I0 is incident light and I is tranmitted light. In addition, absorbance is related to
the concentration of molecules in the sample by Beer’s law:
A ² l c (2.4) = ∗ ∗ where ² is the molar attenuation coefficient of the attenuating species, l is the optical
path length and c is the concentration of the attenuating species. For an absorbance
spectrum, the height or area of a peak is proportional to concentration of molecules in
a sample. The spectra also must be in absorbance if spectral subtraction is used. Degra-
dation of PMMA can be easily detected by FTIR. The significant change of IR absorption Literature Review 24
Figure 2.19. FTIR spectra for extruded PMMA sample. caused by UV irradiance can be observed in the whole IR region (Figure 2.19). The ab-
1 sorbance of carbonyl peak at 1710 cm− is attributed to ester group on the side chain.
Degradation of side chain can also be detected by the changes of C-O-C group which
1 absorbs IR in the 1000 - 1300 cm− region. New bands will also appear in the 3200 -
1 3600 cm− region due to the generation of hydroxyl or hydroperoxide group. The rate of changes of each type of functional groups could also be useful to show the different degradation rates between different formulations of PMMA.
A quantitative analysis of the evolution of FTIR spectra is a promising way to study the degradation science on materials like PMMA polymer. From the analysis of the time evolution of spectra, the molecular level alternations along with time can be detected.
Furthermore, the molecular level alternations can be correlated with some macroscopic relevant property of polymer, such as color and mechanical property.
To get quantitative results from FTIR, spectra need to be processed first. Literature Review 25
Figure 2.20. Block diagram for Gas chromatography–mass spectrome- try using electron ionization for collection of mass spectrum. (Cwszot Kkmurray, 2018, https://en.wikipedia.org/wiki/Gas_chromatography- mass_spectrometry.)
Gas chromatography–mass spectrometry (GC/MS) for Lifetime Study. Determining the structure of UV absorber in industrial polymer is an important step in the service life prediction. Gas chromatography–mass spectrometry (GC/MS) is the most ubiqui- tous analytical method to identify and quantify different substances within a sample.
The GC/MS is composed of the gas chromatograph and the mass spectrometer as shown in Figure 2.20. After different substances are separated on the GC capillary column, they are detected by MS separately. The mass spectrometer detect the different molecules by breaking each molecule in to ionized molecules and detecting them by their mass- to-charge ratio. GC/MS has been used to study the chemical composition of PMMA degradation. Henry et al. observed the trapped volatiles by using GC/MS to study the structural changes in PMMA under hard X-ray irradiation, such as MMA monomer89.
Manring et al. investigated the random side-group scission in the thermal degradation of PMMA22. Based on the GC/MS analysis, they concluded that it is the scission of a methoxycarbonyl side group initiates the thermal degradation of PMMA-H rather than main-chain scission.
UV-Vis Spectroscopy for Lifetime Study. Absorbance and transmittance change of sam- ples provide a quantified measurement of optical property changes of the bulk material Literature Review 26
in photodegradation processes. Induced absorbance to dose (IAD) is developed to mea- sure the change of the optical absorbance per centimeter of a sample. IAD is defined by
Absi (λ)/cm Abs0(λ)/cm IAD − (2.5) = Dose Dose i − 0 Therefore IAD is independent of thickness and can be used to represent the average of
light absorbance per unit of thickness of the sample. Photobleaching and photodark-
ening in the degradation process can be detected and tracked by IAD quantitatively on
a per unit dose basis. The per unit dose basis can be calculated based on either UVA-
340 dose or per spectral dose. In this way, IAD quantifies how the optical property of
materials with stabilizers changes under exposure in successive dose steps.
Optical property of PMMA is an essential aspect in the study of electronic tran-
sition and their possibility of applications in solar collection, green house cover and
optical fiber. To understand the structure and optical property of PMMA (amorphous
nonmetallic materials), the study of absorption spectra is a productive methods. The
study of the optical absorption spectra of PMMA provides essential information related
to the energy gap and the band structure of the non-crystalline PMMA. Degradation
of poly(alkyl methacrylate) leads to an increase of UV absorbance at 230 to 400 nm
range. The formation of unsaturated groups in PMMA can explain the increase of UV
absorbance at 230 to 400 nm range. The absorbance at 275 and 300 nm are useful for
comparison of performance of different formulations of PMMA under weathering ex-
posure.
Absorption edge of a material is the energy at which there is a sharp discontinuity
(rise) of in the absorption spectrum of the material. The rise in absorption spectrum Literature Review 27
Figure 2.21. CIE color space, top view. (Holger Everding, 2015, https://en.wikipedia.org/wiki/CIELAB_color_space) happens where the energy of an absorbed photon has correspondence to an ioniza- tion potential or electronic transition. Absorption edge moves toward longer or shorter wavelength when degradation happens.
Color Measurement for Lifetime Study. Yellowness is Yellowness Index (YI) is a param- eter that quantifies the degree of departure of an object color from colorless/white to- ward yellow. YI per ASTM E313 standard is measured by a HunterLab Colorimeter and calculated as follows:
100(Cx X Cz Z ) YIE313 − (2.6) = Y Delta E (∆E) is a number that represents the difference between two colors. It is used to describe how far off is the color of degraded sample from the original.
q ∆E (L L )2 (a a )2 (b b )2 (2.7) = ∗2 − ∗1 + 2∗ − 1∗ + 2∗ − 1∗ Literature Review 28
Figure 2.22. Spectrophotometer for haze measurement. (Nidgeyr, 2016 https://en.wikipedia.org/wiki/Haze_(optics))
where CIE L∗, a∗, and b∗ values provides a numeric decsriptor of different color in a rectangular coordinate system as shown in Figure 2.21.
Light that is scattered upon passing through a degraded film or sheet of a material can produce a hazy or smoky field when objects are viewed through the material. Haze is used to describe the view of cloudiness of a product. It is an essential appearance attribute that can be quantified and assessed by a colorimeter (Figure 2.22) to control quality for the purpose of product and durability test. Haze (%) per ASTM D1003 stan- dard is measured by HunterLab Colorimeter and calculated as follows:
Tdi f f use Haze(%) 100 (2.8) = ∗ Ttotal Literature Review 29
Fluorescence spectroscopy for Lifetime Study. Fluorescence can be utilized to study the emission behavior in attempt to identify the fluorescent structures changed or pro- duced in the process of polymer degradation. Fluorescence spectroscopy analyzes the
fluorescence of a sample by measuring the light emitted from a sample after a beam of light excites the electrons in certain molecules of the sample as shown in the Jablonski diagram in Figure 2.23. Molecules have different energy states referred to as energy lev- els. Electronic and vibrational states are the main topics of fluorescence spectroscopy.
The species absorbs one photon first. Then it is excited from ground electronic state to one of the vibrational states in excited electronic state. The excited molecule keeps los- ing its vibrational energy due to the collision with other molecules until it is in the lowest vibrational state in excited electronic state. Then the excited molecule returns to one of the vibrational states in ground electronic state by emitting a photon. The emission is measured by a fluorometer and create a emission map. The emission map records the emission spectral caused by a range of excitation wavelengths. Excitation-emission matrices (EEMs) are three-dimensional spectral data that portrays the fluorescent por- perties of chemical mixtures. Effect of UV-irradiation on fluorescent property of PMMA was studies by Kovalonek et al90.
2.6 Statistical Learning Methods in Weathering Studies: State of Art Modeling
2.6.1 Computational Environment for Statistical Learning of Weathering Data
The works in the thesis were created by using free and open source softwares. Most of the analytics are performed in R statistical language. R codes were executed in Rstudio, the integrated development environment for R language. Literature Review 30
Figure 2.23. The Jablonski diagram for fluorescence. (Jacobkhed, 2012, https://en.wikipedia.org/wiki/Jablonski_diagram.)
Figure 2.24. Computational environment for data analysis: several open source software tools and R packages for visualization and spectral anal- ysis. Literature Review 31
2.6.2 Self-organizing map (SOM): an artificial neural network (ANN) algo- rithm for fluorescence data analysis
Although the measurements of EEMs have become easier with the increasing availabil- ity of spectrofluorometric measurement techniques, the interpretation and extraction of useful information are still challenging due to the high dimensional data. The sig- nals and shapes in EEMs of degraded PMMA samples are mixtures of different fluores- cence phenomena of a wide range of molecules in polymer, including monomer resid- uals, polymer, different additives and degraded products of polymer and additives91.
For additives in commercialized polymer, most of molecules cannot be identified due to proprietary reasons. For that reason, it is essential to have a pattern recognition meth- ods than can detect and decompose the overlapping signals in the absence of knowledge about the formulation of a given polymer products.
Self-Organizing Maps (SOM) is a powerful tool to discern relationships and extract features among the data without assumption or knowledge on the complex data sets.
The SOM algorithm is an unsupervised self-learning algorithm that explore data and extract features that can describe an pattern that represents properties of an item92–96.
The feature of SOM of fluorescence data of polymer degradation can refer to the spectral properties of a particular fluorophore or a group of fluorophores from polymer chains or additives in the process of degradation.
To understand the network training of self-organizing map, the fundamental con- cepts of ANN are summarized. Artificial neural networks are powerful computing sys- tems inspired by the neural networks in biological brain97,98. The special structure of
ANN is based on a collection of connected units (neurons) arranged in interconnected
layers. In a typical ANN, the structure is comprised of three layers, input layer, hidden Literature Review 32
Figure 2.25. An artificial network with 3 layers: input layer, hidden layer, and output layer
Figure 2.26. Training process of a self-organizing map. (Mcld, 2010, https://en.wikipedia.org/wiki/Self-organizing_map.) layer, and output layer (Figure 2.25). The neurons in input layer receives the external data and presents to the hidden layer through weighted connections. The output layer produces the overall network response to the external data by the sum of outputs of neurons.
Self-organizing map is a two-layered ANN, consisting of an input layer and an out- put layer. Each of the neurons in input layer is connected with all the input data99–101. Literature Review 33
The connection with input data is represented by a weight vector that has the same di- mension as the input vector. Each neuron in output layer is associated with a weight vector from the input layer. The first step of map building process is to determine the number of neurons in map space through the ratio of two largest eigenvalues of the in- put matrix. After the initialization of map, training process begins with moving weight vectors toward the unfolded data. For each input sample, the neuron in output layer whose has the weight vector most similar to input sample is found and called the best matching unit (BMU). The weights of the BMU and its neighboring neurons are adjusted and moved towards the input vector as shown in Figure 2.26. The update equation for a neuron i with weight vector wi (k)
w (k 1) w (k) ²(k)h (i,k){x (k) w (k)} (2.9) i + = i + p j − i where w (k 1) is the new weight vector of the neuron i, k is the step index, p is i + the index of BMU, ²(k) is the learning rate, hp (i,k) is the neighborhood function which generate the distance between the neuron i and its neighboring neurons.
2.6.3 Degradation Pathway Diagram of Network Structural Equation Mod- eling (netSEM)
Structural equation modeling (SEM) is a type of multivariate statistical analysis tech- nique used to describe and analyze relationships between variables. The network struc- tural equation modeling (netSEM) was developed to allow for quantitative analysis of temporal evolution of polymer degradation based on the domain knowledge of polymer degradation and stability and statistical learning. The netSEM statistical methodology interprets the relationship between variables measured along polymer degradation and Literature Review 34
generates a predictive pathway model between exposure stressors, mechanistic vari- ables, and responses of performance. This netSEM for
Adjusted R-squared is used for model selection for every pair. The netSEM pathway dia- gram summarizes the fitting models (Mod) and the adjusted R2 values (adj-R2) for each relationship are given along the connection lines. Following the strongest relationships can lead to predictive degradation pathway from stress variable (UVA360] dose), mech- anism (e.g. FTIR carbonyl absorption peak) to response variable (Yellowness Index or color). The quantitative pathway equation quantify the relation between mechanistic variables with the final responses of material performance.
Structural equation modeling. Structural equation modeling includes a diverse set of statistical models fit networks of variables to describe and analyze relationships be- tween variables102–105. Latent variable, represented by circles in SEM, is a hypothetical variable that is invoked to explain the observed performance(covariance) of a process, for example durability in engineering and intelligence in psychology. In some literature, latent variables is also called factors. The observed performance is generally called ob- served variables that are measured from participants, represented by squares. The basic understanding is that the latent variable is an underlying cause of a group of observed variables. For example, the weathering test is recorded and measured by yellowing and
FTIR peak intensity change. The variance in response to yellowing and FTIR peak inten- sity change in durability test reflect sample difference in durability. Here it is assuming a latent variable corresponds to durability. The latent variable, durability, is assumed to Literature Review 35
cause the observed correlation among the yellowing and FTIR peak change, the two ob- served variables. Residuals, represented by circles, are error terms estimated for latent variables. There are random errors along with the each correlation because observed variables usually have measurement errors associated with them. Low error means that the model is an accurate representation of of relationship between variables. Exogenous variables are synonymous with independent variables that are thought to be the cause of a phenomenon. Endogenous variables with error are synonymous with dependent variables that are caused by exogenous variables. In a SEM model, the arrow will be go- ing to an endogenous variable. Specification is the first step to build up a SEM model.
Specification is a term for building the hypothesis of the SEM model. It draws out the hy- pothesized relationship between variables based on domain knowledge. Specification error that comes along with specification, also called left our variable error, is originated from the omitted predictors that is important for prediction. The relationship between each measured variables are identified by an analysis of covariance.
Pathway model. Pathway analysis is a statistical method that can describe the hypoth- esized dependencies among a set of variables. It is a special case of structural equation modeling. Path analysis only has measured variables. Exogenous variables are indepen- dent variables. Endogenous variables are dependent, or both dependent and indepen- dent. To construct a pathway model, variables are connected by arrows from a variable to any other variable that is affected. Exogenous variable can only have single-headed arrow exiting from it. Path coefficients and statistical model are usually labeled on the arrow that links a pair of variables with causal connection. In real-world applications, the endogenous variables may also be affected by other factors or variables not listed in path model, called error term and labeled as 0e0. Literature Review 36
Figure 2.27. An example of structural equation model. Boxes con- tain variables that can be measured in the data. Circles con- tain variables that cannot be measured. Residuals and vari- ances are drawn as double headed arrows pointing into an object. (https://en.wikipedia.org/wiki/Structural_equation_modeling)
Figure 2.28. An example of path modeling. Literature Review 37
Figure 2.29. An example of network modeling for yellowness index pre- diction with induced absorbance to dose as mechanism variable. The stressor variable is IrradTotal and the response variable is yellowness in- dex (YI). IAD1, IAD2, IAD2p and IAD3 are the induced absorbance to dose at 275nm, 298nm, 339nm and 440nm. 38
3 Experimental 1
3.1 PMMA formulations
There are 6 formulations of PMMA studied in this project, including UV transparent
(UVT), FF1, UVO, UVP, UV filtering (UVF), and UV absorbing (UVA) acrylic samples.
UVT, FF1, and UVA are from brand A. UVO, UVP, and UVF are from brand B. All the
6 formulations are clear and have thickness around 3 mm. UVT is the only fromula- tion without any light stabilizer. The other 5 stabilized formulations contain different amounts of stabilizers. UVP and FF1 are used in security and transportation industry as substrates. UVF and UVA are 2 formulations tailored for applications that require extra
UV protection and UV blocking.
3.2 Exposures
The 3 different exposure conditions are shown in Table 1. Exposures were performed using QUV Accelerated Weathering Tester (Model QUV/Spray with Solar Eye Irradiance
Control) and Q-SUN Xenon Test Chamber (Model Xe-1/Spray) for full spectrum light exposures. For UV light exposures, the QUV Accelerated Weathering Tester with fluores- cent UV lamps is used to simulate the effect of critical short-wave UV in sunlight. The Experimental 1 39
spectral power distribution of fluorescent UV lamps matches the AM1.5 standard spec- trum between 280nm to 360nm. In Cylic QUV exposure, it has two cycles based on mod- ified ASTM G154 standard cycle 4. The first step is that the UV irradiance at 340nm was
2 set at 1.55 W /m with black panel temperature of QUV chamber at 70 ◦C for 8 hours. The second step is a condensing humidity cycle at 50 ◦C without UV light. In Hot QUV expo- sure, the UV irradiance at 340nm was set at 1.55 W /m2 and the black panel temperature of QUV chamber is 70 ◦C based on modified ASTM G154 standard cycle 4 without con- densing humidity cycle. Due to the absence of condensing humidity cycle, the samples in Hot QUV (Modified-ASTM G154 Cycle 4) were applied light exposure at 1.5 times the rate of those in Cyclic QUV (ASTM G154 Cycle 4). For full spectrum exposure, Q-SUN
Xenon Test Chamber with Daylight-Q filter. The xenon arc full spectral exposure with
Daylight-Q filter (QSUN) can provide an accurate spectral match with sunlight with a nominal cut-on of 295 nm. In QSUN exposure, samples are exposed to repetitive cycles of full spectrum light and moisture under controlled environmental conditions based on modified ASTM G155 standard cycle 1. The first step is that the UV irradiance at
2 340nm was set at 0.6 W /m with black panel temperature of QUV chamber at 63 ◦C for
108 minutes. The second step is a water spray cycle at 43 ◦C with UV irradiance of 340nm at 0.6 W /m2.
In the design of weathering experiment, 24 samples from each of 6 formulations were assigned to 3 different exposure types (8 samples per formulation per exposure type) to provide enough data for statistics.The Total exposure time is 3200 hours for all three exposure conditions. The samples are measured at 0 hours, 400 hours, 800 hours,
1200 hours, 2200 hours and 3200 hours. For 8 samples of each formulation at each ex- posure type, one sample was retained at step 0, step2, step 4 and step 5. Experimental 1 40
Stressor Exposure Condition UV, heat, hu- Cyclic QUV Cyclic exposure of 8 hours of UVA light at 1.55 2 midity W /m at 340 nm at 70 ◦C and 4 hours of con- densing humidity at 50 ◦C in dark UV, heat Hot QUV Constant exposure of UVA light at 1.55 W /m2 at 340 nm at 70 ◦C Full spectrum QSUN Cyclic exposure of 102 minutes of TUV light at 2 70 W /m , temperature 63 ◦C and 18 minutes 2 of TUV light at 70 W /m , temperature 63 ◦C with water spray Table 3.1. Exposure based on ASTM G154 and ASTM G155 standards
To accurately compare the xenon arc and UV light exposures, photodose of light be- tween 280nm to 360nm was calculated in equation where UVA360 is the integrated irra-
2 2 diance (Jm− ) between 280 and 360nm, Eλ is the irradiance (W /m ), λ is the wavelength and t is the time under exposure.
Z t Z 360 UVA360 Eλdλdt (3.1) = 0 280 To compare the irradiance of three exposures, spectral characteristics are shown in
Table 3.2.
Calculation Spectrum Cyclic QUV Hot QUV QSUN Full spectrum 56.36 84.54 390.71 Irradiance TUV 56.36 84.54 70.00 (W /m2) UVA360 40.43 60.65 26.06 Exposure time (h) 3200 3200 3200 2 Photodose(MJ/m ) UVA360 465.84 698.72 300.22 Table 3.2. Spectral characteristics Experimental 1 41
Figure 3.1. The exposure routes for the degradation study of the 6 grades of PMMA in Cylic QUV, Hot QUV, and QSUN.
3.3 Evaluations
A colorimeter (UltrascanPro spectrophotometer, Hunterlab, USA) was used to measure the yellowness index (YI) and Haze of the exposed samples using D65 illuminant with viewing degree angle at 10 degree (coefficients: Cx =1.3013, Cz 1.1498). Yellowness is the
attribute of perception of color by which the departure from colorless/white toward yel-
low is judged. Yellowness index (YI) is a number used to evaluate the departure of color
from colorless, toward yellow. Haze is the ratio of diffuse transmittance to total trans-
mittance of incident light in the wavelength range between 380nm and 780 nm. A UV Experimental 1 42
attenuation filter is inserted partially in the light path of spectrophotometer to simu- lated the D65 daylight. D65 light source is to ensure that there is a single standard for lighting can be utilized across different products, manufacturers and industries. The high-performance colorimeter, used for for accurate color perception and evaluation, allows fast and non-destructive measurement with spectral range from 350 – 1050 nm with 5 nm data output.
The Gas chromatography–mass spectrometry was performed by GC-MS QM2010
Plus from Shimadzu with Pyrolyzer-3030D from Frontier Laboratories Ltd. The PMMA samples were heated in the in the pyrolyer from 60 ◦C to 320 ◦C at a heating rate of
20◦C/min under a helium flow. The evolved gases were continuously introduced into
GC and MS for identification of substances.
The transmittance and reflectance of the PMMA samples were measured by a spec- trophotometer (PARTS, Filmetrics, USA).
The FTIR spectra of the front and back of each sample were measured by a FTIR
Spectrometer (Cary 630, Agilent, USA)
3.4 Urbach analysis of absorption edges and Lorentzian fitting
The electronic properties and band structure of polymers and polymers with additives have a strong influence on the absorption of light. In disordered polymer materials, the exponential absorption edge observed in UV-Vis spectroscopy is known as Urbach edge. This phenomenon was first reported by Urbach106 by the plot of logarithm of absorption coefficient ver sus frequency in electron volts shwoing a straight line. Char- acterization of the Urbach edge can be used to evaluate the performance of electronic Experimental 1 43
devices. In the process of polymer degradation, yellowing will shift the Urbach edge to longer wavelength due to the increased absorption caused by yellowing.
The electronic properties and band structure can be studied by optical absorption spectra. The optical absorption spectra of different polymers has been investigated to describe the band tails.107,108. Urbach analysis of absorption edge is an effective way to quantify the optical absorption edge and distinguish the possible contributions to absorbance106,109.
The equations used for fitting an exponential to the absorption coefficient α are
E E0α α(E) Hαexp( − ) (3.2) = Wα
E (E0α hαWα) ln(α) − − (3.3) = Wα where the relationship between absorption coefficient α and frequency in electron volts
110,111 is characterized by the Urbach Width (W ) and the Urbach edge energy (E0) . The intercept E0α is the Urbach edge energy from the fit with absorption coefficient and the intercept E0A is the Urbach edge energy from the fit with absorbance.
The equations used for fitting an exponential to the absorption coefficient A are
E E0A A(E) HAexp( − ) (3.4) = WA
E (E0A hAWA) ln(A) − − (3.5) = WA
E E0A α Hα E E0α A(E) HAexp( − ) exp( − ). (3.6) = WA = ln(10) = ln(10) Wα Experimental 1 44
ln(A) m E b . (3.7) = A ∗ + A
where Hα hα, HA and hA are fitted parameters.
The residual monomers in PMMA product have an important effect on the rate of chain scissions in degradation process. The absorption of monomer in PMMA can be
fitted by Lorentz oscillator model.
d A(E) . (3.8) = 1 b(E E )2 + − 0 where b, d, and E0 are the parameters for Lorentzian fitting. E0 is the band center of
absorption115,116. 45
4 Results
In this chapter, the measured evaluations of properties of the 6 PMMA extruded sheets under three different exposure conditions will be discussed. Please refer to the experimental chapter for more detail about sample information and exposure condi- tions. Compositional information of baseline samples by GC/MS will be firstly shown in order to evaluate the weatheribility of PMMA. Then the performance loss during degra- dation, such as yellowing and haze formation, shows the different durability of the 6 grade of PMMA under the 3 different acelerated weathering exposures. The three accel- erated exposures are fluorescent UVA-340 (UV-only) irradiation, fluorescent UVA-340
(UV-only) irradiation with condensing humidity cycle and xenon-arc (full spectrum) ex- posure. It was observed that the stabilizer-free formulation (UVA) has the highest degra- dation rate. Residual monomer-induced degradation was proposed to explain the ob- servation by the Urbach analysis. After the color analysis, induced absorbance to dose
(IAD) based on UV-Vis spectra data was utilized to quantify the degradation rate and discriminate the durability of the 6 grades of PMMA. In addition, the fluorescent data of samples exposured in UVA-340 irradiation were analyzed by self-organizing map and three degradation patterns were revealed. Results 46
4.1 Stabilizers in baseline samples of the 6 grades of PMMA
Pyrolysis GC/MS has been used as the method to provide compositional information of additives in polymer materials. Baseline samples of the 6 grades of PMMA were mea- sured by a thermal desorption GC/MS by Polymer Competence Center Leoben GmbH.
Three types of UV stabilizer were detected by GC/MS, antioxidant, hindered amine light stabilizer(HALS) and UV light absorbers. Results of stabilizers information in the 6 grades of PMMA are summarized in table 4.1.
PMMA Brand ID Antioxidant HALS UV absorber grade UVT A sa18021 – – – FF1 A sa18005 – – Tinuvin P (possible fragment) UVO B sa17750 Irganox 1076 – ? UVP B sa17643 Irganox 1076 – Tinuvin P,Tinuvin 327 UVF B sa17830 Irganox 1076 – Tinuvin P,Tinuvin 327 UVA A sa18013 Irganox 1076 Tinuvin 292 ? Table 4.1. Additives information for PMMA in Hot QUV at 0 hour. The ’- ’ stands for that the stabilizer was not detected and the ’?’ stands for the stabilizer should be in formulation based on UV-Vis spectroscopy but was not detected by GC/MS.
UVT from brand A is the only one formulation that is unstabilized. The other two grades of PMMA from brand A are FF1 with UV absorber P and UVA with both antiox- idant (Irganox 1076) and HALS (Tinuvin 292). All the three grades from brand B were detected to have antioxidant Irganox 1076. The Irganox 1076 is the only photostabi- lizer used in UVO. The other 2 grades of PMMA from brand B, UVP and UVF, both have
Irganox and two additional UV absorbers, Tinuvin P and Tinuvin 327.
To understand how the lifetime of PMMA is extended by different type and amount of stabilizers, some fundamental concepts of polymer stabilizer are summarized below. Results 47
Exposure to light, especially UV light, is a major factor causing degradation of polymer.
When oxygen is available, rapid reaction with oxygen should also be inhibited so that polymer can be protected from autoxidation. To protect polymer from light and radical, light stabilizers and antioxidants are added to block light and react with radicals or hy- droperoxides, respectively, and thereby extend the lifetime of polymer. Light stabilizers are used to retard polymer photo-oxidation, which is a free radical process resulted by the combined effect of the reaction with light and oxygen. UV absorber is one type of light stabilizers that dissipate the absorbed light as heat to protect polymer matrix from harmful UV light and hence extend the lifetime of polymer. Another type of generally used light stabilizer is hindered amine light stabilizer (HALS), which can scavenge rad- icals generated by weathering. Different from light stabilizer, antioxidants mainly react with peroxyl radical and hydroperoxide that formed in autoxidation when polymer re- acts with oxygen.
All the structures of detected stabilizers are shown in figure 4.1 and different opti- cal properties of UV absorbers are shown in Figure 4.2. The UV absorbers, Tinuvin P and Tinuvin 327, are both the hydroxyphenylbenzotriazole class, imparting light stabil- ity to PMMA. Both of the UV absorber feature a strong absorption of UV radiation in the
300-400nm region and minimal absorbance in the visible region (>400 nm), providing
UV protection for PMMA matrix. The hindered amine light stabilizer, Tinuvin 292, is a liquid mixture of tow ingredients especially developed for coatings. The antioxidant,
Irganox 1076, is a non-discoloring stabilizer that protects polymer against thermooxida- tive degradation. Results 48
Figure 4.1. Stabilizers in the 6 grades of PMMA detected by GC/MS.4
In conclusion, GC/MS was proved to be useful for the evaluation of compositional information for commercial PMMA products. The additive information indicates differ- ent level of protection by additives in the 6 grades of PMMA. The results obtained also coincided with the observed different yellowing trends of the 6 grades of PMMA, which will be shown in the following part of this chapter. Results 49
Figure 4.2. The optical absorbance for UV absorbers4.
4.2 Yellowness Index and Haze
Discoloration is one of the main performance loss during the photodegradation of PMMA.
Yellowness index (YI) is used to quantify the discoloration of PMMA under exposure in
air. Note that all the samples of 6 PMMA formulations are transparent and in colorless
form at initial stage without any exposure. In the multi-panel plot of YI data, the faceting
groups on the rows are the 3 exposure types and the faceting groups on the columns are
the 6 PMMA formulations. In the 6 columns of PMMA formulations, the first 3 columns
are from brand 1 and the other 3 columns from brand 2. For brand A, the amount of light
stabilizer increases in the sequence of UVT, FF1 and UVA. For example, UVT is stabilizer-
free, FF1 has medium amount of UV absorber, and UVA has the highest amount of UV Results 50
Figure 4.3. Yellowness index data for the stablilizer-free PMMA (UVT).
Figure 4.4. Yellowness index data for the 6 grades of PMMA.
absorber. For brand B, the amount of UV absorbers increases in the sequence of UVO,
UVP,and UVF.For example, UVO only has antioxidant, UV0 has medium amount of light stabilizer and UVF has the highest amount of light stabilizer. Results 51
Yellowness index is used to measure visually observed change in appearance of poly- mer caused by degradation. Changes in YI of the 6 formulations and the different trends of changes of YI shows that YI is sensitive to the small changes in formulation and in the optical performance of PMMA after different exposures. The efficiency of absorber at various amounts in PMMA are different in the 6 formulations. Among the 6 formu- lations, it shows that UVT is yellowing significantly (Figure 4.3) in Cyclic QUV and Hot
QUV and it is much faster compared to other 5 formulations (Figure 4.4). Yellowing of
UVT is visible for samples exposed in Hot QUV and Cyclic QUV. UVT is the sample that has no light stabilizers among the three formulation from brand A.
The light stabilizers protected the other 5 formulations well (Figure 4.5) from yel- lowing by absorbing UV light and dissipate harmlessly as heat therby reducing the UV irradiance that can be absorbed by the polymer matrix. FF1, with medium level of ab- sorber, shows relatively lower yellowing rate. The other 4 formulations with relatively higher amount of UV absorber shows negligible yellowing. The total color change ∆E are shown in Figure 4.12. It can be concluded that knowledge of UV absorber efficiency in the 6 PMMA formulations can be obtained via Cyclic QUV and Hot QUV in a short term, compared to QSUN which has a much slower yellowing rate at same UVA photo- dose.
As a polymer degrades, change of haze may be observed. PMMA degrades and loses optical clarity under different environmental stressors of ultraviolet irradiance, heat and moisture. Note that all the samples of 6 PMMA formulations are transparent and in colorless form at initial stage without any exposure. Hazing is a consequence of surface defects or crystallites formed during degradatoin. The lower the haze value, the higher the clarity of PMMA sample. Results 52
Figure 4.5. Yellowness index data for the 5 grades of PMMA without UVT.
Figure 4.6. Haze data for the 6 grades of PMMA.
Data related to haze formation across the 6 formulations and 3 exposures are shown in Figure 4.6. Samples exposed in full-spectra QSUN show significant haze formation in Results 53
Figure 4.7. Haze data for the 6 grades of PMMA exposed in QSUN.
Figure 4.8. Haze data for the 6 grades of PMMA in Hot QUV and Cyclic QUV.
Figure 4.7. In Q-SUN exposure, samples are exposed to repetitive cycles of full spectrum light and moisture under controlled environmental conditions based on modified ASTM
G155 standard cycle 1. Slight increase of haze was observed under Cyclic QUV (Figure
4.8), however negligible change of haze was observed by Hot QUV.Despite the high level of UVA dose and yellowing in Hot QUV, the absence of moisture cycle reduce the risk Results 54
of haze formation significantly. Therefore, it can be concluded that Haze formation is promoted by moisture rather than UV-light. Hydrolytic degradation is the dominant process in the formation of haze.
The moisture level is also an important factor during haze formation. The changes of surface morphology of the back of PMMA samples at 0 hour and 3200 hours expo- sure under Hot QUV are shown in Figure 4.9 and 4.10. Roughness of the back side is much higher than the irradiated front side as shown in Figure 4.11. For Q-SUN expo- sure, this roughness change is related to the residual water between the Q-panel and the back of PMMA. The residual water cannot totally evaporate during the 102-minute light- only step 1 exposure in Q-SUN, which means that the center of backside of samples in
Q-SUN is in contact with water all the time during the 3200 hours exposure. That fact can explain why there are more haze formations in the center of the backside of PMMA sample in Q-SUN. Therefore, it can be concluded that the combination of moisture and full-spectrum light exposure is the main stress that leads to the significant haze forma- tion than combination of moisture and UV exposure. The haze formation in the center of the backside of sample started as a opaque spot in the center of sample and the value of haze (%) of the spot increases as the irradicance dose increases.
The large sample-to-sample variance in the values of haze (%) in QSUN exposure is attributed to the random shape of the haze formation in the center of PMMA sam- ples. Note that the number of data points is decreasing over time under a given expo- sure just because of sample retention at each exposure step. Under Hot QUV, the lowest sample-to-sample was observed because of negligible Haze formation due to absence of moisture. Results 55
Figure 4.9. Surface morphology of UVT samples at 0 hour
Figure 4.10. Surface morphology of UVT samples at 3200 hours exposure in QUSN. Results 56
Figure 4.11. Surface roughness of irradiated side (front) and non- irradiated side (back) in QSUN
Figure 4.12. ∆E data for the 6 grades of PMMA. Results 57
4.3 Absorbance and Induced Absorbance to Dose
The optical property of PMMA samples was evaluated by the change of UV-Vis absorbance and induced absorbance to dose. Please note that the absorption data will be analyzed sample by sample to show the spectral characteristics even though there are 8 replicate samples of each grade of PMMA for each type of exposures. The fundamental absorption edge of UVT (PMMA without any UV absorbers) is around 275 nm, compared to other
5 PMMA formulations which all have absorption edge larger than 300 nm as shown in
Figure 4.13. Although no UV absorber was found in UVT, it may contain other types of additives. Thus the 275 nm may not represent the fundamental absorption edge of pure
PMMA. But it is acceptable to 275 um as absorption edge other than the real fundamen- tal absorption edge due to the spectral saturation and instrumental limitation. Results 58 Figure 4.13. The panel plots fordata the of baseline sample samples. without any At exposure.indicating Each baseline each panel measurement, grade has of 8 all PMMA UV-Vis spectral the has low panel samplt-to-sample plots variation. in one column should have similar results, Results 59
Figure 4.14. The process of quantification of degradation rate by induced absorbance to dose calculation: UVT exposed in Hot QUV as an example.
The transmittance spectra indicate the changes of light transmission is different for each formulation. The absorption spectra of PMMA samples show radiation induced absorption changes. The baseline spectra at 0 hour show that the absorption edges for each of the 6 PMMA formulations are different as listed in table. Both increment and decrement in absorption proportional to exposure time up to 3200 hours were observed in different wavelength range. The change of absorption was attributed to the photo degradation of PMMA molecular chains, residual monomers, and UV absorbers.
4.3.1 UVT: unstabilized
In the Abs/cm spectra (Figure 4.15) for UVT under Hot QUV, it shows a sharp increase of absorption of light at 275 nm. The baseline spectrum start absorbing UV light at and be- low 325nm, shifting to longer wavelength as high as 600nm because of photodegration. Results 60
Figure 4.15. Abs/cm spectra for UVT under Hot QUV exposure.
Figure 4.16. Induced absorbance to dose (IAD) spectra for UVT under Hot QUV exposure. Results 61
Figure 4.17. Abs/cm spectra for UVT under Cyclic QUV exposure.
Figure 4.18. Induced absorbance to dose (IAD) spectra for UVT under Cyclic QUV exposure. Results 62
Figure 4.19. Abs/cm spectra for UVT under QSUN exposure.
Induced absorbance to dose is used to quantify the change of absorbance per cen- timeter per applied dose basis of a sample, assuming uniform bulk absorption of light
(Figure 4.14). IAD values of UVT in all three exposures conditions (Hot QUV (Figure
4.16), Cyclic QUV (Figure 4.18), and QSUN (Figure 4.20)) are the highest among the 6 grades of PMMA. Please note that a positive IAD value represents photodarkening and a negative IAD value represents photobleaching. UVT has strong absorption in region
1 (275 nm) and region 3 (400 nm) for all three types of exposure (Figure 4.15, 4.17, and
4.19), especially for UVT samples in Hot QUV. The IAD rate for UVT is initially high and levels off after 800 hours. The IAD rate is dose-dependent and keep decreasing as the
UVA dose increases. This is the clear evidence that samples with no UV absorber (UVT) is less UV-stable than the other 5 grades of PMMA. Results 63
Figure 4.20. Induced absorbance to dose (IAD) spectra for UVT under QSUN exposure. Please note that a positive IAD value represents pho- todarkening and a negative IAD value represents photobleaching.
The temperature effect may account for the highest rate of photodegradation in UVT samples in Hot QUV. In addition, the UV light may pass the UVT sample a second time because the aluminum tray on which the samples are mounted are reflective. The other
5 formulations that have UV absorbers protect the samples from UV light by absorb- ing and convert energy in to heat. This may account for the highest IAD rates of UVT photodegradation among the 6 formulations.
The methyl methacrylate monomer can also significantly affect the lifetime of PMMA.
The MMA monomer is thought as the main impurity in PMMA products. Level of resid- ual monomers in bulk polymerized and extruded PMMA is in the range of 0.6 % to 3.9
%3,50,51,112? . Most of the monomers are consumed by attaching to a propagating radical or a peroxyl radical. The formation of a hydroperoxide from MMA and singlet oxygen Results 64
will undergo photolysis and make polymer vulnerable to light. The sensitizing effect of residual monomer on degradation rate has also been attributed to the reaction that converse primary and secondary radical to tertiary alkyl radical. After peroxidation of tertiary radical, the disproportion reaction will be suppressed due to the lack of primary and secondary radical. Then a non-terminating reaction will accelerate the chain scis- sion of PMMA by β-scission of alkoxy radical3.
4.3.2 FF1: stabilized with relatively lower amount of UV absorber
FF1 samples have distinct spectra features at 298nm and 339nm (region 2 and region 20).
The UV absorber that has the same absorption peaks is Tinuvin P113. Tinuvin P,belong-
ing to hydroxyphenyl benzotriazole class, has advantageous properties in UV protection
of PMMA product, such as color-neutral with low yellowness index, involatile and inex-
pensive114. The FF1 shows absorption change in all three regions (Figure 4.21, 4.23, and
4.25). Both FF1 and UVT show significant photodarkening in region 1. Different from
unstabilized UVT, it is observed that there is photobleaching occurring in region 2 and
region 20. The photobleaching can be attributed to the degradation of UV absorber in the samples. The UV absorber absorbs and dissipates the UV light that penetrates into the
PMMA sheet. The UV absorber degrades in the process so that the transmittance of light increases and leads to photobleaching. With the protection of UV absorber, FF1 shows lower IAD values range from 0.001 to 0.006 Abs/cmper M J/m2 at 275nm, compared to
unstabilized UVT wiht IAD values range from 0.020 to 0.036 Abs/cmper M J/m2.
4.3.3 UVO: stabilized with relatively higher amount of UV absorber
UVO is the PMMA from brand B that is stabilized more than FF1 from brand A. The
special features of stabilizer observed in FF1 is disappeared due to the saturation of high Results 65
Figure 4.21. Abs/cm spectra for FF1 under Hot QUV exposure.
Figure 4.22. Induced absorbance to dose (IAD) spectra for FF1 under Hot QUV exposure. Please note that a positive IAD value represents photo- darkening and a negative IAD value represents photobleaching. concentration of light stabilizers. The absorption edge does not change both in Hot QUV and Cyclic QUV, indicating the enhanced durability and the polymer is well protected by light stabilizer. However, there are slight increasing absorption observed for samples Results 66
Figure 4.23. Abs/cm spectra for FF1 under Cyclic QUV exposure.
Figure 4.24. Induced absorbance to dose (IAD) spectra for FF1 under Cyclic QUV exposure. Please note that a positive IAD value represents photodarkening and a negative IAD value represents photobleaching. exposed in QSUN. That may be caused by the haze formation. The IAD values of UVO in the three exposures are very low compared to the high IAD value of stabilizer-free UVT samples. Same as the UVO, no obvious change of absorbance and IAD were observed in Results 67
Figure 4.25. Abs/cm spectra for FF1 under QSUN exposure.
Figure 4.26. Induced absorbance to dose (IAD) spectra for FF1 under QSUN exposure. the other 3 highly-stabilized grades of PMMA. More data can be found in the Appendix
B from Figure B1 to Figure B2. Results 68
Figure 4.27. Abs/cm spectra for UVO under Hot QUV exposure.
Figure 4.28. Induced absorbance to dose (IAD) spectra for UVO under Hot QUV exposure. Please note that a positive IAD value represents pho- todarkening and a negative IAD value represents photobleaching. Results 69
Figure 4.29. Abs/cm spectra for UVO under Cyclic QUV exposure.
Figure 4.30. Induced absorbance to dose (IAD) spectra for UVO under Cyclic QUV exposure. Please note that a positive IAD value represents photodarkening and a negative IAD value represents photobleaching. Results 70
Figure 4.31. Abs/cm spectra for UVO under QSUN exposure.
Figure 4.32. Induced absorbance to dose (IAD) spectra for UVO under QSUN exposure. Please note that a positive IAD value represents pho- todarkening and a negative IAD value represents photobleaching. Results 71
4.4 Urbach fit results for 6 grades of PMMA
To further extract the relevant degradation information of PMMAs from spectral data,
Urbach edge fits were utilized to evaluate the band structure change in bulk material.
The Urbach edge fits were performed with baseline data of UV-Vis spectroscopy for the
6 grades of PMMAs as shown in Figure 4.33.
Then the degraded samples of UVT and FF1 were analyzed by the Urbach edge fits as shown in Figure 4.36 and Figure 4.37, respectively. No obvious change of Urbach edge position and Urbach edge width was observed due to the protection of high level of light stabilizers in other 4 grades of PMMA.
The UV-Vis spectrum of UVT (pure PMMA) has an absorption band at 275 nm to
305nm region as shown in Figure 4.34. The absorption band is attributed to n-π? tran-
sition of ester bond in the side chain of PMMA monomer108. The absorption of residual
Figure 4.33. The Urbach fit analysis of neat samples without exposure Results 72
Figure 4.34. The Urbach fit analysis of UVT without exposure
Figure 4.35. Lorenzian fit of the absorbance of residual monomer. Results 73
Figure 4.36. The Urbach fit analysis of UVT in Hot QUV
Figure 4.37. The Urbach fit analysis of FF1 in Hot QUV
monomer was analyzed by Lorentzian fitting as shown in Figure 4.35 with a absorption center at 4.25 eV (292 nm). Results 74
All the other five formulations are UV stabilized. The order from least to most stabi- lized is UVT, FF1, UVO, UVP,UVF,and UVA.
4.5 Self-organizing map as a tool to explore degradation pat- terns in 6 grades of PMMA
The self-organizing map techniques was applied to 30 fluorescence spectra (raw data shown in Figure 4.38 and Figure 4.39) of PMMA degradation to explore degradation pat- terns in the 6 grades of PMMA. The scatter was removed (Figure 4.40) from the EEM flu- orescence spectra for self-organizing map analysis. After removal of scatter, the three- dimensional EEM data were transformed to a two-dimensional data set. In the trans- formed two-dimensional data set, each row represents the fluorescence intensity of one
EEM data at each emission-excitation wavelength pair. In the data set, the NaN values are replaced with zero. To improve the accuracy of the algorithm, the data was normal- ized prior to SOM analysis.
The map built from 30 fluorescence spectra of neat and degraded PMMA samples in
Hot QUV has the dimensions 10 neurons x 3 neurons as shown in Figure 4.41. The uni-
fied distance matrix (U-matrix) visualizes the distance between neurons with more units than the map size of SOM. The distribution of each sample on SOM map was shown to- gether with U-matrix.
Each neuron has the information of all samples linked to the wining neuron. Please note that each neuron could be the winning neuron for multiple samples if similarity of
fluorescence data is high. Only the sample has the most instances is shown in the SOM map in Figure 4.41. From the sample distribution, samples close to each other have similar fluorescence properties, i.e. same chromophore that leads to yellowing, same Results 75
PMMA Brand Label Antioxidant HALS UV absorber grade UVT A 1 – – – FF1 A 2 – – Tinuvin P (possible fragment) UVA A 3 Irganox 1076 Tinuvin 292 ? UVO B 4 Irganox 1076 – ? UVP B 5 Irganox 1076 – Tinuvin P,Tinuvin 327 UVF B 6 Irganox 1076 – Tinuvin P,Tinuvin 327 Table 4.2. Additives information for PMMA in Hot QUV at 0 hour with brand name and label for self-organizing map. The ’-’ stands for that the stabilizer was not detected and the ’?’ stands for the stabilizer should be in formulation based on UV-Vis spectroscopy but was not detected by GC/MS.
locations of UV absorbers peaks. The map shows that the upper right side corner is neat
PMMA of UVT, UVA, and FF1 from A company. The lower left part of the map is the location for neat UVO, UVP, and UVF from B company. The different location of neat
PMMA samples indicates that they may have different types of UV absorber or other different additives.
The SOM analysis can also be visualized by hit histogram. The importance of each neuron is indicated by the size markers and the number of hits in each neuron. There are 6 hits for neuron 21 in the upper right corner, whereas only 1 hits for neuron 9 and 10 in the lower left corner. To compare hit response for neat and corresponding degraded
PMMA samples for UVT, the results are shown in Figure 4.42 and 4.43. For UVT, it shows great variation between neat and degraded samples that is indicative of fast degradation and yellowing or UV absorber loss. The intensity is increasing as the polymers degrade with increasing yellowness index. Conversely, UVA shows no variation between neat and degraded samples that is indicative of the lowest degradation. In Figure 4.43, the three grades of PMMA form brand B shows very similar trend of the change in fluorescence Results 76
Figure 4.38. The excitation-emission matrix fluorescence spectra of sam- ples from brand A. properties. For neat PMMA of UVO, UVP,and UVF,it reveals a significant different trend of fluorescence properties from the three grades from brand A during yellowing. The intensity is decreasing as the polymers degrade with increasing yellowness index. Results 77
Figure 4.39. The excitation-emission matrix fluorescence spectra of sam- ples from brand B.
Figure 4.40. The removal of scatter from the excitation-emission matrix fluorescence spectra of Hot QUV samples Results 78
Figure 4.41. The U-matrix (left) and self-organized map (right) of 30 excitation-emission matrix fluorescence spectra of Hot QUV samples. Sample distribution was labled on the SOM map. Labelling used (e.g. 1a0 - UVT sample without any exposure at step 0 (0 hour exposure); 1d5 - UVT sample at step 5 (3200 hours exposure))
Figure 4.42. Hit histograms: single hit histogram (a), multiple hit his- tograms for UVT (b), FF1 (C), and UVA(d) from brand A. Red color - samples exposed 3200 hours in Hot QUV; Green color - baseline samples without any exposure; Yellow color - samples exposed at 800 hours, 1200 hours, 2200 hours. Results 79
Figure 4.43. Hit histograms: single hit histogram (a), multiple hit his- tograms for Optix (b), UV0 (C), and UVF(d) from brand B. Red color - samples exposed 3200 hours in Hot QUV; Green color - baseline samples without any exposure; Yellow color - samples exposed at 800 hours, 1200 hours, 2200 hours.
4.5.1 Network model: Stressor Mechani sm Response < | | >
One of the goals of this project is to predict property change on the basis of exposure dose and exposure conditions. A predictive netSEM model of PMMA degradation rep- resents the systematic information that exposure dose and exposure conditions pro- vide about the property change of PMMA samples. The model of UVT samples exposed in Hot QUV, with UV photodose (UVdose) as stressor variable, IAD at 275nm (IAD275) and IAD at 400nm (IAD400) as tracking variable for degrdation mechanims, and YI as response variable, was shown in Figure 4.44. The model of FF1 samples exposed in
Hot QUV, with UV photodose (UVdose) as stressor variable, IAD at 275nm (IAD275),
IAD at 339nm (IAD339) and IAD at 400nm (IAD400) as tracking variable for degrdation mechanims, and YI as response variable, was shown in Figure 4.45. Pairwise plots of the raw data for pathway model can be found in Figure B9 and B10 in Appendix B. Results 80
Different tracking variables, IAD at 275nm (corresponding the change of fundamen- tal absorption edge), 339nm (corresponding the photobleaching of Tinuvin P) and 440nm
(corresponding the yellowing chromophores), were used to track different degradation mechanism and understand the yellowing process in a mechanistic way. The model demonstrates that the yellowing process is related to the change of fundamental ab- sorptiond edge, photobleaching of UV abrosber, and the chromophores generated from degradation. For netSEM model of the stabilizer-free UVT, it demonstrates that IAD275, corresponding the fundamental absorption edge, has strong relationship with both UV dose (Adjusted R2=0.949) and YI (Adjusted R2=0.991), indicating the tracking variable
IAD275 tracks the degradation process well. For the netSEM model of the UV-stabilized
FF1, it demonstrates that IAD275, corresponding the fundamental absorption edge, has strong relationship with both UV dose (Adjusted R2=0.829) and YI (Adjusted R2=0.878)
as seen in UVT samples. The IAD400, corresponding the tracking variable related to UV
absorber Tinuvin degradation, has weak relationship with UV dose (Adjusted R2=0.289)
and YI (Adjusted R2=0.336). The weak relationship can be attributed to that the UV dose
is not high enough to degrade most of the UV absorber and UV absorber protect the
polymer efficiently. Results 81
Figure 4.44. Pathway model with mechanistic variables: IAD at 275nm (corresponding Fund_Abs_Edge in the blue square, the change of fun- damental absorption edge) and 400nm (corresponding Chromo in the blue square, the yellowing chromophores). The fitting models (Mod) and the adjusted R2 values (adj-R-sqr) for the relationship of each pair of variables are labeled along the connection lines. The models for fitting different realtionships are SL (simple linear), SQuad (simple quadratic), Quad(quadratic), Exp (exponential), Log (logarithmic), CP (change point), and nls (non-linear least squares regression) Results 82
Figure 4.45. Pathway model with mechanistic variables: IAD at 275nm (corresponding Fund_Abs_Edge in the blue square, the change of fun- damental absorption edge), 339nm (corresponding Tinuvin in the blue square, the photobleaching of Tinuvin P) and 400nm (corresponding Chromo in the blue square, the yellowing chromophores). The fitting models (Mod) and the adjusted R2 values (adj-R-sqr) for the relationship of each pair of variables are labeled along the connection lines. The mod- els for fitting different realtionships are SL (simple linear), SQuad (sim- ple quadratic), Quad(quadratic), Exp (exponential), Log (logarithmic), CP (change point), and nls (non-linear least squares regression) 83
5 Discussion
5.1 Urbach edge analysis
It is observed that increasing UV dose increases the absorption of UVT samples in fig- ure 4.15. Therefore the Urbach edge position moved from shorter wavelength to visible region. In addition, the Urbach edge width became wider as the absorption of light in- creases. Summary of Urbach edge fit parameters for baseline samples of the six grades of PMMA, UVT and FF1 in hot QUV are shown in Table 5.1, Table 5.2, and Table 5.3 re- spectively. The wider absorption of degraded UVT is attributed to the chromophores generated in the yellowing process.
5.1.1 Urbach edge position of the neat PMMA samples
In the Urbach analysis of baseline samples, the Urbach edge position and width are sum- marized in table 5.1. Formulations with UV absorber have Urbach edge at longer wave- length. Due to the different structure of UV absorber, the Urbach edge width is randomly distributed for different samples. It is observed that UVT that has no UV absorbers has
Urbach edge at 285nm. It can be seen that the UVT has a weak absorption band from
280nm to 310nm that overlaps the absorption bands arising from pure PMMA. Based on literature52, pure MMA monomer has strong absorption from 280nm to 310nm. By using Urbach analysis on UVT without UV absorber, the monomer absorption peak was Discussion 84
successfully detected. The MMA monomer residuals in PMMA product have an impor- tant effect on the rate of chain scissions and yellowing in degradation process. It shows in table 5.2 that the Urbach edge position of UVT shifts from 285 to 398 and Urbach edge width become wider due to the degradation accelerated by monomer residual. Chro- mophores generated during degradation also make the Urbach tail become less expo- nential. It shows in table 5.3 that the Urbach edge position of FF1 shifts from 375nm to
377nm and Urbach edge width become slightly wider due to the protection of UV ab- sorber. The Urbach edge analysis can be a numeric indicator for PMMA degradation and yellowing process. Samples with more UV absorber show Urbach edge width values rage from 0.04 eV to 0.09 eV.
PMMA Step Exposure Urbach edge Urbach edge Urbach edge grade time (hour) Position (nm) Position (eV) width (eV) UVT 0 0 284 4.36 0.36 FF1 0 0 375 3.30 0.09 UVO 0 0 382 3.24 0.06 UVP 0 0 391 3.17 0.06 UVF 0 0 393 3.15 0.06 UVA 0 0 397 3.12 0.04 Table 5.1. Urbach edge fit parameters for PMMA in Hot QUV at 0 hour
PMMA Step Exposure Urbach edge Urbach edge Urbach edge grade time (hour) Position (nm) Position (eV) width (eV) UVT 0 0 285 4.35 0.36 UVT 1 400 320 3.87 0.44 UVT 2 800 324 3.82 0.44 UVT 3 1200 343 3.61 0.57 UVT 4 2200 373 3.32 0.73 UVT 5 3200 398 3.11 0.84 Table 5.2. Urbach edge fit parameters for UVT PMMA in Hot QUV Discussion 85
PMMA Step Exposure Urbach edge Urbach edge Urbach edge grade time (hour) Position (nm) Position (eV) width (eV) FF1 0 0 375 3.30 0.09 FF1 1 400 375 3.30 0.09 FF1 2 800 375 3.30 0.10 FF1 3 1200 376 3.29 0.11 FF1 4 2200 376 3.29 0.12 FF1 5 3200 377 3.28 0.13 Table 5.3. Urbach edge fit parameters for FF1 PMMA in Hot QUV
5.2 The effect of exposure types on degradation rate
Photodegradation mechanisms under different source of radiation will vary based on the spectral emission of light sources and spectral absorption of the polymer being ex- posed. To understand the wavelength dependent degradation process, yellowing, pho- todarkening, and photobleaching rates can be used to quantify the degradation rate un- der different exposure conditions. It was observed that Hot QUV causes the highest yel- lowing rate when compare with Cyclic QUV and QSUN on a degradation per dose basis as shown in Figure 5.1. The high IAD vale for UVT and FF1 exposed in Hot QUV confirms with the yellowing results. This result is within expectation because samples in Hot QUV undergoes the UV light all the time without water cycle and the temperature of exposure chamber remains high at 70◦C. Yellowing, correponding a reduction of transmittance is usually attributed to the onxidation products formed during photodegradation. The re- didual monomer-induced photooxidation can accelerated the degradation by several radical reaction as dicussed in Chapter 2. In addition, the synergy effect of tempera- ture of radiation may also account for the highest yellowing rate observed in the highly intensified exposure in Hot QUV. Discussion 86
Figure 5.1. Induced absorbance to dose value for UVT and FF1 under 3 exposure conditions at 275nm, 298nm, 339nm, and 400nm.
5.3 Comparison of the durability of the 6 grades of PMMA
The understanding of degradation mechanisms, rate is critical for optimizing PMMA formulation and extending the liftime of PMMA. IAD values for the 6 grades of PMMA show that the stabilizer-free UVT samples absorbed more UV light and degraded faster compared to other PMMAs in all three exposure conditions as shown in Figure 5.1 and
5.2. Without the protection from stabilizer, the UV light can pass through the UVT sample multiple times due to the reflection of aluminum trays in side exposure cham- ber. Photobleaching due to the degradation of UV absorber was observed for medium- stabilized FF1 sample. The two distinct spectral features at 298nm and 339nm can be assigned to the absorption of Tinuvin P absorber. However, photodarkening was also Discussion 87
Figure 5.2. Induced absorbance to dose for UVP,UVO, UVF, and UVA un- der 3 exposure conditions. observed around 275nm, which may caused by the oxidized product of UV absorbers or polymer.
The other 4 grades of PMMA are considered highly stabilized, especially the UVF and
UVA. The highly-stabilized UVF and UVA showed a slight YI increase only between 0.5 to
0.7 compared to a significant YI increase of over 25 for the stabilizer-free UVT. Note that the UVF and UVA are formations that have more than one type of light stabilizer. That indicates the desirable highly radiation-durability can be achieved by proper combina- tion of different types of light stabilizer.
5.4 Degradation pathway models of PMMA
The network models of unstabilized and partially-stabilized samples demonstrate that the yellowing process is related significantly to the change of fundamental absorptiond Discussion 88
edge. Degradation pathway models of unstabilized and partially-stabilized in the < Stressor Mechani sm Response framework both demonstrate that IAD275, the track- | | > ing variable for the fundamental absorption edge of PMMA, has a strong relationship with both UV dose (Adjusted R2=0.82-0.94) and YI (Adjusted R2=0.87-0.99). The fitting models (Mod) and the adjusted R2 values (adj-R-sqr) for the relationship of each pair of variables are labeled along the connection lines as shown in Figure 4.43 and 4.44.
The models for fitting different realtionships are SL (simple linear), SQuad (simple qua- dratic), Quad(quadratic), Exp (exponential), Log (logarithmic), CP (change point), and nls (non-linear least squares regression). The best fit for each relationship is determined based on adjusted R2 values and domain knowledge. The best fits for pair-wise relation- ship between UV dose and IAD275 and relationship between IAD 275 and YI are both quadratic as shown in Figure 5.3. FF1 shows significant slower degradation rate than
UVT with the help of package of light stabilizers. Discussion 89
Figure 5.3. Degradation pathways for UVT and FF1 exposed in Hot QUV. Regression lines are obtained from pathway modes from netSEM analy- sis. The best fit for each relationship is determined based on adjusted R2 values and domain knowledge. 90
6 Conclusion
To optimize and extend the service life of polymer materials to more than 25 years in the outdoor environment, a domain knowledge-based and data-driven approach has been utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms and rates under various stepwise exposure conditions. The six grades of
PMMA, including one unstabilized and five stabilized PMMAs, were studied in three weathering conditions. The unstabilized PMMA showed a significant YI increase of over
25, whereas a highly-stabilized PMMA showed a slight YI increase only between 0.5 to
0.7. The amount of YI change depended on not only on the concentration of stabilizers but also the types of stabilizer. The combination of antioxidant, HALS and UV absorber may also play an essential role in the remarkably better durability of highly-stabilized
PMMA.
The use of different light sources, which are UVA-340 (Hot QUV and Cyclic QUV) and xenon arc full spectrum (Q-SUN) exposures, revealed that the degradation process is wavelength-dependent. The six grades of PMMA respond to the UVA-340 exposure at a higher yellowing rate than to the full spectrum exposures. The different performance caused by light sources is mitigated sharply for absorber-formulated PMMAs, compared to the PMMA without any light stabilizers. Conclusion 91
Three degradation categories, unstabilized, partially-stabilized, and highly-stabilized, each with characteristic behaviors,were revealed by self-organizing map (SOM) analy- sis on fluoresence data of baseline PMMA samples and degraded PMMA samples. The different degradation patterns originate from the PMMA formulations, in terms of the usage of stabilizers, the combination of stabilizers, and the concentration of stabilizers.
Given that SOM does not rely on assumptions regarding prior knowledge about vari- able distribution, variable relationships, SOM analysis is a powerful tool to improve the understanding of complicated process of polymer degradation.
The network models of unstabilized and partially-stabilized samples demonstrate that the yellowing process is related significantly to the change of fundamental absorp- tiond edge. Degradation pathway models of unstabilized and partially-stabilized in the
Stressor Mechani sm Response framework both demonstrate that IAD275, the < | | > tracking variable for the fundamental absorption edge of PMMA, has a strong relation- ship with both UV dose (Adjusted R2=0.82-0.94) and YI (Adjusted R2=0.87-0.99). Appendix 92
Appendix A Preparation of this document
This document was prepared using pdfLATEX and other open source tools. The (free) programs implemented are as follows:
LAT X implementation: • E MiKTEX
http://www.miktex.org
TEXLive
https://www.tug.org/texlive/
TEXstudio
https://https://www.texstudio.org
Bibliographical: • BibTEX
http://www.bibtex.org/
Zotero
https://www.zotero.org/ Appendix 93
Appendix B Figures 1 Absorbance data for highly stabilized formulations
The UV-Vis absorbance of UVP, UVF, and UVA does not change due to the inhibition of degradation pathways by a high concentration of light stabilizers. In Figure D.1 and
D.2, the absorbance and IAD data of UVA, the formulation with the highest amount of stabilizers exposed in Hot QUV, were shown, respectively. No changes of absorption edge was observed, indicating the efficacy of the combination of different types of light stabilizers.
Figure B.1. Abs/cm spectra for UVA under Hot QUV exposure. Appendix 94
Figure B.2. Induced absorbance to dose (IAD) spectra for UVA under Hot QUV exposure.
2 GC-MS data of the 6 grades of PMMA
No stabilizer was detected in the stabilizer free UVT samples, including baseline UVT and degraded UVT samples as shown in Figure D.3. Figure D.4 shows the GC-MS data for all the 3 formulations without exposure from brand A, UVT, FF1, and UVA. Figure D.5 shows the GC-MS data for all the 3 formulations without exposure from brand B, UVO,
UVP,and UVF. Appendix 95
Figure B.3. GC-MS data for UVT sample at baseline and at 3200 hours: sample 18021 is the UVT sample at baseline and sample 17438 si the UVT sample at 2200 hours exposed in Hot QUV.
Figure B.4. GC-MS analysis for UVT sample at baseline and at 3200 hours: sample 18021 is the UVT sample at baseline and sample 17438 si the UVT sample at 2200 hours exposed in Hot QUV. Appendix 96
Figure B.5. GC-MS data for UVT (18021), FF1 (18005), and UVA (18013) samples at baseline.
Figure B.6. GC-MS analysis for UVT (18021), FF1 (18005), and UVA (18013) samples at baseline. Appendix 97
Figure B.7. GC-MS data for UVO (1775), UVP (17643) and UVF (17830) samples at baseline.
Figure B.8. GC-MS analysis for UVO (1775), UVP (17643) and UVF (17830) samples at baseline. Appendix 98
3 Pairs plots for UVT and FF1 sample exposed in Hot QUV.
The pairsplots summarize the YI data for UVT and FF1 samples exposed in Hot QUV for
3200 hours. The pairs plot is a grid of scatterplots that displays the bivariate relationship between all pairs of variables in the multivariate degradation data set fo exploratory data analysis.
Figure B.9. Pairs plot for UVT sample exposed in Hot QUV.The mechanis- tic variables: IAD at 275 nm (corresponding the change of fundamental absorption edge) and IAD at 400 nm (corresponding the yellowing chro- mophores). Appendix 99
Figure B.10. Pairs plot for FF1 sample exposed in Hot QUV. The mecha- nistic variables: IAD at 275 nm (corresponding the change of fundamen- tal absorption edge), IAD at 339 nm (corresponding the photobleach- ing of Tinuvin P) and IAD at 400 nm (corresponding the yellowing chro- mophores). Bibliography 100
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