Degradation science: Mesoscopic evolution and temporal analytics of photovoltaic energy materials

French, Roger H.; Podgornik, Rudolf; Peshek, Timothy J.; Bruckman, Laura S.; Xu, Yifan; Wheeler, Nicholas R.; Gok, Abdülkerim; Hu, Yang; Hossain, Mohammad A.; Gordon, Devin A.; Zhao, Pei; Sun, Jiayang; Zhang, Guo Qiang

doi:10.1016/j.cossms.2014.12.008

Cited by 53 publications

(18 citation statements)

References 86 publications

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“…In this degradation science study, the epidemiological data science approach was applied in a laboratory‐based, randomized, longitudinal study design . Samples of unstabilized PET films (Dupont‐Teijin Melinex 454, 75 μm) were randomly assigned to exposure conditions and followed over time with repeated measurements.…”

Section: Methodsmentioning

confidence: 99%

Reciprocity and spectral effects of the degradation of poly(ethylene‐terephthalate) under accelerated weathering exposures

Gok

Gordon

Burns³

et al. 2019

J of Applied Polymer Sci

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Exposures of poly(ethylene‐terephthalate) (PET) films were performed under xenon‐arc (full spectrum) and fluorescent UVA‐340 (UV only) irradiation to investigate the reciprocity principle and wavelength dependence of photodegradation in weathering. When the intensity of full spectral irradiance is increased from 5× to 50×, the reciprocity principle is not obeyed in this material system. The change in optical properties for PET is attributed to the corresponding longer exposure time of the 5× exposure that allows oxidative reactions to occur at lower irradiance. Full spectrum xenon‐arc and fluorescent UVA‐340 lamps have different spectral distributions and samples exhibits different material degradation mechanisms under each source. The UVA‐340 exposure leads to more pronounced degradation relative to the full spectrum xenon‐arc. This study shows that intensification of a single stressor in lab‐based weathering experiments to accelerate degradation and the use of differing light sources are not sufficient to allow reliable determination of service lifetime of polymeric materials. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47589.

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Section: Methodsmentioning

confidence: 99%

Reciprocity and spectral effects of the degradation of poly(ethylene‐terephthalate) under accelerated weathering exposures

Gok

Gordon

Burns³

et al. 2019

J of Applied Polymer Sci

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“…29 Handling and analyzing the massive amount of time-series data from these sites is critical for understanding PV system degradation and lifetime performance (ie, the performance loss rate). 30 Combined with a nonrelational data warehouse (wherein data storage is optimized for the specific requirements of the type of data being stored, rather than structured in a typical tabular schema), his research group uses Apache Hadoop 31 / Hbase/Spark 32 architecture for distributed and high-performance (ie, petabyte and petaflop) computing (Dist/HPC) for three different types of modeling: predictive modeling to identify stress and response relationships; 33 network/inferential modeling for stress-mechanism-response relationships; 34 and machine learning modeling for image processing and…”

Section: Data Science: Informatics and Analyticsmentioning

confidence: 99%

Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Guire

Bartolo

Brindle³

et al. 2019

J Am Ceram Soc

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Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics to develop better functional materials more efficiently. Simultaneously, the data science community is looking for a way to access materials data resources to test and validate their advanced computational learning algorithms. To address this issue, The American Ceramic Society (ACerS) convened a Glass and Ceramic Data Science Workshop in February 2018, sponsored by the National Institute for Standards and Technology (NIST) Advanced Manufacturing Technologies (AMTech) program. The workshop brought together a select group of leaders in the data science, informatics, and glass and ceramics communities, ACerS, and Nexight Group to identify the greatest opportunities and mechanisms for facilitating increased collaboration and coordination between these communities. This article summarizes workshop discussions about the current challenges that limit interactions and collaboration between the glass and ceramic and data science communities, opportunities for a coordinated approach that leverages existing knowledge in both communities, and a clear path toward the enhanced use of data science technologies for functional glass and ceramic research and development.

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“…9 In L&DS, a stress, mechanism, and response framework 10, 11 ({S|M|R}) is used to explore changes in a system degrading under stress. To summarize a major theme, a transition is needed from {S|R} models 12 that only predict responses numerically, towards bottoms up physics-based models that are constrained to known physical and chemical mechanisms.…”

Section: Lifetime and Degradation Sciencementioning

confidence: 99%

A data science approach to understanding photovoltaic module degradation

et al. 2015

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The expected lifetime performance and degradation of photovoltaic (PV) modules is a major issue facing the levelized cost of electricity of PV as a competitive energy source. Studies that quantify the rates and mechanisms of performance degradation are needed not only for bankability and adoption of these promising technologies, but also for the diagnosis and improvement of their mechanistic degradation pathways. Towards this goal, a generalizable approach to degradation science studies utilizing data science principles has been developed and applied to c-Si PV modules. By combining domain knowledge and data derived insights, mechanistic degradation pathways are indicated that link environmental stressors to the degradation of PV module performance characteristics. Targeted studies guided by these results have yielded predictive equations describing rates of degradation, and further studies are underway to achieve this for additional mechanistic pathways of interest.

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Degradation science: Mesoscopic evolution and temporal analytics of photovoltaic energy materials

Cited by 53 publications

References 86 publications

Reciprocity and spectral effects of the degradation of poly(ethylene‐terephthalate) under accelerated weathering exposures

Reciprocity and spectral effects of the degradation of poly(ethylene‐terephthalate) under accelerated weathering exposures

Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

A data science approach to understanding photovoltaic module degradation

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