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231226s2024 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202300259
|2 doi
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|a pubmed24n1409.xml
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|a (DE-627)NLM354664026
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|a (NLM)36961263
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Liu, Chao
|e verfasserin
|4 aut
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|a Understanding Causalities in Organic Photovoltaics Device Degradation in a Machine-Learning-Driven High-Throughput Platform
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 16.05.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2023 The Authors. Advanced Materials published by Wiley‐VCH GmbH.
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|a Organic solar cells (OSCs) now approach power conversion efficiencies of 20%. However, in order to enter mass markets, problems in upscaling and operational lifetime have to be solved, both concerning the connection between processing conditions and active layer morphology. Morphological studies supporting the development of structure-process-property relations are time-consuming, complex, and expensive to undergo and for which statistics, needed to assess significance, are difficult to be collected. This work demonstrates that causal relationships between processing conditions, morphology, and stability can be obtained in a high-throughput method by combining low-cost automated experiments with data-driven analysis methods. An automatic spectral modeling feeds parametrized absorption data into a feature selection technique that is combined with Gaussian process regression to quantify deterministic relationships linking morphological features and processing conditions with device functionality. The effect of the active layer thickness and the morphological order is further modeled by drift-diffusion simulations and returns valuable insight into the underlying mechanisms for improving device stability by tuning the microstructure morphology with versatile approaches. Predicting microstructural features as a function of processing parameters is decisive know-how for the large-scale production of OSCs
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|a Journal Article
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|a Gaussian process regression (GPR) prediction
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|a device stability
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|a linear Pearson correlations
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| 650 |
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4 |
|a machine learning
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| 650 |
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|a organic solar cells
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| 700 |
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|a Lüer, Larry
|e verfasserin
|4 aut
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|a Corre, Vincent M Le
|e verfasserin
|4 aut
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|a Forberich, Karen
|e verfasserin
|4 aut
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|a Weitz, Paul
|e verfasserin
|4 aut
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|a Heumüller, Thomas
|e verfasserin
|4 aut
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|a Du, Xiaoyan
|e verfasserin
|4 aut
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|a Wortmann, Jonas
|e verfasserin
|4 aut
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|a Zhang, Jiyun
|e verfasserin
|4 aut
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|a Wagner, Jerrit
|e verfasserin
|4 aut
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|a Ying, Lei
|e verfasserin
|4 aut
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|a Hauch, Jens
|e verfasserin
|4 aut
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|a Li, Ning
|e verfasserin
|4 aut
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|a Brabec, Christoph J
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 20 vom: 26. Mai, Seite e2300259
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
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|g volume:36
|g year:2024
|g number:20
|g day:26
|g month:05
|g pages:e2300259
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|u http://dx.doi.org/10.1002/adma.202300259
|3 Volltext
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