Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dots
Abstract The development of recipes for synthesis of quantum dots (QDs), a novel semiconductor material for application in optoelectronic devices, is currently purely based on experiments. Since the quality of QDs represented by quantum yield (QY) and emission peak strongly depends on a number of di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-02-01
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| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.12122 |
| _version_ | 1819289550754676736 |
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| author | Min Fu Thomas Mrziglod Weiling Luan Shan‐Tung Tu Leslaw Mleczko |
| author_facet | Min Fu Thomas Mrziglod Weiling Luan Shan‐Tung Tu Leslaw Mleczko |
| author_sort | Min Fu |
| collection | DOAJ |
| description | Abstract The development of recipes for synthesis of quantum dots (QDs), a novel semiconductor material for application in optoelectronic devices, is currently purely based on experiments. Since the quality of QDs represented by quantum yield (QY) and emission peak strongly depends on a number of different parameters (route, precursors, conditions, etc), a large number of experiments is necessary. In this article, we show that data‐driven modeling can be used as a supporting tool for optimization and a better understanding of the synthesis process. By using the results collected during the development of CuInS2/ZnS (CIS/ZnS) QDs, a neural network model has been established. The model is able to predict the optical properties (QY and emission peak) of CIS/ZnS QDs as a function of the most important synthesis parameters, such as reaction temperature, time of CIS core formation and ZnS shell growth, feed molar ratio of Cu/In and Zn/Cu, various starting precursors, and types of ligands. Finally, a model analysis under various effects influencing the quality of QDs is performed. |
| first_indexed | 2024-12-24T03:08:38Z |
| format | Article |
| id | doaj.art-4cd74dda622040d99d65b1403033c45d |
| institution | Directory Open Access Journal |
| issn | 2577-8196 |
| language | English |
| last_indexed | 2024-12-24T03:08:38Z |
| publishDate | 2020-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj.art-4cd74dda622040d99d65b1403033c45d2022-12-21T17:17:55ZengWileyEngineering Reports2577-81962020-02-0122n/an/a10.1002/eng2.12122Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dotsMin Fu0Thomas Mrziglod1Weiling Luan2Shan‐Tung Tu3Leslaw Mleczko4Key Laboratory of Pressure Systems and Safety (MOE), School of Mechanical and Power Engineering East China University of Science and Technology Shanghai ChinaEngineering & Technology Bayer AG Leverkusen GermanyKey Laboratory of Pressure Systems and Safety (MOE), School of Mechanical and Power Engineering East China University of Science and Technology Shanghai ChinaKey Laboratory of Pressure Systems and Safety (MOE), School of Mechanical and Power Engineering East China University of Science and Technology Shanghai ChinaEngineering & Technology Bayer AG Leverkusen GermanyAbstract The development of recipes for synthesis of quantum dots (QDs), a novel semiconductor material for application in optoelectronic devices, is currently purely based on experiments. Since the quality of QDs represented by quantum yield (QY) and emission peak strongly depends on a number of different parameters (route, precursors, conditions, etc), a large number of experiments is necessary. In this article, we show that data‐driven modeling can be used as a supporting tool for optimization and a better understanding of the synthesis process. By using the results collected during the development of CuInS2/ZnS (CIS/ZnS) QDs, a neural network model has been established. The model is able to predict the optical properties (QY and emission peak) of CIS/ZnS QDs as a function of the most important synthesis parameters, such as reaction temperature, time of CIS core formation and ZnS shell growth, feed molar ratio of Cu/In and Zn/Cu, various starting precursors, and types of ligands. Finally, a model analysis under various effects influencing the quality of QDs is performed.https://doi.org/10.1002/eng2.12122CuInS2/ZnSneural networkoptimizationquantum dotssimulation |
| spellingShingle | Min Fu Thomas Mrziglod Weiling Luan Shan‐Tung Tu Leslaw Mleczko Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dots Engineering Reports CuInS2/ZnS neural network optimization quantum dots simulation |
| title | Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dots |
| title_full | Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dots |
| title_fullStr | Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dots |
| title_full_unstemmed | Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dots |
| title_short | Neural network modeling and simulation of the synthesis of CuInS2/ZnS quantum dots |
| title_sort | neural network modeling and simulation of the synthesis of cuins2 zns quantum dots |
| topic | CuInS2/ZnS neural network optimization quantum dots simulation |
| url | https://doi.org/10.1002/eng2.12122 |
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