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4 results on '"Kant Gupta, Ritesh"'

2. Supporting Information: Artificial intelligence-based, wavelet-aided prediction of long-term outdoor performance of perovskite solar cells

Author

Kouroudis, Ioannis, Tabah, Kenedy, Karimipour, Masoud, Ben Ali, Aziz, Kishore Kumar, D., Sudhakar, Vediappan, Kant Gupta, Ritesh, Visoly-Fisher, Iris, Lira-Cantú, Mónica, Gagliardi, Alessio, Kouroudis, Ioannis, Tabah, Kenedy, Karimipour, Masoud, Ben Ali, Aziz, Kishore Kumar, D., Sudhakar, Vediappan, Kant Gupta, Ritesh, Visoly-Fisher, Iris, Lira-Cantú, Mónica, and Gagliardi, Alessio

Published
2024

3. Artificial intelligence-based, wavelet-aided prediction of long-term outdoor performance of perovskite solar cells

Author

Ministerio de Educación y Ciencia (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia e Innovación (España), Ministerio de Ciencia, Innovación y Universidades (España), Federal Ministry for Economics Affairs and Energy (Germany), Ministry of Energy (Israel), European Commission, European Research Council, German Research Foundation, Universidad Autónoma de Barcelona, Kouroudis, Ioannis, Tabah, Kenedy, Karimipour, Masoud, Ben Ali, Aziz, Kishore Kumar, D., Sudhakar, Vediappan, Kant Gupta, Ritesh, Visoly-Fisher, Iris, Lira-Cantú, Mónica, Gagliardi, Alessio, Ministerio de Educación y Ciencia (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia e Innovación (España), Ministerio de Ciencia, Innovación y Universidades (España), Federal Ministry for Economics Affairs and Energy (Germany), Ministry of Energy (Israel), European Commission, European Research Council, German Research Foundation, Universidad Autónoma de Barcelona, Kouroudis, Ioannis, Tabah, Kenedy, Karimipour, Masoud, Ben Ali, Aziz, Kishore Kumar, D., Sudhakar, Vediappan, Kant Gupta, Ritesh, Visoly-Fisher, Iris, Lira-Cantú, Mónica, and Gagliardi, Alessio

Abstract

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.

Published
2024

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