Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
| Autor: | Maxim Guc, Victor Izquierdo-Roca, Zouheir Sekkat, Safae Aazou, Robert Fonoll-Rubio, Ignacio Becerril-Romero, Alejandro Pérez-Rodríguez, Nada Benhaddou, Ikram Anefnaf, Enric Grau-Luque, Edgardo Saucedo |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Institut de Recerca en Energía de Catalunya, Universitat Politècnica de Catalunya. MNT - Grup de Recerca en Micro i Nanotecnologies |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Solar cells
Work (thermodynamics) Materials science Process (engineering) Complex system 02 engineering and technology engineering.material 010402 general chemistry Machine learning computer.software_genre 7. Clean energy 01 natural sciences law.invention symbols.namesake law Solar cell Performance prediction General Materials Science Kesterite Renewable Energy Sustainability and the Environment business.industry Photovoltaic system Energies::Energia solar fotovoltaica::Cèl·lules solars [Àrees temàtiques de la UPC] General Chemistry 021001 nanoscience & nanotechnology 0104 chemical sciences symbols engineering Cèl·lules solars Artificial intelligence 0210 nano-technology Raman spectroscopy business computer |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | Solar cells based on quaternary kesterite compounds like Cu2ZnGeSe4 are complex systems where the variation of one parameter can result in changes in the whole system, and, as consequence, in the global performance of the devices. In this way, analyses that take into account this complexity are necessary in order to overcome the existing limitations of this promising Earth-abundant photovoltaic technology. This study presents a combinatorial approach for the analysis of Cu2ZnGeSe4 based solar cells. A compositional graded sample containing almost 200 solar cells with different [Zn]/[Ge] compositions is analyzed by means of X-ray fluorescence and Raman spectroscopy, and the results are correlated with the optoelectronic parameters of the different cells. The analysis results in a deep understanding of the stoichiometric limits and point defects formation in the Cu2ZnGeSe4 compound, and shows the influence of these parameters on the performance of the devices. Then, intertwined connections between the compositional, vibrational and optoelectronic properties of the cells are revealed using a complex analytical approach. This is further extended using a machine learning algorithm. The latter confirms the correlation between the properties of the Cu2ZnGeSe4 compound and the optoelectronic parameters, and also allows proposing a methodology for device performance prediction that is compatible with both research and industrial process monitoring environments. As such, this work not only provides valuable insights for understanding and further developing the Cu2ZnGeSe4 photovoltaic technology, but also gives a practical example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research. |
| Databáze: | OpenAIRE |
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