Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

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 nece...

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Authors: Grau Luque, Enric, Anefnaf, Ikram, Benhaddou, Nada, Fonoll Rubio, Robert, Becerril Romero, Ignacio, Aazou, Safae, Saucedo Silva, Edgardo Ademar|||0000-0003-2123-6162, Sekkat, Zouheir, Pérez Rodríguez, Alejandro, Izquierdo Roca, Víctor, Guc, Maxim|||0000-0002-2072-9566
Format: article
Publication Date:2021
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/356118
Online Access:https://hdl.handle.net/2117/356118
https://dx.doi.org/10.1039/d1ta01299a
Access Level:Open access
Keyword:Solar cells
Cèl·lules solars
Àrees temàtiques de la UPC::Energies::Energia solar fotovoltaica::Cèl·lules solars
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spelling Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performanceGrau Luque, EnricAnefnaf, IkramBenhaddou, NadaFonoll Rubio, RobertBecerril Romero, IgnacioAazou, SafaeSaucedo Silva, Edgardo Ademar|||0000-0003-2123-6162Sekkat, ZouheirPérez Rodríguez, AlejandroIzquierdo Roca, VíctorGuc, Maxim|||0000-0002-2072-9566Solar cellsCèl·lules solarsÀrees temàtiques de la UPC::Energies::Energia solar fotovoltaica::Cèl·lules solarsSolar 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.Peer ReviewedRoyal Society of Chemistry (RSC)20212021-04-2820212021-11-11journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/356118https://dx.doi.org/10.1039/d1ta01299areponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3561182026-05-27T15:37:01Z
dc.title.none.fl_str_mv Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
title Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
spellingShingle Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
Grau Luque, Enric
Solar cells
Cèl·lules solars
Àrees temàtiques de la UPC::Energies::Energia solar fotovoltaica::Cèl·lules solars
title_short Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
title_full Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
title_fullStr Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
title_full_unstemmed Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
title_sort Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
dc.creator.none.fl_str_mv Grau Luque, Enric
Anefnaf, Ikram
Benhaddou, Nada
Fonoll Rubio, Robert
Becerril Romero, Ignacio
Aazou, Safae
Saucedo Silva, Edgardo Ademar|||0000-0003-2123-6162
Sekkat, Zouheir
Pérez Rodríguez, Alejandro
Izquierdo Roca, Víctor
Guc, Maxim|||0000-0002-2072-9566
author Grau Luque, Enric
author_facet Grau Luque, Enric
Anefnaf, Ikram
Benhaddou, Nada
Fonoll Rubio, Robert
Becerril Romero, Ignacio
Aazou, Safae
Saucedo Silva, Edgardo Ademar|||0000-0003-2123-6162
Sekkat, Zouheir
Pérez Rodríguez, Alejandro
Izquierdo Roca, Víctor
Guc, Maxim|||0000-0002-2072-9566
author_role author
author2 Anefnaf, Ikram
Benhaddou, Nada
Fonoll Rubio, Robert
Becerril Romero, Ignacio
Aazou, Safae
Saucedo Silva, Edgardo Ademar|||0000-0003-2123-6162
Sekkat, Zouheir
Pérez Rodríguez, Alejandro
Izquierdo Roca, Víctor
Guc, Maxim|||0000-0002-2072-9566
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Solar cells
Cèl·lules solars
Àrees temàtiques de la UPC::Energies::Energia solar fotovoltaica::Cèl·lules solars
topic Solar cells
Cèl·lules solars
Àrees temàtiques de la UPC::Energies::Energia solar fotovoltaica::Cèl·lules solars
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-04-28
2021
2021-11-11
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/356118
https://dx.doi.org/10.1039/d1ta01299a
url https://hdl.handle.net/2117/356118
https://dx.doi.org/10.1039/d1ta01299a
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Royal Society of Chemistry (RSC)
publisher.none.fl_str_mv Royal Society of Chemistry (RSC)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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