spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.

Grau-Luque, Enric; Atlan, Fabien; Becerril‐Romero, Ignacio; Pérez-Rodrı́guez, A.; Guc, Maxim; Izquierdo-Roca, Víctor

doi:10.21105/joss.03781

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Taking this into account, in the case of spectroscopic techniques (as Raman and PL), the group from IREC has developed a Python package spectrapepper, which includes several useful functions allowing to treat big amount of data in an easy and fast way. [ 110 ] For other techniques similar packages can be found or need to be developed with the participation of the experienced researchers in the field, as automation seems to be the most logic evolution in the research data treatment in the nearest future.…”

Section: Practical Considerations For the Optimal Implementation Of C...mentioning

confidence: 99%

“…Thus application of Combinograms for the data analysis and presentation can be a key for the combinatorial analysis of thin‐film PV devices. In the recent years, some software tools have been developed to help researchers in performing combinatorial analysis and correct results presentations, which is the case of the aforementioned spectrapepper [ 110 ] and the COMBIgor [ 116 ] software packages.…”

Section: Practical Considerations For the Optimal Implementation Of C...mentioning

confidence: 99%

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Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

et al. 2022

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One of the fastest ways for the discovery, understanding, development, and further optimization of new complex materials is the application of combinatorial analysis methodologies, which have already shown impressive results for different organic and inorganic materials, leading to the fast development of different scientific fields and industrial applications. However, in the case of thinfilm materials for optoelectronic devices and, in particular, for second-generation photovoltaic (PV) devices, the application of combinatorial analysis is still quite uncommon with a desultory rather than systematic application. The present review discusses the main constraints for the application of combinatorial analysis to thin-film materials with a focus on chalcogenide compounds and different strategies to overcome them. Special attention is paid to the requirements for the preparation of graded thin films, characterization, and analysis of the results, providing different hints for the implementation of high-quality combinatorial analysis. Finally, an overview of the currently published results in the field of chalcogenide thin-film PV technologies is presented, showing the relevance of the combinatorial approach for boosting the development not only of this promising PV technology, but also of other optoelectronic devices based on complex materials and multilayered structures.

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Section: Practical Considerations For the Optimal Implementation Of C...mentioning

confidence: 99%

Section: Practical Considerations For the Optimal Implementation Of C...mentioning

confidence: 99%

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

et al. 2022

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Insights into the Effects of RbF‐Post‐Deposition Treatments on the Absorber Surface of High Efficiency Cu(In,Ga)Se₂ Solar Cells and Development of Analytical and Machine Learning Process Monitoring Methodologies Based on Combinatorial Analysis

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Grau‐Luque

et al. 2022

Advanced Energy Materials

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The latest record efficiencies of the Cu(In,Ga)Se2 (CIGSe) photovoltaic technology are driven by heavy alkali post‐deposition treatments (PDTs). Despite their positive effect, especially in the Voc of the devices, their underlying mechanisms are still under discussion. This work sheds light on this topic by performing a high statistics analysis on 620 high efficiency CIGSe solar cells submitted to four different PDT processes (different RbF source temperature) employing a combinatorial approach based on Raman and photoluminescence (PL) spectroscopies. This reveals with statistical confidence subtle differences in the spectroscopic data that relate to the redistribution of defects between the ordered vacancy compound (OVC) and the chalcopyrite phases at the absorber surface. In particular, there is an intertwined decrease of the OVC phase and increase of the so‐called “defective chalcopyrite phase.” Additionally, two industry‐compatible methodologies for the assessment of the RbF‐PDT process and prediction of the Voc of the final devices with a ±2% error and an efficacy of ≈95% are developed based on analytical and machine learning approaches. These results show that the combined Raman and PL spectroscopic techniques represent a powerful tool for the future development of the CIGSe technology at a research level and for more efficient industrial manufacturing.

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Accelerating the Development of Thin Film Photovoltaic Technologies: An Artificial Intelligence Assisted Methodology Using Spectroscopic and Optoelectronic Techniques

Grau‐Luque,

Becerril‐Romero,

Atlan

et al. 2024

Small Methods

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Thin film photovoltaic (TFPV) materials and devices present a high complexity with multiscale, multilayer, and multielement structures and with complex fabrication procedures. To deal with this complexity, the evaluation of their physicochemical properties is critical for generating a model that proposes strategies for their development and optimization. However, this process is time‐consuming and requires high expertise. In this context, the adoption of combinatorial analysis (CA) and artificial intelligence (AI) strategies represents a powerful asset for accelerating the development of these complex materials and devices. This work introduces a methodology to facilitate the adoption of AI and CA for the development of TFPV technologies. The methodology covers all the necessary steps from the synthesis of samples for CA to data acquisition, AI‐assisted data analysis, and the extraction of relevant information for research acceleration. Each step provides details on the necessary concepts, requirements, and procedures and are illustrated with examples from the literature. Then, the application of the methodology to a complex set of samples from a TFPV production line highlights its ability to rapidly glean significant insights even in intricate scenarios. The proposed methodology can be applied to other types of materials and devices beyond PV and using different characterization techniques.

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spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.

Cited by 3 publications

References 19 publications

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Insights into the Effects of RbF‐Post‐Deposition Treatments on the Absorber Surface of High Efficiency Cu(In,Ga)Se₂ Solar Cells and Development of Analytical and Machine Learning Process Monitoring Methodologies Based on Combinatorial Analysis

Accelerating the Development of Thin Film Photovoltaic Technologies: An Artificial Intelligence Assisted Methodology Using Spectroscopic and Optoelectronic Techniques

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spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.

Cited by 3 publications

References 19 publications

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Insights into the Effects of RbF‐Post‐Deposition Treatments on the Absorber Surface of High Efficiency Cu(In,Ga)Se2 Solar Cells and Development of Analytical and Machine Learning Process Monitoring Methodologies Based on Combinatorial Analysis

Accelerating the Development of Thin Film Photovoltaic Technologies: An Artificial Intelligence Assisted Methodology Using Spectroscopic and Optoelectronic Techniques

Contact Info

Product

Resources

About

Insights into the Effects of RbF‐Post‐Deposition Treatments on the Absorber Surface of High Efficiency Cu(In,Ga)Se₂ Solar Cells and Development of Analytical and Machine Learning Process Monitoring Methodologies Based on Combinatorial Analysis