Data Analysis for Emerging Materials in Hybrid Perovskite Photovoltaics
Hasanov, Ruslan (2025-07-04)
Data Analysis for Emerging Materials in Hybrid Perovskite Photovoltaics
(04.07.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025082584356
https://urn.fi/URN:NBN:fi-fe2025082584356
Tiivistelmä
Perovskite solar cells have garnered significant attention in the field of photovoltaics due to their low production costs and high efficiency. Device instability and the transition to multi-pixel device configurations underscore the critical need for standardised high-throughput characterisation and robust data analysis. Currently, the lack of automated workflows for analysing large-scale and repetitive current-voltage measurements leads to inconsistencies and inefficiencies in data processing, hindering research progress.
This thesis addresses the gap by presenting an open-source Python-based analytical software tool that automates the analysis and reporting of current-voltage data from 2- and 8-pixel perovskite solar cell devices, allowing for standardised performance evaluation. The developed tool features input handling, automatic extraction of essential performance parameters, such as fill factor and power conversion efficiency, as well as the generation of plots and comprehensive PDF reports.
Applied to real experimental datasets, the software demonstrates its ability to process data from devices with both 2-pixel and 8-pixel configurations. The proposed solution is built with a modular design, which allows for the customisation and integration of functionalities to meet the evolving research demands. The tool provides reproducible, high-quality reports, significantly reducing manual workload. The structure of generated reports consists of the current density-voltage graphs, performance tables, and box plots. The produced reports are aimed at providing researchers with immediate insights into device performance and variability, while offering better comparison across experimental conditions.
The tool enhances research efficiency, ensures consistent results, and supports scalable workflows by streamlining data analysis. These features make the tool a highly valuable asset for advancing perovskite solar cell research and development.
AI-based language assistance tools (e.g., ChatGPT) were utilized exclusively to improve grammar, structure, and clarity during the writing of this thesis. The technical content, analysis, and interpretations were developed independently by the author.
This thesis addresses the gap by presenting an open-source Python-based analytical software tool that automates the analysis and reporting of current-voltage data from 2- and 8-pixel perovskite solar cell devices, allowing for standardised performance evaluation. The developed tool features input handling, automatic extraction of essential performance parameters, such as fill factor and power conversion efficiency, as well as the generation of plots and comprehensive PDF reports.
Applied to real experimental datasets, the software demonstrates its ability to process data from devices with both 2-pixel and 8-pixel configurations. The proposed solution is built with a modular design, which allows for the customisation and integration of functionalities to meet the evolving research demands. The tool provides reproducible, high-quality reports, significantly reducing manual workload. The structure of generated reports consists of the current density-voltage graphs, performance tables, and box plots. The produced reports are aimed at providing researchers with immediate insights into device performance and variability, while offering better comparison across experimental conditions.
The tool enhances research efficiency, ensures consistent results, and supports scalable workflows by streamlining data analysis. These features make the tool a highly valuable asset for advancing perovskite solar cell research and development.
AI-based language assistance tools (e.g., ChatGPT) were utilized exclusively to improve grammar, structure, and clarity during the writing of this thesis. The technical content, analysis, and interpretations were developed independently by the author.