Machine Learning-enhanced Copper (I) Thiocyanate-based Perovskite-silicon Tandem Solar Cells: Optimization Strategies for Enhanced Efficiency and Stability DOI Open Access

John Sunday Uzochukwu,

Okey-Onyesolu Chinenye Faith,

Ezechukwu Chioma Mary-Jane

et al.

Archives of Case Reports, Journal Year: 2025, Volume and Issue: 9(3), P. 081 - 131

Published: March 26, 2025

This paper investigates the role of machine learning (ML) techniques in advancing CuSCN-based perovskite tandem solar cells (PTSCs), addressing critical challenges such as power conversion efficiency, scalability, and long-term operational stability. CuSCN is emphasized a promising hole transport layer due to its affordability, thermal stability, compatibility with scalable manufacturing techniques. Leveraging ML-driven frameworks , study optimizes key parameters, enhances uniformity, reduces defect density, refines interface engineering, achieving significant improvements compared conventional methods . Results demonstrate that ML-based optimization facilitates efficiencies exceeding 29% under controlled conditions while offering precise predictions performance degradation mechanisms. outcome establishes benchmark for integrating into PTSCs maintaining environmental economic sustainability. Furthermore, underscores ML’s capability tailoring complex device architectures minimizing experimental efforts required achieve optimal configurations. The novelty this work lies proposing hybrid methodologies integrate ML fabrication techniques, computational cost limitations hinder widespread application. Additionally, contributes expanding open-access datasets lightweight models, access tools resource-limited environments. research bridges gaps previous studies by presenting comprehensive framework material providing solutions expedite PTSC commercialization. These findings position transformative, sustainable alternative renewable energy technologies meeting global demands.

Language: Английский

Exploring KGeCl3 material for perovskite solar cell absorber layer through different machine learning models DOI
Nikhil Shrivastav,

Mir Aamir Hamid,

Jaya Madan

et al.

Solar Energy, Journal Year: 2024, Volume and Issue: 278, P. 112784 - 112784

Published: July 18, 2024

Language: Английский

Citations

8
Exploring various Integration Methods of carbon quantum dots in CsPbCl3 perovskite solar cells for enhanced power conversion efficiency DOI Creative Commons
Eman F. Sawires,

Zahraa Ismail,

Mona Samir

et al.

Journal of Materials Science Materials in Electronics, Journal Year: 2024, Volume and Issue: 35(11)

Published: April 1, 2024

Abstract In this study, we explore the integration of carbon quantum dots (QDs) in cesium lead halide perovskite solar cells (PSCs) across electron transport layer (ETL), hole (HTL), and absorber to enhance power conversion efficiency (PCE). We conduct a comprehensive investigation from thin film analysis complete device characterization, encompassing eight different topologies. Our results reveal that QDs various layers significantly impacts performance PSCs. Notably, adding HTL ETL improves charge reduces recombination, enhancing efficiency. Furthermore, introducing leads modifications energy landscape, reducing trapping stability. observe trade-off between short-circuit current overall PCE, with QD strategies yielding distinct outcomes. Additionally, incorporating hysteresis, attributed mitigated ion migration charge-trapping effects. Overall, addition these demonstrates improved transport, reduced enhanced stability, ultimately contributing cells, reaching 22.5%. This study paves way for future investigations into potential PSC technology their impact on forecasting operational

Language: Английский

Citations

4
Designing and optimization of a highly efficient and new lead-free Cs2RbGaI6 based double perovskite solar cell through SCAPS-1D and machine learning DOI
Vishal Deswal,

Sarita Baghel

Inorganic Chemistry Communications, Journal Year: 2025, Volume and Issue: unknown, P. 114316 - 114316

Published: March 1, 2025

Language: Английский

Citations

0
Is the end of AI in photovoltaic power? Evidence from China DOI
Haoran Zhang,

Xiaohong Yu,

Zixuan Gao

et al.

Energy Economics, Journal Year: 2025, Volume and Issue: unknown, P. 108423 - 108423

Published: March 1, 2025

Language: Английский

Citations

0
Machine Learning-enhanced Copper (I) Thiocyanate-based Perovskite-silicon Tandem Solar Cells: Optimization Strategies for Enhanced Efficiency and Stability DOI Open Access

John Sunday Uzochukwu,

Okey-Onyesolu Chinenye Faith,

Ezechukwu Chioma Mary-Jane

et al.

Archives of Case Reports, Journal Year: 2025, Volume and Issue: 9(3), P. 081 - 131

Published: March 26, 2025

This paper investigates the role of machine learning (ML) techniques in advancing CuSCN-based perovskite tandem solar cells (PTSCs), addressing critical challenges such as power conversion efficiency, scalability, and long-term operational stability. CuSCN is emphasized a promising hole transport layer due to its affordability, thermal stability, compatibility with scalable manufacturing techniques. Leveraging ML-driven frameworks , study optimizes key parameters, enhances uniformity, reduces defect density, refines interface engineering, achieving significant improvements compared conventional methods . Results demonstrate that ML-based optimization facilitates efficiencies exceeding 29% under controlled conditions while offering precise predictions performance degradation mechanisms. outcome establishes benchmark for integrating into PTSCs maintaining environmental economic sustainability. Furthermore, underscores ML’s capability tailoring complex device architectures minimizing experimental efforts required achieve optimal configurations. The novelty this work lies proposing hybrid methodologies integrate ML fabrication techniques, computational cost limitations hinder widespread application. Additionally, contributes expanding open-access datasets lightweight models, access tools resource-limited environments. research bridges gaps previous studies by presenting comprehensive framework material providing solutions expedite PTSC commercialization. These findings position transformative, sustainable alternative renewable energy technologies meeting global demands.

Language: Английский

Citations

0