ArticleScientific reports2026
Investigating the optoelectronic properties and photovoltaic performance of Na
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Machine learning prediction of dual absorber lead-free perovskite solar cells for boosting PCE.Scientific reports · 2026Article
- Deep generative modeling for AI-guided inverse design of perovskite photovoltaic devices.Frontiers in artificial intelligence · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study offers a thorough analysis of Density Functional Theory (DFT) and SCAPS-1D to assess the optoelectronic performance of cubic Na2AuGaBr6 double perovskites, highlighting its potential for advanced optoelectronic and photovoltaic applications. Device simulations were methodically conducted utilizing Na2AuGaBr6 as the active absorber material, in conjunction with various electron transport layers (ETLs) including TiO2, ZnO, WS2, C60, IGZO, and In2S3, as well as hole transport layers (HTLs) such as CuI, CFTS, NiO, CuSbS2, V2O5, Sb2S3, MoTe2, and CuO, to ascertain the optimal device configuration. Comprehensive parametric optimization was performed by analyzing the effects of different left (Co, Ni, Au, Pt, Pd, and Se) and right (Ca, Ba, Mg, Ag, Al, and Cr) metal contacts, band alignment, layer thicknesses, interface and bulk defect concentrations, as well as temperature on the overall photovoltaic performance. Of the 48 simulated device structures, the Al/FTO/WS2/Na2AuGaBr6/V2O5/Ni configuration demonstrated superior performance, achieving a power conversion efficiency (PCE) of 28.96%. Additionally, advanced machine learning (ML) and deep learning (DL) models were utilized to forecast and corroborate device performance trends. Of the eleven methods evaluated, the Gradient Boosting model exhibited remarkable predictive accuracy, attaining a R2 of 0.954 and a negligible mean absolute percentage error (MAPE) of 0.0218. These findings affirm the significant promise of Na2AuGaBr6-based perovskites for lead-free, high-efficiency solar systems and establish ML and DL-assisted modeling as an efficient method for performance improvement and material design in photovoltaic research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.