Impact of Indium Doping in Lead-Free (CH<sub>3</sub>NH<sub>3</sub>)<sub>3</sub>Bi<sub>2–<i>x</i></sub>In<sub><i>x</i></sub>I<sub>9</sub> Perovskite Photovoltaics for Indoor and Outdoor Light Harvesting
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Hybrid halide perovskites (HHPs) have revolutionized the field of solar cells due to their low cost, solution-processable synthesis, and exceptional device performance. Although lead (Pb)-based perovskites are currently the most efficient, their application in indoor photovoltaics and wearable electronics is limited by lead’s toxicity. This has intensified the search for Pb-free alternatives, particularly for use in portable electronic devices. In this study, we utilized a vapor-assisted solution process to systematically engineer the composition of bismuth-based perovskite-inspired materials (PIMs) through indium doping, forming homogeneous and pinhole-free (CH 3 NH 3 ) 3 Bi 2– x In x I 9 (Bi–In) films. These bimetallic Bi–In perovskites exhibit enhanced properties, including high recombination resistance, reduced low-frequency capacitance, lower defect density, and minimal microstrain. Electrochemical impedance spectroscopy (EIS) shows significantly reduced ion migration in Bi–In compositions compared with pure bismuth-based counterparts. The optimized Bi–In-based solar cells achieved a power conversion efficiency (PCE) of 2.5% under outdoor illumination and 5.9% under indoor lighting, showcasing their potential as promising lead-free alternatives for photovoltaic applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".