High‐Efficiency Fabry‐Pérot‐Resonance‐Based Color‐Tunable Bifacial Perovskite Solar Cells for Building Integrated Photovoltaics
Bibliographic record
Abstract
Abstract Perovskite solar cells (PSCs) are promising for building‐integrated photovoltaics (BIPV) owing to their superior low‐light response and tunable bandgap. However, the implementation of perovskite‐based BIPV still faces critical challenges, primarily due to the inherent trade‐off between achieving color tunability via bandgap engineering and maintaining high power conversion efficiency (PCE), as well as ensuring sufficient operational stability. Here, rear‐side color vibrancy of bifacial perovskite solar cells (BPSCs) is enhanced by introducing a low‐loss ultrathin metal (LLUM) layer at the high‐loss SnO2/indium zinc oxide (IZO) interface under the guidance of Fabry‐Pérot (F‐P) resonance, achieving 60% coverage of sRGB color gamut for 1.52 eV BPSCs. Furthermore, the incorporation of 4‐methylphenethylammonium chloride (4M‐P) and an in‐situ substrate‐heated‐crystallization strategy enhances carrier diffusion lengths, allowing LLUM‐based BPSCs with a 900‐nm‐thick absorber to achieve a PCE of 23.7% under front illumination. Under albedo conditions of 0.1 and 0.2 sun irradiation intensity, the bifacial PCEs are elevated to 24.9% and 27.4%, respectively. The replacement of metal electrodes with IZO counterparts effectively suppresses metal ion diffusion, enabling BPSC devices to retain 87% of their initial efficiency after 1000 h of thermal aging at 85 °C. These results demonstrate the potential of LLUM‐based BPSCs for efficient, color‐tunable, and stable BIPV.
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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".