Advanced Photoluminescence Imaging Method for Robust and Scalable Perovskite Quality Monitoring in Monolithic Tandem Solar Cells
Bibliographic record
Abstract
Photoluminescence‐based characterization techniques are widely employed in perovskite solar cell research, offering a noninvasive and contactless means of obtaining information about the implied open‐circuit voltage ( ) and, hence, the absorber quality. Driven by the idea of developing a robust yet quantitative in‐line imaging method for perovskite/Si tandem solar cells, we have extended an intensity‐dependent photoluminescence method initially reported for single‐junction solar cells. This method enables local quality assessment of the perovskite thin‐film absorbers processed over planar and textured silicon bottom solar cells with high spatial resolution. A single effective parameter k, also called optical ideality factor, is extracted, which accounts for the complex superposition of locally competing recombination processes. This work demonstrates that our method, called k‐imaging, is a robust and versatile characterization tool for perovskite/Si tandem solar cells that allows the assessment of the general thin‐film absorber quality as well as of specific defects for both scientific and industrial applications. Consequently, it accelerates perovskite research and paves the way for highly reproducible perovskite deposition processes toward commercialized perovskite/Si tandem solar cells.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".