In-situ perovskite solar cell carrier dynamics characterized by ultrafast photovoltaic spectroscopy (UPVS)
Bibliographic record
Abstract
Understanding carrier dynamics in operational solar cells, i.e., under working conditions, remains a significant challenge. Establishing a direct correlation between carrier dynamics and solar cell performance requires an innovative approach that integrates the following key features: 1) in operando and in situ diagnostics, rather than conventional ex situ methods such as structural characterization by SEM, TEM, or XRD; 2) a focus on electrical rather than optical properties; 3) comprehensive carrier dynamics measurement with ultrafast time resolution, rather than limited and slower temporal ranges; and 4) a high-throughput screening capability. In this report, we employ a novel ultrafast photovoltaic spectroscopy technique to investigate the ultrafast carrier dynamics in >20% CsFAMAPbIBr perovskite solar cells by directly capturing the photocurrent with a time resolution of less than 40 picoseconds. This approach enables the characterization of carrier mobility and lifetime within in situ solar cells under operational conditions. Our findings reveal that the carrier transport mechanism is dominated by traps, a conclusion supported by the dependence of transport behavior on both electric field and temperature. This study represents a significant step toward advancing our understanding of carrier dynamics in operational solar cells, paving the way for more effective diagnostics and improved device performance.
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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 | 0.000 | 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".