Characterization of interfaces: Lessons from the past for the future of perovskite solar cells
Bibliographic record
Abstract
Abstract A photovoltaic technology historically goes through two major steps to evolve into a mature technology. The first step involves advances in materials and is usually accompanied by the rapid improvement of power conversion efficiency. The second step focuses on interfaces and is usually accompanied by significant stability improvement. As an emerging generation of photovoltaic technology, perovskite solar cells are transitioning to the second step of their development when a significant focus shifts toward interface studies and engineering. While various interface engineering strategies have been developed, interfacial characterization is crucial to show the effectiveness of interfacial modification. Here, we review the characterization techniques that have been utilized in studying interface properties in perovskite solar cells. We first summarize the main roles of interfaces in perovskite solar cells, and then we discuss some typical characterization methodologies for morphological, optical, and electrical studies of interfaces. Successful experiences and existing problems are analyzed when discussing some commonly used methods. We then analyze the challenges and provide an outlook for further development of interfacial characterizations. This review aims to evoke strengthened research devotion on novel and persuasive interfacial engineering.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".