Recent Progress on Perovskite Surfaces and Interfaces in Optoelectronic Devices
Bibliographic record
Abstract
Surfaces and heterojunction interfaces, where defects and energy levels dictate charge-carrier dynamics in optoelectronic devices, are critical for unlocking the full potential of perovskite semiconductors. In this progress report, chemical structures of perovskite surfaces are discussed and basic physical rules for the band alignment are summarized at various perovskite interfaces. Common perovskite surfaces are typically decorated by various compositional and structural defects such as residual surface reactants, discrete nanoclusters, reactions by products, vacancies, interstitials, antisites, etc. Some of these surface species induce deep-level defect states in the forbidden band forming very harmful charge-carrier traps and affect negatively the interface band alignments for achieving optimal device performance. Herein, an overview of research progresses on surface and interface engineering is provided to minimize deep-level defect states. The reviewed subjects include selection of interface and substrate buffer layers for growing better crystals, materials and processing methods for surface passivation, the surface catalyst for microstructure transformations, organic semiconductors for charge extraction or injection, heterojunctions with wide bandgap perovskites or nanocrystals for mitigating defects, and electrode interlayer for preventing interdiffusion and reactions. These surface and interface engineering strategies are shown to be critical in boosting device performance for both solar cells and light-emitting diodes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".