Engineering of the Electron Transport Layer/Perovskite Interface in Solar Cells Designed on TiO<sub>2</sub> Rutile Nanorods
Bibliographic record
Abstract
Abstract The engineering of the electron transport layer (ETL)/light absorber interface is explored in perovskite solar cells. Single‐crystalline TiO2 nanorod (NR) arrays are used as ETL and methylammonium lead iodide (MAPI) as light absorber. A dual ETL surface modification is investigated, namely by a TiCl4 treatment combined with a subsequent PC61BM monolayer deposition, and the effects on the device photovoltaic performance were evaluated with respect to single modifications. Under optimized conditions, for the combined treatment synergistic effects are observed that lead to remarkable enhancements in cell efficiency, from 14.2% to 19.5%, and to suppression of hysteresis. The devices show JSC, VOC, and fill factor as high as 23.2 mA cm−2, 1.1 V, and 77%, respectively. These results are ascribed to a more efficient charge transfer across the ETL/perovskite interface, which originates from the passivation of defects and trap states at the ETL surface. To the best of our knowledge, this is the highest cell performance ever reported for TiO2 NR‐based solar cells fabricated with conventional MAPI light absorber. Perspective wise, this ETL surface functionalization approach combined with more recently developed and better performing light absorbers, such as mixed cation/anion hybrid perovskite materials, is expected to provide further performance enhancements.
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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".