Energy‐Level Modulation in Diboron‐Modified SnO<sub>2</sub> for High‐Efficiency Perovskite Solar Cells
Bibliographic record
Abstract
Energy‐level modulation between perovskite and carrier transport layers to obtain a promoted carrier extraction and reduced charge recombination is an effective way to achieve high‐efficiency perovskite solar cells. Here, diboron is used as an effective interfacial modifier between SnO2 and perovskite. By taking advantage of the higher Fermi level on the surface of SnO2 after diboron treatment, a power‐conversion efficiency of 22.04% in a solar cell device based on two‐step solution‐processed planar n‐i‐p structure is obtained. With the help of thorough characterizations, it is argued that the diboron‐treated SnO2 exhibits some Sn3+ species, which serve as electron donors with a more n‐type nature, promoting electron extraction and reducing carrier recombination in the electron transport layer (ETL)/perovskite interface. Further analysis speculates that the formation of surface diboron–oxygen Lewis pair induces a reducing state of diboron complexes, resulting in the spontaneous electron redistribution and the formation of Sn3+−O–• species. This provides an effective chemical approach to tune the energy alignment between the oxide ETL and absorber.
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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".