3 D NiO Nanowall Hole‐Transporting Layer for the Passivation of Interfacial Contact in Inverted Perovskite Solar Cells
Bibliographic record
Abstract
Nickel oxide (NiO) materials with excellent stability and favorable energy bands are desirable candidates for hole-selective contact (HSC) of inverted perovskite solar cell (PSC). However, studies that focus on addressing interfacial issues, which are induced by the poor NiO/perovskite contact or other defects, are scarce. In this study, a facile one-step hydrothermal strategy is demonstrated for the development of a 3 D NiO nanowall (NW) film as a promising HSC. The new NiO NWs HSC exhibits a robust and homogenous mesoporous network structure, which improved the NiO/perovskite interface contact, passivated the interfacial defect and improved the quality of the perovskite film. The optimized interface features enabled a power conversion efficiency (PCE) approaching 18 %. A diethanolamine (DEA) interlayer was introduced to further passivate the intrinsic defect of the NiO surface, resulting in better charge transfer with suppressed recombination loss. As a result, the champion PCE of the NiO NWs/DEA-based device was increased to 19.16 % with a high open-circuit voltage (≈1.11 V) and fill factor (>80 %), which is prominent in methylammonium lead iodide-based inverted PSCs. Furthermore, the device exhibited better stability and lower hysteresis behavior than a conventional solution-based NiO nanocrystal device.
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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) | 0.000 | 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".