Understanding and Mitigating the Degradation of Perovskite Solar Cells Based on a Nickel Oxide Hole Transport Material during Damp Heat Testing
Bibliographic record
Abstract
The development of stable materials, processable on a large area, is a prerequisite for perovskite industrialization. Beyond the perovskite absorber itself, this should also guide the development of all other layers in the solar cell. In this regard, the use of NiO x as a hole transport material (HTM) offers several advantages, as it can be deposited with high throughput on large areas and on flat or textured surfaces via sputtering, a well-established industrial method. However, NiO x may trigger the degradation of perovskite solar cells (PSCs) when exposed to environmental stressors. Already after 100 h of damp heat stressing, a strong fill factor (FF) loss appears in conjunction with a characteristic S-shaped J–V curve. By performing a wide range of analysis on cells and materials, completed by device simulation, the cause of the degradation is pinpointed and mitigation strategies are proposed. When NiO x is heated in an air-tight environment, its free charge carrier density drops, resulting in a band misalignment at the NiO x /perovskite interface and in the formation of a barrier impeding hole extraction. Adding an organic layer between the NiO x and the perovskite enables higher performances but not long-term thermal stability, for which reducing the NiO x thickness is necessary.
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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".