Reducing the variability in performance of organic solar cells containing vacuum deposited MoO<sub>x</sub>extraction layers
Bibliographic record
Abstract
Reproducibility in efficiency and lifetime of organic solar cells (OSCs) remains a major concern, especially with the development of more complex and modern multi-layer device architectures. In this work, OSCs are studied for their efficiency and photo-stability as a function of the quality of their thermally evaporated MoOx hole extraction layer (HEL). To this end, the characteristics of the MoOx film are demonstrated to change with repeat evaporation runs from the same source material. These variations have strong effects on polymer OSCs (p-OSCs), with an effective halving of the power conversion efficiency after only three MoO3 evaporation runs. In contrast, vacuum deposited small molecule OSCs (sm-OSCs) appear to be unaffected by the history of the MoO3 source material. sm- OSCs are instead shown to be prone to large changes in efficiency as a function of the delay time in between deposition of the MoOx HEL and subsequent photo-active materials. Increased delay time between these deposition steps is also demonstrated to decrease the sm-OSC photo-stability. The results thus emphasize subtleties in materials deposition processes that can play a significant role in obtaining reproducible and scientifically relevant data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".