Organic Photochemistry-Assisted Nanoparticle Segregation on Perovskites
Bibliographic record
Abstract
The segregation (or exsolution) of nanoparticles (NPs) on the surface of perovskite oxide parents has emerged as an advanced technology to design functional materials for renewable energy. However, this process relies heavily upon lengthy reduction (800–1,200 K) in hydrogen-rich environments to facilitate the electron transfer from hydrogen to oxides, making this process costly. Here, we show that, in addition to thermal driving forces, photo-illumination can drive electron donation and facilitate the electron harvesting on perovskite directly. This results in segregation of NPs at room temperature with the assistance of trialkyl amine as a hole acceptor. A proton-coupled electron transfer catalytic cycle is suggested to explain this unusual electron transfer pathway, which is redox neutral and an intrinsically closed cycle. The practicality of this process is demonstrated by the improved performance in a trial electrocatalytic oxygen evolution reaction. This work suggests a promising design principle for perovskite functionalization.
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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".