Dependence of interface energetics and kinetics on catalyst loading in a photoelectrochemical system
Bibliographic record
Abstract
Solar hydrogen production by the photoelectrochemical method promises a means to store solar energy. While it is generally understood that the process is highly sensitive to the nature of the interface between the semiconductor and the electrolyte, a detailed understanding of this interface is still missing. For instance, few prior studies have established a clear relationship between the interface energetics and the catalyst loading amount. Here we aim to study this relationship on a prototypical Si-based photoelectrochemical system. Two types of interfaces were examined, one with GaN nanowires as a protection layer and one without. It was found that when GaN was present, higher Pt loading (> 0.1 μg/cm 2 ) led to not only better water reduction (and, hence, hydrogen evolution) kinetics but also more favorable interface energetics for greater photovoltages. In the absence of the protection layer, by stark contrast, increased Pt loading exhibited no measurable influence on the interface energetics, and the main difference was observed only in the hydrogen evolution kinetics. The study sheds new light on the importance of interface engineering for further improvement of photoelectrochemical systems, especially concerning the role of catalysts and protection layers.
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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 | 0.001 | 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".