The Mechanism of Hydrogen Evolution Reaction at the Buried Interface of Silica-Coated Electrocatalysts
Bibliographic record
Abstract
Semipermeable oxide coatings can protect electrocatalysts in harsh environments without reducing the catalytic performance (Labrador, Esposito et al. ACS Catal. 8, 2018, 1767–1778), making them attractive for direct seawater electrolysis. We recently showed that the buried SiO2/Pt interface of silica-coated platinum electrocatalysts is environment-dependent and changes with the pH value of the electrolyte and the electrode potential (Qu and Urban, ACS Appl. Mater. Interfaces 12, 2020, 52125–52135). Here, we discuss the impact of silica membrane coatings on the hydrogen evolution reaction (HER) mechanism at the interface with different transition-metal surfaces. Stable configurations of the buried SiO2/TM interface at HER conditions were determined using density-functional theory (DFT) calculations. Computed Pourbaix diagrams for different transition-metal substrates show the pH and potential dependence of reaction intermediates and the hydrogen coverage on the metal surface. Our results indicate that the HER mechanism at the buried SiO2/catalyst interfaces may involve the silica membrane. Hence, besides the protective quality of silica membranes, this also points to the possibility of designing synergistic membrane-coated electrocatalysts that surpass the bare surfaces of earth-abundant transition metals in terms of catalytic performance (stability, activity, and/or selectivity).
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".