Bibliographic record
Abstract
Abstract Electrocatalysis offers an alternative solution for the energy crisis because it lowers the activation energy of reaction to produce economic fuels more accessible. Non-noble electrocatalysts have shown their capabilities to practical catalytic applications as compared to noble ones, whose scarcity and high price limit the development. However, the puzzling catalytic processes in non-noble electrocatalysts hinder their advancement. In-situ techniques allow us to unveil the mystery of electrocatalysis and boost the catalytic performances. Recently, various in-situ X-ray techniques have been rapidly developed, so that the whole picture of electrocatalysis becomes clear and explicit. In this review, the in-situ X-ray techniques exploring the structural evolution and chemical-state variation during electrocatalysis are summarized for mainly oxygen evolution reaction (OER), hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), and carbon dioxide reduction reaction (CO2RR). These approaches include X-ray Absorption Spectroscopy (XAS), X-ray diffraction (XRD), and X-ray Photoelectron Spectroscopy (XPS). The information seized from these in-situ X-ray techniques can effectively decipher the electrocatalysis and thus provide promising strategies for advancing the electrocatalysts. It is expected that this review could be conducive to understanding these in-situ X-ray approaches and, accordingly, the catalytic mechanism to better the electrocatalysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".