Bibliographic record
Abstract
Abstract This chapter presents a review of electrode materials used for the hydrogen evolution reactions and a comparison of their electrocatalytic activities. The introduction presents a brief definition of various thermodynamic water electrolysis potentials. In general, there are two ways to improve the performance of electrode materials: (1) use of electrode materials characterized by higher intrinsic activity i.e., higher exchange current density and (2) use of electrodes characterized by large real surface area. Both methods are used in practice. In this chapter various electrode materials are reviewed: smooth metals, alloys, intermetallic compounds and composites, Raney type materials (Raney Ni, Zn, etc.), oxides, carbides, sulfides, borides, phosphides, amorphous and nano‐crystalline materials etc., Among the most active materials are noble metals, doped Raney‐type alloys, IrO2/Ru2O, sulfides, borides, and Ni/Mo based alloys. Unfortunately, noble metals are very expensive and easily poisoned and their activity decreases with time. Although many materials may still be improved and optimized there is a need to study the detailed mechanism and kinetics of the HER on these materials and the relation between the geometric (surface roughness) and intrinsic electrocatalytic properties.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".