Stainless Steel-Supported Amorphous Nickel Phosphide/Nickel as an Electrocatalyst for Hydrogen Evolution Reaction
Bibliographic record
Abstract
Recently, nickel phosphides (Ni-P) in an amorphous state have emerged as potential catalysts with high intrinsic activity and excellent electrochemical stability for hydrogen evolution reactions (HER). However, it still lacks a good strategy to prepare amorphous Ni-P with rich surface defects or structural boundaries, and it is also hard to construct a porous Ni-P layer with favorable electron transport and gas-liquid transport. Herein, an integrated porous electrode consisting of amorphous Ni-P and a Ni interlayer was successfully constructed on a 316L stainless steel felt (denoted as Ni-P/Ni-316L). The results demonstrated that the pH of the plating solution significantly affected the grain size, pore size and distribution, and roughness of the cell-like particle surface of the amorphous Ni-P layer. The Ni-P/Ni-316L prepared at pH = 3 presented the richest surface defects or structural boundaries as well as porous structure. As expected, the as-developed Ni-P/Ni-316L demonstrated the best kinetics, with η10 of 73 mV and a Tafel slope of ca. 52 mV dec-1 for the HER among all the electrocatalysts prepared at various pH values. Furthermore, the Ni-P/Ni-316L exhibited comparable electrocatalytic HER performance and better durability than the commercial Pt (20 wt%)/C in a real water electrolysis cell, indicating that the non-precious metal-based Ni-P/Ni-316L is promising for large-scale processing and practical use.
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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) | 0.000 | 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".