Electrodeposited NiFeCoMoW High-Entropy Alloys with Nanoscale Amorphous Structure as Effective Hydrogen Evolution Electrocatalysts
Bibliographic record
Abstract
The growing demand for hydrogen and the effectiveness of alkaline anion exchange membrane (AEM) electrolyzers has led to an increased interest in finding lower cost alternatives to traditional noble metal electrocatalysts for the hydrogen evolution reaction (HER). High-entropy alloy (HEA) electrocatalysts have received significant attention due to their properties such as high configurational entropy and high lattice distortion which can help promote electrocatalytic reactions. In this work, an aqueously electrodeposited and structurally amorphous NiFeCoMoW HEA was synthesized and investigated for its HER performance. The importance of factors such as surface morphology, chemical composition, and microstructure on the electrochemical activity and stability was also explored. Increased electrochemical activity was observed in the HEA compared to electrodeposited binary alloys, owing to a larger number of active sites with differing electronic structures and adsorption energies. NiFeCoMoW HEAs electrodeposited at a pH of 5 exhibited the smallest Tafel slope of all the tested samples with an average Tafel slope of 83 mV/dec. Meanwhile, the lowest overpotential for 10 mA/cm 2 GA of 171 mV was observed in the samples prepared at pH 6, which possessed a higher roughness factor (RF). These results highlight the potential of using HEAs for electrocatalytic applications and demonstrate aqueous electrodeposition as a simple, inexpensive, and scalable synthesis method to produce effective HEA electrocatalysts.
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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) | 0.001 | 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".