Ni<sub><i>x</i></sub>W<sub>1–<i>x</i></sub> Nanoparticles as Electrocatalysts for Hydrogen Evolution in Acidic Medium
Bibliographic record
Abstract
This study investigates the electrocatalytic performance of nanostructured Ni–W electrocatalysts for the hydrogen evolution reaction (HER) in an acidic medium (0.5 M H 2 SO 4 ). Electrocatalysts were synthesized using a solution combustion synthesis method, followed by metal reduction under a 10 vol % H 2 /Ar atmosphere. By systematically varying the Ni-to-W atomic ratio in Ni x W 1– x electrocatalysts ( x = 0.1, 0.3, 0.5, 0.7, and 0.9), we identified the optimal composition for maximum HER activity. Comprehensive structural and electrochemical analyses, including X-ray diffraction, X-ray photoelectron spectroscopy, ultraviolet photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, linear sweep voltammetry, and electrochemical impedance spectroscopy, were conducted to investigate phase composition, surface chemistry, work function (Φ), morphology, and HER catalytic activity. Among the compositions evaluated, Ni 0.7 W 0.3 demonstrated superior HER performance, characterized by a Tafel slope of 100 ± 2 mV dec –1, an exchange current density ( j 0 ) of 677 ± 106 μA cm –2, and an overpotential (η 10 ) of −143 ± 4 mV at a cathodic current density of −10 mA cm –2 . When compared to the previously reported Ni–W electrocatalysts for acidic HER, Ni 0.7 W 0.3 stands out as one of the most effective electrocatalysts. This remarkable performance results from an optimized phase composition, enabling synergistic interactions among the Ni–W alloy phase, metallic tungsten, and oxygen-deficient tungsten oxide species. These structural features create an enhanced electronic environment, facilitating charge transport and promoting efficient water dissociation kinetics. Moreover, the high work function of Ni 0.7 W 0.3 contributes to improved electron transfer kinetics, effectively lowering the energy barriers of the HER process. Its well-defined nanostructure, featuring multiple active edges and facets, further increases the density of accessible active sites, enhancing catalytic activity. Collectively, these findings highlight the potential of Ni–W electrocatalysts as cost-effective and highly efficient alternatives to noble metal-based electrocatalysts for acidic water electrolysis.
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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".