Low-Cost Nanostructured Cathode Electrocatalysts and Supports for Water Electrolysis in Acidic and Alkaline Media
Bibliographic record
Abstract
Hydrogen production via water electrolysis in an acidic medium relies heavily on noble-metal-based electrocatalysts like platinum and iridium, which are highly efficient but expensive and scarce. Further, alkaline water electrolysis in anion-exchange-membrane water electrolysers necessities electrodes of higher specific electrochemical activity. To transition to a hydrogen economy, where hydrogen serves as a clean energy carrier, replacing fossil fuels in industries like transportation, ammonia production, and steel manufacturing, cost reductions in hydrogen production are essential. By developing cost-effective and efficient non-precious metal catalysts and better catalyst supports, large-scale green hydrogen production via water electrolysis can become more viable, reducing dependence on fossil fuels and lowering carbon emissions across multiple sectors. This aligns with global efforts to decarbonize heavy industries and promote renewable energy integration. Herein, we present our recent works (i) on the development of cost-effective cathode electrocatalysts for hydrogen evolution in an acidic medium, based on Ni-W nanocomposite materials, and (ii) on the development of carbon-based support for cathodes used in an alkaline water electrolysers based on hollow multiactivity carbon spheres (HMCS). Ni-W and Ni-W-Ru nanocomposite electrocatalysts were synthesized via a sequential, optimized three-step process to enhance hydrogen evolution reaction (HER) performance in acidic media. Initially, Ni–W nanocomposite materials were prepared using a single-step solution combustion synthesis (SCS) method. Optimization of the fuel-to-oxidant ratio established φ = 9 as ideal. The Ni 0.9 W 0.1 alloy phase with minimal oxide impurities, tested in 0.5 M H 2 SO 4 demonstrated the superior HER performance, correlating directly with higher proportions of metallic Ni and W states (Figure 1a, green). However, residual carbon impurities limited catalytic efficiency. To mitigate this limitation, an additional annealing step under a 10 vol.% H 2 /Ar reductive atmosphere was introduced. Among the various compositions evaluated, Ni 0.7 W 0.3 exhibited optimal HER performance, achieving a Tafel slope of 100 mV dec −1 , exchange current density of 677 μA cm − 2 , and an overpotential (η 10 ) of −143 mV at 10 mA cm − 2 (Figure 1a, blue). The enhanced catalytic activity was attributed to a synergistic interaction between metallic Ni–W, metallic tungsten, and oxygen-deficient tungsten oxide phases. To further enhance electrocatalytic efficiency toward the level of noble-metal-based catalysts, Ru was incorporated with Ni 0.7 W 0.3 , forming (Ni 0.7 W 0.3 ) 1−x Ru x electrocatalysts with low Ru loadings (x ≤ 0.10). The analysis revealed a heterogeneous structure consisting of an interconnected network of both spherical and irregularly shaped nanoparticles, with uniform elemental distribution of Ni, W, and Ru. Structural analyses confirmed the presence of metallic Ru and Ni 0.9 W 0.1 phases along with oxygen-deficient WO 2 . The (Ni 0.7 W 0.3 ) 0.96 Ru 0.04 catalyst displayed comparable HER activity ( η 10 = -109.3 mV) to the commercial 5 wt.% Ru/C (η 10 = -109.8 mV), despite containing only half of the Ru content (2.48 wt.%). Moreover, (Ni 0.7 W 0.3 ) 0.95 Ru 0.05 , containing 3.77 wt.% Ru, outperformed the commercial 5 wt.% Ru/C benchmark with an even lower η 10 of -99.2 mV (Figure 1a, red). Furthermore, all synthesized (Ni 0.7 W 0.3 ) 1−x Ru x electrocatalysts exhibited superior exchange current densities and mass activities compared to the commercial 5 wt.% Ru/C catalyst. In fact, the (Ni 0.7 W 0.3 ) 0.98 Ru 0.02 catalyst exhibited a remarkable mass activity of 2.07 A mg Ru −1 at an overpotential of −125 mV, nearly twice that of the commercial 5 wt.% Ru/C catalyst (1.04 A mg Ru −1 ). The remarkable catalytic activity was attributed to synergistic electronic interactions between Ru, Ni, and W phases, enhancing HER kinetics. Additionally, the high specific surface area and porous three-dimensional nanostructure maximized active site exposure. This research demonstrates that engineering ternary metallic interactions and structural characteristics in Ni-W-Ru nanocomposites effectively enhances HER performance, offering a promising and cost-effective alternative to conventional noble-metal catalysts in acidic media. In the context of alkaline water electrolysis, we successfully synthesized HMCS as a support for nickel nanoparticles in cathodes (Figure 1b,c). The HMCS exhibited a high specific surface area (up to 284 ± 10 m²g -1 ), facilitating the formation of a Ni/HMCS electrocatalyst with significantly enhanced HER activity compared to pure Ni. This result highlights HMCS as a promising metal-catalyst support. Notably, the non-activated Ni/HMCS outperformed the activated Ni/HMCS in HER performance. Figure 1
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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".