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Enregistrement W4416601054 · doi:10.1149/ma2025-02391833mtgabs

Low-Cost Nanostructured Cathode Electrocatalysts and Supports for Water Electrolysis in Acidic and Alkaline Media

2025· article· W4416601054 sur OpenAlexaff
Kang‐Hoon Choi, Ahmed Abdulla Ahmed Almaazmi, Sasha Omanovic

Notice bibliographique

RevueECS Meeting Abstracts · 2025
Typearticle
Langue
DomaineEnergy
ThématiqueElectrocatalysts for Energy Conversion
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésHydrogen productionElectrolysis of waterElectrolysisHydrogenCatalysisElectrochemistryAlkaline water electrolysisWater splittingNanocompositeCarbon fibers

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,002

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,227
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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