Evaluating silver nickelate-reduced graphene oxide for water splitting: A machine learning approach
Articolo
Data di Pubblicazione:
2025
Abstract:
The oxygen evolution reaction (OER) is a key step in water electrolysis for sustainable hydrogen (H2) production,
yet its sluggish kinetics hinder overall efficiency. To address this, a novel hybrid catalyst, silver nickelate
embedded on reduced graphene oxide (NiAgO2-rGO), was synthesized via a hydrothermal method and thor-oughly characterized. XRD analysis confirmed a rhombohedral NiAgO2 phase with an average particle size of
~17.54 nm. Electrochemical measurements using cyclic voltammetry and linear sweep voltammetry on an
flourine-doped tin oxide (FTO) electrode showed promising OER activity, delivering a potential of 1.4959 V vs.
RHE at 10 mAcm
yet its sluggish kinetics hinder overall efficiency. To address this, a novel hybrid catalyst, silver nickelate
embedded on reduced graphene oxide (NiAgO2-rGO), was synthesized via a hydrothermal method and thor-oughly characterized. XRD analysis confirmed a rhombohedral NiAgO2 phase with an average particle size of
~17.54 nm. Electrochemical measurements using cyclic voltammetry and linear sweep voltammetry on an
flourine-doped tin oxide (FTO) electrode showed promising OER activity, delivering a potential of 1.4959 V vs.
RHE at 10 mAcm
Tipologia CRIS:
01.01 - Articolo in rivista
Keywords:
Hydrothermal synthesis, Electrochemical studies, Support vector machine, Decision tree regression, Linear regression, K-nearest neighbors
Elenco autori:
Iqbal, Sadaf; Aftab, Kiran; Ali Khan, Moonis; Khan, Mohammad Rizwan; Javed, Nighat; Jannat, Fakiha Tul; Ahmad, Naushad; Hussain, Muzammil
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