Comparative Analysis of Catalyst Supports in Green Hydrogen Production Processes

Thesis Abstract

The increasing demand for sustainable energy has intensified interest in green hydrogen production, yet the efficiency and long-term viability of electrochemical and thermochemical routes are profoundly influenced by the choice of catalyst supports, which govern dispersion, stability, and catalytic activity. This study addresses the problem of inconsistent performance attribution in catalyst systems for green hydrogen production, where support materials often confound true active-site effects and process scalability. The aim is to comparatively analyze how different catalyst supports affect the efficiency, stability, and environmental footprint of hydrogen production processes, with specific objectives to (i) quantify the impact of carbonaceous, oxide, and composite supports on catalytic activity and stability under fixed operational conditions; (ii) evaluate mass- and heat-transfer limitations introduced by support properties using electrochemical impedance spectroscopy (EIS) and in situ X-ray diffraction (XRD); (iii) assess lifecycle environmental implications of supports through a cradle-to-gate assessment; (iv) model performance dependencies using regression and multivariate analysis to identify key predictor variables; and (v) propose optimized support-design guidelines for scalable green hydrogen technologies. A mixed-methods approach is employed. The research adopts a comparative cross-sectional design, combining experimental synthesis and testing with analytical modeling. The primary population comprises commercially relevant catalyst systems for water electrolysis and photocatalytic water splitting, with sample frames including 12 distinct supports activated carbon, graphene oxide, carbon nanotubes, TiO2, Al2O3, SiO2, CeO2, ZrO2, TiN, AlN, SiC, and a graphene–oxide–oxide hybrid. For each support, a standardized catalyst is prepared by depositing a fixed wt% of a state-of-the-art Ni–Fe or Pt–Ru active phase, ensuring consistent metal loading across samples. The experimental sample size comprises 144 catalyst–support combinations (12 supports × 12 active-phase configurations) tested under identical electrolysis conditions (80 °C, 1.8–2.2 V cell voltage, 1.5 M KOH electrolyte) and simulated solar irradiation for photocatalytic runs. Data collection employs (i) electrochemical performance metrics (current density, overpotential, Faradaic efficiency) via chronoamperometry and rotating disk electrode (RDE) measurements; (ii) structural and surface characterization through TEM, SEM, XRD, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and BET surface area analysis; (iii) in situ/operando techniques including EIS to extract charge-transfer and mass-transport resistances; (iv) stability assays over 100-hour continuous operation; (v) life-cycle assessment data gathered from supplier specifications and process simulations, focusing on energy consumption and greenhouse gas emissions associated with each support material; (vi) environmental and safety profiles through hazard screening. Analytical methods include descriptive statistics and inferential analyses analysis of variance (ANOVA) to compare performance across support classes, multivariate linear and non-linear regression to model performance predictors, principal component analysis (PCA) for dimensionality reduction, and response surface methodology (RSM) to identify optimal support properties. Theoretical grounding integrates the Sabatier principle for activity–selectivity optimization, the electron-conduction theory for charge-transfer efficiency, and the dispersion-stability framework for active-site utilization. The study also adopts the theory of mass- and heat-transfer limitations in porous catalysts to interpret EIS and operando data, with regression models validated via cross-validation and bootstrapping to ensure robustness. Key expected findings include (i) distinctly superior activity and stability for specific supports (e.g., graphene-based and mixed oxides) correlating with higher metal dispersion and improved electron transport; (ii) identifiable trade-offs between conductivity, surface area, and corrosion resistance influencing long-term performance; (iii) quantifiable life-cycle environmental advantages for certain low-emission supports, offsetting minor reductions in activity; and (iv) a predictive model that links support physico-chemical properties to hydrogen production efficiency, enabling a design framework for optimal catalyst-support pairs. The study contributes to knowledge by systematically disentangling support effects from active-phase contributions in green hydrogen contexts, offering a data-driven framework for selecting and engineering supports with minimal environmental burden and maximal durability. Recommendations include adopting composite supports that balance conductivity and stability, prioritizing scalable fabrication routes, and integrating the predictive model into catalyst development pipelines to accelerate the deployment of efficient, sustainable green hydrogen technologies.