Identifying Reactive Trends in Glycerol Electro-Oxidation Using an Automated Screening Approach: 28 Ways to...

Area of Science:

• Electrochemistry
• Materials Science
• Catalysis

Background:

• Automated, rapid electrocatalyst discovery is crucial for exploring chemical spaces and ensuring reproducibility.
• Biomass valorization is a growing area of interest, requiring efficient catalytic processes.
• Glycerol electro-oxidation reaction (GEOR) in alkaline media serves as a model for studying electrocatalyst performance.

Purpose of the Study:

• To demonstrate an automated methodology for electrocatalyst discovery and optimization.
• To investigate the effects of electrodeposition parameters on gold catalysts for GEOR.
• To correlate catalyst properties with performance in flow electrolysis.

Main Methods:

• Developed an automated platform combining individually addressable electrode arrays and a Python API (HardPotato) for potentiostat control.
• Systematically investigated electrodeposition reduction potential (El) and pulse width (PW) for gold catalysts, evaluating 28 conditions.
• Utilized cyclic voltammetry (CV) and flow electrolysis to characterize catalyst activity, surface area, and product speciation.

Main Results:

• Identified a direct correlation between El and GEOR activity, with a 52% increase in peak current density and a -0.25 V shift in peak potential.
• Determined an optimal pulse width of approximately 1.0 s for maximum catalytic performance.
• Correlated performance enhancements with increased electrochemical surface area and preferential deposition of Au(110) and Au(111) sites.

Conclusions:

• The automated platform enables rapid and versatile electrocatalyst screening and optimization.
• Methodology successfully validated by scaling up electrodeposition and correlating intrinsic activity with product selectivity.
• Highlights the potential of automation in electrocatalyst discovery for industrial decarbonization, particularly in biomass valorization.