[Paper Review] A Critical Evaluation of a Self-Driving Laboratory for the Optimization of Electrodeposited Earth-Abundant Mixed-Metal Oxide Catalysts for the Oxygen Evolution Reaction (OER)

Erfan Fatehi, Manish Thadani+2 more|arXiv (Cornell University)|May 21, 2023

Electrocatalysts for Energy Conversion10 citations

TL;DR

The paper presents a self-driving laboratory framework to optimize electrodeposited amorphous earth-abundant mixed-metal oxide catalysts for acidic OER, emphasizing experimental variability and automation.

ABSTRACT

This work highlights the potential of earth-abundant mixed-metal oxide catalysts for the acid-based oxygen evolution reaction. These catalysts offer numerous combinations of metal-centre compositions, which can enhance catalytic activity and stability compared to precious-metal-based catalysts commonly used today. Despite substantial research in this field, there is a need for new methods and approaches to accelerate the exploration of these materials. In this study, we present a comprehensive approach to designing, developing, and implementing a self-driving laboratory to optimize the electrodeposition synthesis of amorphous mixed-metal oxide catalysts for the acidic oxygen evolution reaction. We particularly emphasize the development of methodologies to address experimental variability. We investigate crucial parameters and considerations when transitioning from manual bench-top synthesis and evaluation to automation and machine learning guided optimization. We address both experimental and optimization algorithm considerations in the presence of experimental variability. To illustrate our approach, we demonstrate the optimization of CoFeMnPbOx electrodeposited catalyst materials through multiple campaigns. Our results highlight considerations for optimizing overpotential and stability based on the outcomes of our experiments.

Motivation & Objective

• Motivate the exploration of earth-abundant mixed-metal oxide catalysts as alternatives to precious metals for acidic OER.
• Develop and evaluate a self-driving laboratory workflow for autonomous fabrication and testing of electrodeposited catalysts.
• Address experimental variability in automated synthesis and evaluation to enable reliable optimization.
• Investigate key parameters and considerations when transitioning from manual bench-top work to automated, ML-guided optimization.
• Illustrate the approach with optimization campaigns on CoFeMnPbOx materials to study activity and stability.

Proposed method

• Design and implementation of a self-driving laboratory framework for autonomous synthesis and testing of electrodeposited amorphous mixed-metal oxide catalysts.
• Automation-enabled optimization guided by machine learning and iterative experimentation.
• Strategies to mitigate and account for experimental variability in both synthesis and evaluation.
• Analysis of optimization parameters and workflow considerations during the bench-to-automation transition.
• Execution of multiple optimization campaigns on CoFeMnPbOx catalyst systems to demonstrate autonomous exploration of composition-space and performance metrics.

Experimental results

Research questions

• RQ1 How can a self-driving laboratory accelerate the discovery and optimization of earth-abundant mixed-metal oxide catalysts for the acidic OER?
• RQ2 What are the dominant sources of experimental variability in automated electrodeposition and evaluation, and how can they be mitigated?
• RQ3 What parameters and workflow design are critical when moving from manual bench-top methods to automated ML-guided optimization?
• RQ4 How does autonomous optimization balance activity (overpotential) and stability for CoFeMnPbOx systems in the OER?

Key findings

• Demonstrates autonomous optimization campaigns for electrodeposited CoFeMnPbOx catalysts.
• Highlights methodological and practical considerations for optimizing overpotential and stability in the presence of experimental variability.
• Shows how automation and ML-guided optimization can be applied to earth-abundant mixed-metal oxide systems for OER.
• Identifies challenges and strategies in transitioning from manual synthesis to a self-driving laboratory framework.