Automating electrochemical characterization for studies in energy storage and conversion

Pence, Michael A.

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https://hdl.handle.net/2142/129167 Copy

Description

Title

Automating electrochemical characterization for studies in energy storage and conversion

Author(s)

Pence, Michael A.

Issue Date

2025-02-13

Director of Research (if dissertation) or Advisor (if thesis)

Rodríguez-López, Joaquín

Doctoral Committee Chair(s)

Rodríguez-López, Joaquín

Committee Member(s)

• Kenis, Paul J.A.
• Han, Hee-Sun
• Jackson, Nicholas E.

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Electrochemistry
• Automation

Abstract

The climate crisis requires a dramatic change in the way we generate and utilize energy, shifting from fossil fuels to green and sustainable technologies. Technologies based on electrochemical principles are at the center of this energy revolution, but the discovery and optimization of emerging electrochemical systems require rapid progress to match the urgency of mounting societal pressures. To this end, laboratory automation in electrochemistry aims to accelerate discovery by increasing experimental throughput through continuous and passive experimentation. This thesis aims to develop a comprehensive toolbox for automating electrochemistry, enabling accelerated experimental throughput for electrochemical characterization of emerging technologies in energy storage (redox-flow batteries) and energy conversion (molecular catalysis). Chapter 1 reviews current progress in the field of automated electrochemistry with a focus the hardware design and construction of automated platforms. Chapter 2 presents the development of multiplexed and microfabricated electrode arrays as an enabling tool for automated measurement of redox-flow battery decomposition. Chapter 3 couples the microfabricated array strategy from Chapter 2 with an automated solution handling robot, the Electrolab, to study ionic strength-dependent dynamics of polyelectrolytes. Moving beyond energy storage, Chapter 4 demonstrates a streamlined automated electrochemistry platform, eLab, that integrates pH measurement into the experimental workflow to enable the study of the pH-dependent kinetics of electrocatalyzed alcohol oxidation. Chapter 5 incorporates the eLab platform with Bayesian optimization to autonomously identify experimental parameters that yield kinetically informative current-potential curves, finding application in molecular electrocatalysis. Finally, Chapter 6 provides an outlook on the future of automated electrochemistry, highlighting exciting avenues for further research.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129167

Copyright and License Information

Copyright 2025 Michael Pence

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