Optimizing selectivity and activity in heterogeneous and homogeneous catalysis for chemical production

Zhao, Yuanya

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https://hdl.handle.net/2142/120573 Copy

Description

Title

Optimizing selectivity and activity in heterogeneous and homogeneous catalysis for chemical production

Author(s)

Zhao, Yuanya

Issue Date

2023-04-28

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

Rodríguez-López, Joaquín

Doctoral Committee Chair(s)

Rodríguez-López, Joaquín

Committee Member(s)

• Murphy, Catherine J
• Flaherty, David W
• Shen, Mei

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Catalyst
• Secm
• Orr
• Hor
• Machine Learning
• Data Processing
• Tempo
• Glycerol Oxidation

Language

eng

Abstract

The world is marching towards the era of sustainability and environmentalism. Governments, companies, and individuals are gaining more and more awareness of protecting the Earth and taking actions on combating environment destruction. The grand topic of this thesis is utilizing electrochemistry for next generation technologies towards the greener production of chemicals. Chapter 1 introduces the recent advances in combinatorial screening methods for electrocatalysts, which increases the discovery and development efficiency of high-performance catalysts that can make chemical processes more productive, sustainable and environmental. Chapter 2 describes the electrochemical screening method for catalysts used for the thermocatalytic synthesis of hydrogen peroxide, which is a next-generation green oxidant. The transfer from traditional oxidants to hydrogen peroxide can greatly reduce pollution generated during industrial chemical production. The method screens catalysts based on their oxygen reduction and hydrogen oxidation activity probed via voltammetric scanning electrochemical microscopy. Chapter 3 discusses the application of machine learning models for fast processing of complicated experimental data such as the ones presented in Chapter 2. The machine learning-based approach can largely boost efficiency in chemistry research and thus expedite the development in technology and science. At the end, Chapter 4 pays attention to the environmental problem caused by the surplus of glycerol, proposing TEMPO as a homogeneous catalyst for glycerol valorization through electrochemical oxidation with a high selectivity towards valuable C3 products without C-C bond cleavage.

Graduation Semester

2023-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/120573

Copyright and License Information

Copyright 2023 Yuanya Zhao

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