Pathways to Energy-efficient Chemical Upgrade via CO2 Electrolysis: Modulating the Microenvironment at Catalytic Sites
Bibliographic record
Abstract
The use of electricity to convert carbon into chemicals and fuels offers the potential to reduce the carbon intensity of hard-to-decarbonize sectors such as aviation fuel and chemical manufacturing. The electrochemical reduction of carbon dioxide (CO2R) can produce ethylene or ethanol, but present-day technology demands high energy. Enhancements in single-pass conversion efficiency (SPCE), Faradaic efficiency (FE), and cell voltage (V) are necessary to reduce cost, streamline product separation, and minimize carbon footprint.In this thesis, I tackle these challenges, designing catalysts that improve reactant gas transport, prevent overoxidation, and maintain active sites. Using electron microscopy, gas adsorption-desorption, and electrochemical characterization, catalysts' atomic structure, properties, and behavior are studied. Using operando Raman and X-ray absorption spectroscopy, the microenvironment of the catalyst is investigated during CO2R operation. Firstly, ligand-capped copper catalysts were synthesized and deployed in acidic CO2R towards the goal of selective ethylene production. Traditional copper catalysts in acidic conditions face the competing hydrogen evolution reaction (HER) due to the depletion of under-coordinated copper sites that are crucial for ethylene production. Monopod ligand-capped copper (Cu-1) demonstrates high ethylene selectivity due to hydrophobicity and strong binding affinity to copper, and preserves under-coordinated sites, this latter seen in operando spectroscopy. Next, I develop microporous carbon supports for efficient gas transport near catalytic sites. Carbon supports with varying nitrogen doping ratios and particle sizes affect ethanol and propanol selectivity. Introducing porous carbon supports controls local reactant gas concentrations, enhancing single-pass conversion efficiency to 85% and ethanol selectivity to 45% in the carbon monoxide reduction reaction (COR). Finally, aldehyde oxidation as an anodic reaction was explored to reduce cell voltage, an approach that takes advantage of low oxidation potentials and the anodic hydrogen evolution (a-HER). Previously-studied oxide-derived copper catalysts (OD-Cu) suffer from limited stability due to overoxidation. Gold-doped OD-Cu shows enhanced activity and stability, maintaining performance for over 10 hours at 350 mA/cm² due to resistance to overoxidation. Coupling a-HER with COR achieves 63% ethylene selectivity, 91% SPCE, and a cell voltage of 0.92 V at 400 mA/cm². A techno-economic assessment evaluates the low-carbon intensity system for sustainable chemicals production. Overall, this study points to the critical role of not only the catalyst, but also the electrochemical microenvironment – regulated by the catalyst support, the electrolyte, and the local environment operando – as key drivers of CO2 reduction performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".