Electrochemical CO2 Reduction in a Membrane Electrode Assembly: From Bench to Pilot Scale
Bibliographic record
Abstract
The recent increase in global wealth and productivity has occurred at the expense of fossil fuel use and carbon dioxide emissions – which is the main driver of climate change. The electrochemical reduction of CO2 offers a pathway to consume excess CO2 and store excess renewable energy. Despite the numerous advancements in the field, there are a limited number of studies focused on scalability of systems and catalysts. This thesis seeks to address and overcome the most significant barriers to scaling CO2 electrolysis systems. The first work describes the design of a membrane electrode assembly system for CO2 reduction to high-value, concentrated multi-carbon products that avoided the electrolyte instability of previous liquid catholyte flow cells (Chapter 3). To reduce the energy cost of anode gas separation, a permeable CO¬2 regeneration layer is coupled with a cation exchange membrane to eliminate CO2 crossover and achieve high CO2 single-pass conversion (Chapter 4). A scalable, electrically conductive electrode is designed to be resistant to the most common failure mechanisms in CO2 reduction and it is scaled by several orders of magnitude (to 8000 cm2) in the largest CO2 electrolysis demonstration to date (Chapter 5). The initial pilot-scale cell design suffers from significant performance penalties compared to the lab-scale cells. A new pilot-scale cell is designed to overcome the major shortcomings of the previous design and it demonstrated improved selectivity towards CO2 reduction products (Chapter 6). Energy-dense liquid alcohols are generated with high energy efficiency, high concentration, and low crossover with a scalable system design via carbon monoxide reduction (Chapter 7). Collectively in this work, significant progress is made towards the practical application of electrochemical technologies for CO2 conversion to multi-carbon products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".