Electrolytic CO<sub>2</sub> Reduction in a Flow Cell
Bibliographic record
Abstract
Conspectus Electrocatalytic CO 2 conversion at near ambient temperatures and pressures offers a potential means of converting waste greenhouse gases into fuels or commodity chemicals (e.g., CO, formic acid, methanol, ethylene, alkanes, and alcohols). This process is particularly compelling when driven by excess renewable electricity because the consequent production of solar fuels would lead to a closing of the carbon cycle. However, such a technology is not currently commercially available. While CO 2 electrolysis in H-cells is widely used for screening electrocatalysts, these experiments generally do not effectively report on how CO 2 electrocatalysts behave in flow reactors that are more relevant to a scalable CO 2 electrolyzer system. Flow reactors also offer more control over reagent delivery, which includes enabling the use of a gaseous CO 2 feed to the cathode of the cell. This setup provides a platform for generating much higher current densities ( J ) by reducing the mass transport issues inherent to the H-cells. In this Account, we examine some of the systems-level strategies that have been applied in an effort to tailor flow reactor components to improve electrocatalytic reduction. Flow reactors that have been utilized in CO 2 electrolysis schemes can be categorized into two primary architectures: Membrane-based flow cells and microfluidic reactors. Each invoke different dynamic mechanisms for the delivery of gaseous CO 2 to electrocatalytic sites, and both have been demonstrated to achieve high current densities ( J > 200 mA cm –2 ) for CO 2 reduction. One strategy common to both reactor architectures for improving J is the delivery of CO 2 to the cathode in the gas phase rather than dissolved in a liquid electrolyte. This physical facet also presents a number of challenges that go beyond the nature of the electrocatalyst, and we scrutinize how the judicious selection and modification of certain components in microfluidic and/or membrane-based reactors can have a profound effect on electrocatalytic performance. In membrane-based flow cells, for example, the choice of either a cation exchange membrane (CEM), anion exchange membrane (AEM), or a bipolar membrane (BPM) affects the kinetics of ion transport pathways and the range of applicable electrolyte conditions. In microfluidic cells, extensive studies have been performed upon the properties of porous carbon gas diffusion layers, materials that are equally relevant to membrane reactors. A theme that is pervasive throughout our analyses is the challenges associated with precise and controlled water management in gas phase CO 2 electrolyzers, and we highlight studies that demonstrate the importance of maintaining adequate flow cell hydration to achieve sustained electrolysis.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".