Spatiotemporal visualization of CO2 electrolysis
Bibliographic record
Abstract
Abstract We report here an electrolysis optical coherence tomography (eOCT) platform to visualize the chemical reactions occurring in a CO2 electrolyzer. The eOCT platform was designed to capture three dimensional images and videos at high spatial (~1 µm) and temporal (<1 µs) resolutions. We captured 12 h of footage of an electrolyzer, containing a porous electrode separated by a membrane, converting a continuous feed of liquid KHCO3 to reduce CO2 into CO at applied current densities of 50-800 mA cm−2. This video resolved the nucleation and formation of CO2 in the cathode compartment upon reaction of HCO3– with H+ delivered through the membrane, the formation of CO at the cathode|electrolyte interface, and H2 throughout the cathode compartment. The video captured the strikingly dynamic movement of the cathode and membrane components during electrolysis, and linked CO production to regions of the electrolyzer in which CO2 were in direct contact with both the membrane and the catalyst layers. This visualization enabled us to correlate the decrease in CO production to a buildup of CO2, CO, and H2, and (bi)carbonate salts at the cathode layer. The statistical analysis of phase distribution images allowed us to understand the competition between main (CO2 reduction) and side (hydrogen evolution) reactions in both time and space domains. We also show how a pulsing current liberates these residual bubbles and (bi)carbonates to recover CO production. These results highlight the power of using an eOCT platform to temporally and spatially resolve CO2RR 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".