Scaling CO<sub>2</sub> Electrolyzer Cell Area from Bench to Pilot
Bibliographic record
Abstract
To contribute meaningfully to carbon dioxide (CO 2 ) emissions reduction, CO 2 electrolyzer technology will need to scale immensely. Bench-scale electrolyzers are the norm, with active areas <5 cm 2 . However, cell areas on the order of 100s or 1000s of cm 2 will be required for industrial deployment. Here, we study the effects of increasing cell area, scaling over 2 orders of magnitude from a 5 cm 2 lab-scale cell to an 800 cm 2 pilot plant-scale cell. A direct scaling of the bench-scale cell architecture to the larger area results in a ∼20% drop in ethylene (C 2 H 4 ) selectivity and an increase in the parasitic hydrogen (H 2 ) evolution reaction (HER). We instrument an 800 cm 2 electrolyzer cell to serve as a diagnostic tool and determine that nonuniformities in electrode compression and flow-influenced local CO 2 availability are the key drivers of performance loss upon scaling. Machining of an initial 800 cm 2 cell results in a standard deviation in MEA compression that is 7-fold that of a similarly produced 5 cm 2 cell (0.009 mm). Using these findings, we redesign an 800 cm 2 cell for compression tolerance and increased CO 2 transport and achieve an H 2 FE in the revised 800 cm 2 cell similar to that of the 5 cm 2 case (16% at 200 mA cm –2 ). These results demonstrate that by ensuring uniform compression and fluid flow, the CO 2 electrolyzer area can be scaled over 100-fold and retain C 2 H 4 selectivity (within 10% of small-scale selectivity).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".