Accelerated development of gas diffusion electrodes for CO2 electrolyzers
Bibliographic record
Abstract
Here we present a high-throughput flexible automation system, AdaCarbon, to accelerate the development of gas diffusion electrodes (GDEs) for CO2 electrolysis. AdaCarbon consists of a team of seven robots with automated modules for GDE fabrication and characterization and an automated test cell (ATC) that performs zero-gap CO2 electrolysis. We use this platform to fabricate and test 90 GDEs (30 unique GDEs in triplicate) with varied compositions of Cu-Ag metal and Nafion-Sustainion ionomer bilayers with the goal of increasing the yield of ethylene produced at the current density of 200 mA cm–2. We show GDEs with higher Cu and Nafion ionomer content increased ethylene selectivity 5 to 9%. We also demonstrate that AdaCarbon accelerates the workflow for making and testing GDEs by a factor of three compared to a manual workflow.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".