How Catalyst Dispersion Solvents Affect CO<sub>2</sub> Electrolyzer Gas Diffusion Electrodes
Bibliographic record
Abstract
Cathode gas diffusion electrodes (GDEs) in CO 2 electrolyzers facilitate reagent transport and the reduction of CO 2 into chemicals and fuels. While GDEs are routinely leveraged to achieve high rates of product formation, design principles for high-performing cathodes have not yet been established. In this report, we demonstrate the influence of a central parameter in GDE fabrication, the catalyst ink solvent, on the properties and performance of spray-coated cathode GDEs. We show that the choice of solvent used during catalyst deposition impacts the faradaic efficiency for CO by as much as 50% at 200 mA cm –2 . Moreover, the solvent modulates the surface area, hydrophobicity, and capillarity of GDE catalyst layers. By measuring the hydrodynamic radii of catalyst inks, we conclude that solvent-mediated ionomer aggregation is a key factor that affects the microstructure and properties of GDE catalyst layers. We find that using ethanol as the ink solvent promotes moderate ionomer aggregation and yields the highest performing GDEs. This work describes the influence of electrode fabrication methodologies and demonstrates practical methods for preparing GDEs.
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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) | 0.000 | 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 | 0.000 | 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".