How to Enhance Gas Removal from Porous Electrodes?
Bibliographic record
Abstract
This article presents a structure-based modeling approach to optimize gas evolution at an electrolyte-flooded porous electrode. By providing hydrophobic islands as preferential nucleation sites on the surface of the electrode, it is possible to nucleate and grow bubbles outside of the pore space, facilitating their release into the electrolyte. Bubbles that grow at preferential nucleation sites act as a sink for dissolved gas produced in electrode reactions, effectively suctioning it from the electrolyte-filled pores. According to the model, high oversaturation is necessary to nucleate bubbles inside of the pores. The high oversaturation allows establishing large concentration gradients in the pores that drive a diffusion flux towards the preferential nucleation sites. This diffusion flux keeps the pores bubble-free, avoiding deactivation of the electrochemically active surface area of the electrode as well as mechanical stress that would otherwise lead to catalyst degradation. The transport regime of the dissolved gas, viz. diffusion control vs. transfer control at the liquid-gas interface, determines the bubble growth law.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".