Investigating the Role of the Triple-Phase Boundary in Zinc-Air Cathodes Using Pore Network Modeling
Bibliographic record
Abstract
Zinc-air flow batteries are a promising energy storage technology. Their performance depends on their porous cathodes where the oxygen reduction reaction (ORR) occurs. A key feature of the cathode is the invasion of electrolyte, creating the so-called triple phase boundary between air, electrolyte and catalyst, which is shown in this work to be an overly simplified picture. In this study a mathematical framework based on pore network modeling (PNM) was developed to better understand the interplay between electrode structure, transport of species and electrolyte invasion. The results suggest that increasing electrolyte volume provides highly branched invasion pattern and enhances performance up to a saturation of 0.7 , whereas further invasion reduces air-liquid interfacial area and lowers the performance. Interestingly, at lower saturations (<0.3) the liquid structure is so excessively branched that hydroxide ions are unable to diffuse to the anode at a sufficient rate, resulting in supersaturation, which is a degradation problem. The pore size distribution of the catalyst layer also affects the performance with wider pore size distributions generally performing better. This work represents the first 3D PNM of a zinc-air cathode that includes all the key physics and transport mechanisms, enabling prediction of the structure-performance relationship of porous cathodes.
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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 | 0.001 | 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".