Utilizing Pore Network Modeling for Performance Analysis of Multi-Layer Electrodes in Vanadium Redox Flow Batteries
Bibliographic record
Abstract
Vanadium redox flow batteries (VRFBs) are promising energy storage devices. The microstructure of the porous electrode affects the performance of VRFBs. Therefore, identifying optimized electrode structures is an active research area. However, designing optimal microstructures requires studying varieties of structural parameters and design cases using a modeling tool with low computational cost. In this study, a pore network modeling (PNM) framework was developed to study the effects of multi-layer electrodes on VRFB electrode performance. In contrast to previous experimental works that were focused on multi-layer structure of the same material, this study explored the effect of using different microstructures in each layer. Using an image generation algorithm, fibrous materials were generated from which pore networks were extracted. The developed PNM included a modification by adding throat nodes in the geometry to accommodate a velocity dependent mass transfer coefficient. The results showed that putting a highly permeable layer near the membrane provides an alternative preferential path for fluid to distribute and supply those regions with reactive species, resulting in 57% increase in limiting current density in contrast to the opposite order. However, selection of the desired structures must be based on a trade-off between the current/power density and pressure drop.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".