Insights into the Effect of Structural Heterogeneity in Carbonized Electrospun Fibrous Mats for Flow Battery Electrodes by X‐Ray Tomography
Bibliographic record
Abstract
Electrospun custom made flow battery electrodes are imaged in 3D using X-ray computed tomography. A variety of computational methods and simulations are applied to the images to determine properties including the porosity, fiber size, and pore size distributions as well as the material permeability and flow distributions. The simulations are performed on materials before and after carbonization to determine the effect it has in the internal microstructure and material properties. It is found that the deposited fiber size is constantly changing throughout the electrospinning process. The results also show that the surfaces of the fibrous material are the most severely altered during carbonization and that the rest of the material remained intact. Pressure driven flow is modeled using the lattice Boltzmann method and excellent agreement with experimental results is found. The simulations coupled with the material analysis also demonstrate the highly heterogeneous nature of the flow. Most of the flow is concentrated to regions with high porosity while regions with low porosity shield other pores and starve them of flow. The importance of imaging these materials in 3D is highlighted throughout.
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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 | 0.000 | 0.000 |
| 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".