X‐ray Nano Computed Tomography of Electrospun Fibrous Mats as Flow Battery Electrodes
Bibliographic record
Abstract
Abstract Many electrochemical energy storage and conversion devices employ porous media as electrodes, gas diffusion layers or separators. Recently, electrospinning has received significant attention as a way to generate nano‐fibers of polymers with controlled morphology and properties that, once carbonised, can act as conductive and porous media for electrochemical energy devices. The recent advances in X‐ray computed tomography have led the technique to be widely used in the characterisation of energy technologies and porous media as it offers a uniquely non‐destructive insight into the 3D microstructure of materials. Here we present electrospun fibrous mats with uncontrolled, controlled and aligned morphology for use as redox flow battery electrodes and, for the first time, obtain ultra‐high resolution nano‐tomographic X‐ray imaging of the materials using a lab source. The virtual 3D volumes enable extraction of parameters that would not be possible via other characterisation routes.
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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 | 0.001 | 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) | 0.002 | 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".