Structure & composition of carbon fibers for electrochemical applications
Bibliographic record
Abstract
Carbon-based electrode materials are used in a broad range of energy storage systems and influence their performance significantly. Electrode materials must be investigated to optimize the technologies' efficiency. This study examines lab-fabricated and commercial electrode materials for vanadium redox flow batteries (VRFBs), and the influence of thermal treatment on these materials. Scanning electron microscopy images and X-ray nano-computed tomography revealed significant differences between the 3D shapes of the carbon fibers, which are influenced by the choice of precursor material and manufacturing process. Both have a crucial influence on the inner structure of the fibers, such as holes, which lower the mechanical stability. Furthermore, the composition of the fibers was assessed using wide-angle X-ray scattering and X-ray photoelectron spectroscopy highlighting especially differences in the fibers' oxygen- and carbon content. The applied thermal treatment increased the O-content and thus enhanced the material's wettability, which was investigated with dynamic vapor sorption. No structural changes in the fiber shape were monitored after thermal treatment. The materials' electrochemical performance was studied for VRFBs. The study of different electrode materials here shows the importance of choosing a suitable precursor and manufacturing process and the need for a multimodal characterization of materials to identify potential candidates.
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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) | 0.002 | 0.001 |
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".