Investigating the Influence of Treatments on Carbon Felts for Vanadium Redox Flow Batteries
Bibliographic record
Abstract
Vanadium redox flow battery (VRFB) electrodes face challenges related to their long-term operation. We investigated different electrode treatments mimicking the aging processes during operation, including thermal activation, aging, soaking, and storing. Several characterization techniques were used to deepen the understanding of the treatment of carbon felts. Synchrotron X-ray imaging, electrochemical impedance spectroscopy (EIS) with the distribution of relaxation times analysis, and dynamic vapor sorption (DVS) revealed differences between the wettability of felts. The bulk saturation after electrolyte injection into the carbon felts significantly differed from 8 % to 96 %. DVS revealed differences in the sorption/desorption behavior of carbon felt ranging from a slight change of 0.8 wt % to over 100 wt %. Additionally, the interactions between the water vapor and the sample change from type V to type H2. After treatment, morphology changes were observed by atomic force microscopy and scanning electron microscopy. Cyclic voltammetry and EIS were used to probe the electrochemical performance, revealing different catalytic activities and transport-related impedances for the treated samples. These investigations are crucial for understanding the effects of treatments on the performance and optimizing materials for long-term operation.
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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".