Applications of Graphene in Vanadium Redox Flow Batteries
Bibliographic record
Abstract
Vanadium redox flow batteries (VRFBs) exhibit great promise as easily scalable, long-lasting, modular systems for grid-scale energy storage. However, vanadium crossover and poor reaction kinetics increase their operating costs by requiring frequent system regeneration and reducing energy efficiency, respectively. In this thesis, Nafion membranes were modified with single to few-layer nitrogen/sulfur-doped graphene (NS-graphene) by developing a large area Langmuir film deposition method with the aim of reducing vanadium crossover and potentially improving reaction kinetics. Using this approach, the ability to reduce vanadium permeability through Nafion 117 and Nafion 115 membranes by 75% and 53%, respectively, was demonstrated while maintaining a high enough proton conductivity that the overall selectivity of the membranes was increased by 243% and 65% when compared to the results for bare Nafion. To determine the impact of the intrinsic electrocatalytic activity of graphene on redox flow battery performance, a comparison of NS-graphene, graphene oxide (GO), and reduced graphene oxide (RGO) was carried out using both monolayer electrodes and drop-cast films. Through this work, it was confirmed that the previously established approach developed by Punckt et al. [1] to account for porosity could not be extended to quasi-reversible systems such as that of the VRFB. An alternative data analysis scheme based on Dunn’s Method is proposed, showing mildly promising results, with more work needed in the area to develop strong conclusions.
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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) | 0.001 | 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".