A Nano-Carbon Scaffold (NCS) Electrode for the Vanadium Redox Flow Battery
Bibliographic record
Abstract
In this study a template nano-carbon scaffold (NCS) electrode material is evaluated for use in the vanadium redox flow battery (VRB). Scanning electron microscopy (SEM) was used to characterize the morphology of the electrode materials. This material has an organized nanoporous structure and the pore size can be as small as 18nm. The electrochemical properties of the NCS materials were explored using cyclic voltammetry (CV). To investigate the performance of NCS as an electrode material, the NCS was attached to the surface of conventional carbon paper electrodes. The charge discharge performance of the VRB was determined using a flow through mode of operation. The performance of nano carbon scaffold (NCS) with different pore size and thickness was compared with a conventional heat-treated carbon paper. The results show that by using nano carbon scaffold (NCS-85-HT), the voltage efficiency increased from 77% to 94% at 10 mAcm-2. The energy efficiency also increased from 56% to 69% at 10 mAcm-2due to the increased voltage efficiency. The results indicate that the large surface area of the NCS, associated with its nano structure, lead to a reduction in overpotential of around 75%, and thus higher battery efficiencies. Cell performance under different current density was also explored and the improved efficiencies for NCS were maintained at all the current densities studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".