V<sub>2</sub>O<sub>5</sub> and its Carbon‐Based Nanocomposites for Supercapacitor Applications
Bibliographic record
Abstract
Abstract Vanadium pentoxide (V2O5) is renowned among the highly efficient supercapacitor electrode‐materials for high power and energy densities, excellent specific capacitance, prolonged cycle lives, variable oxidation states of V, reversible nature of interconversions, theoretical importance, etc. Various synthetic methodologies and morphologies, formation of composites, and doping for tuning properties are the additional causes of interest. Different synthetic techniques like sol‐gel, solvothermal, electro‐deposition, electro‐spinning, atomic layer deposition, etc. are employed to prepare V2O5‐based electrode materials with merits and demerits. High rate of material agglomeration and poor conductivity limit its usage in pristine morphology. Accordingly, the impact on charge storage behavior of V2O5 on blending with various carbon‐based systems has been explored for materials like activated carbons, conducting polymers, carbon nanotubes and functionalized graphene systems as binary/ternary composites. The aim has been to optimize the key factors such as reduced nanostructure lumping, minimal interfacial resistance and ultrafast charge diffusion across hollow porous structures which may eventually lead to the theoretically expected high specific capacitance (>1000 F g−1). In this review, we have discussed on the recent progress in the research of V2O5‐based materials and highlighted on the correlation between morphology and electrochemical performances. In the course, we have attempted to delineate the advantage‐disadvantages of different composite morphologies that may help to outline the present status and future aspects of these materials that the authors believe will be of first‐hand assistance especially to the beginners in the field of research.
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 | 0.000 | 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".