High Areal Capacitance of V<sub>2</sub>O<sub>3</sub>–Carbon Nanotube Electrodes
Bibliographic record
Abstract
V 2 O 3 -multiwalled carbon nanotube (MWCNT) electrodes with active mass loading of 30 mg cm −2 and ratio of active material mass to current collector mass of 33% have been developed for charge storage in electrochemical supercapacitors. Good electrochemical performance at high active mass loading allowed for an electrode areal capacitance as high as 4.4 F cm −2 and low electrode resistance. This high performance was, in part, achieved using an advanced colloidal processing method, which involved the use of lauryl gallate (LG) as a dispersant. It was demonstrated that LG allowed for good dispersion of both V 2 O 3 and MWCNT and facilitated their mixing. An electrode activation procedure (AP) was also developed, and contributed to the high capacitance and low resistance at high active mass loadings. To further understand the AP, as received and ball milled V 2 O 3 electrodes were investigated using cyclic voltammetry and impedance spectroscopy before and after activation, as well as with cycling stability. This, coupled with XRD, XPS and SEM data provided an insight into the composition and morphology changes of the active material. The influence of particle size on electrode capacitance, cyclic stability and capacitance retention at high charge-discharge rates has been analyzed. The results of this investigation showed that V 2 O 3 -MWCNT composites are promising for practical applications in electrochemical supercapacitors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".