A binder‐free Ir<sub>0.4</sub>Ru<sub>0.6</sub>‐oxide/functionalized multi‐walled carbon nanotube electrode for possible applications in supercapacitors
Bibliographic record
Abstract
An initial study on a simple and inexpensive method to form an Ir0.4Ru0.6‐oxide (MMO) coating onto high‐area plasma functionalized multi‐walled carbon nanotubes (f‐MWCNTs) at the bench‐scale for possible supercapacitor (SC) applications is presented. f‐MWCNT electrodes are prepared in a two‐step process combining the growth of MWCNTs directly onto a 316 stainless steel mesh by thermal‐chemical vapour deposition (t‐CVD), followed by the addition of oxygen‐containing functionalities to their surface by plasma functionalization. The plasma functionalization step is done to: (i) improve electrode wettability and (ii) improve capacitive properties through the addition of pseudocapacitive oxygen functionalities. A simple dip‐dry method is then employed to coat the f‐MWCNTs with the desired MMO coating (Ir0.4Ru0.6‐oxide) prepared initially in a liquid precursor mixture. f‐MWCNT electrodes are suspended and dipped into the precursor then heated in air to evaporate the solvent while building the oxide layer. The resulting MMO/f‐MWCNT electrode exhibits excellent stability in 4 mol/L KOH electrolyte, yielding larger specific capacitance values than those obtained on bare f‐MWCNT electrodes; at a charging/discharging current density of 0.5 mA cm−2, the MMO/f‐MWCNT and f‐MWCNT electrodes achieve specific capacitances of 664 ± 7 and 190 ± 30 F g−1 in a 3‐electrode cell, respectively. The MMO/f‐MWCNT electrodes show good rate capability performance up to 10 mA · cm−2 and excellent stability.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".