High areal capacitance of Fe<sub>3</sub>O<sub>4</sub>‐decorated carbon nanotubes for supercapacitor electrodes
Bibliographic record
Abstract
Abstract A conceptually new approach has been developed for the fabrication of magnetite (Fe3O4)‐decorated carbon nanotubes (M‐CNTs) for negative electrodes of electrochemical supercapacitors. M‐CNTs were prepared by an ultrasonic‐assisted chemical synthesis method, which involved dispersion of functionalized CNTs in water, Fe3O4 formation on the CNTs surface, and particle extraction through liquid‐liquid interface (PELLI). Palmitic acid was found to be an efficient new extractor for PELLI. The slurries produced after drying and redispersing M‐CNTs and slurries obtained using PELLI were used for electrode fabrication. The electrodes prepared using PELLI showed superior performance due to reduced particle agglomeration. Testing results provided an insight into the influence of Fe3O4/CNTs mass ratio on the capacitance and capacitance retention at high charge‐discharge rates. A capacitance of 5.82 F cm−2 (145.4 F g−1) was achieved in Na2SO4 electrolyte using electrodes with high active mass of 40 mg cm−2 and ratio of active mass to current collector mass of 0.6. Good electrochemical performance was achieved at low impedance. The capacitance of the negative M‐CNTs electrodes was comparable with capacitance of advanced positive MnO2‐CNTs electrodes, which was beneficial for the fabrication of asymmetric devices. The asymmetric device has been fabricated, which showed promising performance in a voltage window of 1.6 V.
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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 | 0.000 | 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".