Application of Octanohydroxamic Acid for Salting out Liquid–Liquid Extraction of Materials for Energy Storage in Supercapacitors
Bibliographic record
Abstract
The ability to achieve high areal capacitance for oxide-based supercapacitor electrodes with high active mass loadings is critical for practical applications. This paper reports the feasibility of the fabrication of Mn3O4-multiwalled carbon nanotube (MWCNT) composites by the new salting-out method, which allows direct particle transfer from an aqueous synthesis medium to a 2-propanol suspension for the fabrication of advanced Mn3O4-MWCNT electrodes for supercapacitors. The electrodes show enhanced capacitive performance at high active mass loading due to reduced particle agglomeration and enhanced mixing of the Mn3O4 particles and conductive MWCNT additives. The strategy is based on the multifunctional properties of octanohydroxamic acid, which is used as a capping and dispersing agent for Mn3O4 synthesis and an extractor for particle transfer to the electrode processing medium. Electrochemical studies show that high areal capacitance is achieved at low electrode resistance. The electrodes with an active mass of 40.1 mg cm−2 show a capacitance of 4.3 F cm−2 at a scan rate of 2 mV s−1. Electron microscopy studies reveal changes in electrode microstructure during charge-discharge cycling, which can explain the increase in capacitance. The salting-out method is promising for the development of advanced nanocomposites for energy storage in 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 | 0.000 |
| 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".