Influence of High Energy Ball Milling and Dispersant on Capacitive Properties of Fe2O3—Carbon Nanotube Composites
Bibliographic record
Abstract
This investigation is motivated by increasing interest in ferrimagnetic materials and composites, which exhibit electrical capacitance. It addresses the need for the development of magnetic materials with enhanced capacitive properties and low electrical resistance. γ-Fe2O3-multiwalled carbon nanotube (MWCNT) composites are developed by colloidal processing and studied for energy storage in negative electrodes of supercapacitors. High energy ball milling (HEBM) of ferrimagnetic γ-Fe2O3 nanoparticles results in enhanced capacitive properties. The effect of HEBM on particle morphology is analyzed. Gallocyanine is used as a co-dispersant for γ-Fe2O3 and MWCNTs. The polyaromatic structure and catechol ligand of gallocyanine facilitated its adsorption on γ-Fe2O3 and MWCNTs, respectively, and facilitated their electrostatic dispersion and mixing. The adsorption mechanisms are discussed. The highest capacitance of 1.53 F·cm−2 is achieved in 0.5 M Na2SO4 electrolyte for composites, containing γ-Fe2O3, which is high energy ball milled and co-dispersed with MWCNTs using gallocyanine. HEBM and colloidal processing strategies allow high capacitance at low electrical resistance, which facilitates efficient charge–discharge. Obtained composites are promising for fabrication of multifunctional devices based on mutual interaction of ferrimagnetic and capacitive properties.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".