Pseudocapacitive behavior of ferrimagnetic NiFe2O4-carbon nanotube electrodes prepared with a multifunctional dispersing agent
Bibliographic record
Abstract
We investigate the pseudocapacitive properties of ferrimagnetic NiFe2O4 ceramics in a 0.5 M Na2SO4 aqueous electrolyte. These ceramics have a high capacitance of 2.33 Fcm−2 since a more efficient colloidal technique is applied, which enhances the current response of the electrode in the negative potential range. NiFe2O4 is co-dispersed with multiwalled carbon nanotubes (MWCNTs) using Celestine Blue (CB) dye as a multifunctional co-dispersant. The capacitance measurements are performed by cyclic voltammetry, impedance spectroscopy and galvanostatic charge-discharge. The experiments reveal the synergistic effects of NiFe2O4 and conductive MWCNTs additives and the benefit they provide through co-dispersion with CB, which acts as an electron transfer mediator, enhancing pseudocapacitance. The mass ratio of NiFe2O4 and MWCNTs in the composite electrodes is varied to optimize the capacitance enhancement, lower electrical resistance at a high active mass loading of 40 mg cm−2, and achieve favorable capacitance retention at high charge-discharge rates. The results reveal a path for the development of pseudocapacitive ferrimagnetic ceramic composites that have advanced functionality.
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 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 | 0.001 |
| 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 | 0.001 |
| 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".