Magnetic CuFe2O4 Nanoparticles with Pseudocapacitive Properties for Electrical Energy Storage
Bibliographic record
Abstract
This investigation is motivated by increasing interest in the development of magnetically ordered pseudocapacitors (MOPC), which exhibit interesting magnetocapacitive effects. Here, advanced pseudocapacitive properties of magnetic CuFe2O4 nanoparticles in negative potential range are reported, suggesting that CuFe2O4 is a promising MOPC and advanced negative electrode material for supercapacitors. A high capacitance of 2.76 F cm−2 is achieved at a low electrode resistance in a relatively large potential window of 0.8 V. The cyclic voltammograms and galvanostatic charge–discharge data show nearly ideal pseudocapacitive behavior. Good electrochemical performance is achieved at a high active mass loading due to the use of chelating molecules of ammonium salt of purpuric acid (ASPA) as a co-dispersant for CuFe2O4 nanoparticles and conductive multiwalled carbon nanotube (MCNT) additives. The adsorption of ASPA on different materials is linked to structural features of ASPA, which allows for different interaction and adsorption mechanisms. The combination of advanced magnetic and pseudocapacitive properties in a negative potential range in a single MOPC material provides a platform for various effects related to the influence of pseudocapacitive/magnetic properties on magnetic/pseudocapacitive behavior.
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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".