Frequency-Dependent Effective Capacitance of Supercapacitors Using Electrospun Cobalt-Carbon Composite Nanofibers
Bibliographic record
Abstract
Mixing carbon-based materials with pseudocapacitive material is a widely used strategy to prepare high-energy, high-power supercapacitors. However, phase separation is inevitable after extended charging/discharging which leads to the degradation of performance metrics of the device. Here, we prepare in a single step cobalt-incorporated carbon nanofibers (CNF) by electrospinning homogeneous solutions of polyacrylonitrile (PAN) with cobalt acetate at different nominal proportions (1:0 to 1:1), and investigate their stability and capacitive behavior in symmetric supercapacitors. The electrochemical analyzes demonstrated up to an order of magnitude increase in the effective capacitive with increasing the cobalt content at both close-to-dc frequencies and at around 50/60 Hz power line frequencies. The maximum capacitance is recorded for a nominal cobalt acetate-to-PAN ratio of 0.9 (e.g. 29, 27, 22 and 10 mF at 0.01. 0.1, 1 and 100 Hz respectively; both electrodes are loaded with 1 mg cm − 2 of active material each). All devices showed also an outstanding capacitance retention exceeding 99% for 10000 cycles at 1 mA and 5 mA charging/discharging rates each. The improved energy storage capabilities are attributed to both the increased electrical conductivity and high pyridinic nitrogen content which was revealed from the material characterization results using XRD, FE-SEM/TEM, and XPS.
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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 | 0.001 |
| 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 | 0.000 |
| 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".