Spectral Capacitance of Series and Parallel Combinations of Supercapacitors
Bibliographic record
Abstract
Abstract The porous nature of the electrode material in supercapacitors and the apparent conductivity of the electrolyte cause their impedance to show a complex frequency‐dependent behavior, which in turn makes it incorrect to treat them as ideal capacitors, even at a frequency of a few millihertz. This is particularly crucial if the intended application requires a configuration that uses stacked supercapacitor banks, in which errors in defining the metrics of the individual components accumulate. Although manufacturers provide supercapacitor ratings under DC only, by using a detailed impedance spectroscopy study of all possible series and parallel combinations of two different commercial 1 Farad carbon–carbon supercapacitors, we show that these nominal DC capacitances are not suitable to evaluate the equivalent capacitance. Instead, by using a model that consists of a series resistance and a constant‐phase element, we employed a real, effective capacitance (in proper Farad units) suitable for direct application. This effective capacitance can be used to define the frequency‐dependent quality factor of a supercapacitor and enables the easy calculation of series and parallel associations of identical or different devices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".