Electrochemical supercapacitors and their complementarity to fuel cells and batteries
Bibliographic record
Abstract
Abstract In recent years, so‐called supercapacitors have been developed providing specific capacitances on the order of 50–100 Farads g −1 of high surface area substrates, e.g., carbon powders, fibers or aerogels, and some transition‐metal oxide films having specific real areas up to ca. 2000 m 2 g −1 . The capacitance of carbon‐based devices originates from the double‐layer capacity of the carbon/liquid electrolyte interfaces, giving rise to a high degree of reversibility of the charging/discharging processes, leading to perceived operability at large power densities. The large achievable specific capacitance provides systems for substantial charge and electrical energy storage, complementary to that provided by batteries. However, on account of the distributed nature of capacitative, C , and ohmic, R , components of the impedance, a broad range of RC time‐constants arises, corresponding to a power spectrum. Hence high power operation is available from only a fraction of the total capacitance at large current densities or a.c. frequencies, or in short‐time pulses. An important application of supercapacitors has been envisaged in a load‐leveling role in hybrid configuration with fuel cells or rechargeable batteries. To achieve this, the relation between operable power densities and achievable energy densities of each of the components has to be evaluated in terms of so‐called Ragone plots in a complementary way.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".