Benchmarking the ragone behaviour and power performance trends of pseudocapacitive batteries
Bibliographic record
Abstract
Abstract The ‘holy grail’ of energy storage is to achieve both high energy and high power densities ( ⩾ 100 Wh l−1 and ∼ 10 4 W l−1, respectively) as characterized in a Ragone plot. However, across the macroscopic dimensions over which energy storage systems operate, power performance is fundamentally limited by both drift and diffusion processes. In this work a macroscopic variation on the Gerischer–Hopfield formalism is applied to explore how the motion of electrical charges, moving between redox species, and screening counter-ions might be engineered in a pseudocapacitive system employing quantized capacitance (in the form of a pseudocapacitive battery) to reach this long-sought metric. Our theoretical findings show that the electron diffusion timescale between redox species generally determines power performance trends when pseudocapacitive coatings are applied monolithically. This electron-diffusion–dominated timescale, in turn, is shown to scale with the square of the coating thickness. However, when conducting pathways (or shunts) are introduced to substantially reduce the mean distance for electron diffusion the Ragone performance becomes dominated by ion drift and diffusion—even when the diffusion constants of all species are held equal. The resulting trends, for this shunting regime, show a power performance timescale that scales in a more linear fashion with increasing thickness of the redox-active region. By analyzing the Ragone performance metrics for realistic coating thicknesses between these two operational regimes, the resulting findings suggest that the diffusion constants needed to achieve the aforementioned high-performance metrics are plausibly achievable for both electronic and ionic charges in this proposed class of pseudocapacitive systems.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".