Capacity Contribution Induced by Pseudo-Capacitance Adsorption Mechanism of Anode Carbonaceous Materials Applied in Potassium-ion Battery
Bibliographic record
Abstract
The intrinsic bottleneck of graphite intercalation compound mechanism in potassium-ion batteries necessitates the exploitation of novel potassium storage strategies. Hence, utmost efforts have been made to efficiently utilize the extrinsic pseudo-capacitance, which offers facile routes by employing low-cost carbonaceous anodes to improve the performance of electrochemical kinetics, notably facilitating the rate and power characteristics for batteries. This mini-review investigates the methods to maximize the pseudo-capacitance contribution based on the size control and surface activation in recent papers. These methods employ the use of cyclic voltammetry for kinetics analysis, which allows the quantitative determination on the proportion of diffusion-dominated vs. pseudo-capacitance by verifying a representative pseudo-capacitive material of single-walled carbon nanotubes. Synergistically, additional schemes such as establishing matched binder-electrolyte systems are in favor of the ultimate purpose of high-performance industrialized potassium-ion batteries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".