Kinetics process for structure-engineered integrated gradient porous paper-based supercapacitors with boosted electrochemical performance
Bibliographic record
Abstract
Due to their rich and adjustable porous network structure, paper-based functional materials have become a research hotspot in the field of energy storage. However, reasonably designing and making full use of the rich pore structure of paper-based materials to improve the electrochemical performance of paper-based energy storage devices still faces many challenges. Herein, we propose a structure engineering technique to develop a conductive integrated gradient porous paper-based (CIGPP) supercapacitor, and the kinetics process for the influence of gradient holes on the electrochemical performance of the CIGPP is investigated through experimental tests and COMSOL simulations. All results indicate that the gradient holes endow the CIGPP with an enhanced electrochemical performance. Specifically, the CIGPP shows a significant improvement in the specific capacitance, displays rich frequency response characteristics for electrolyte ions, and exhibits a good rate performance. Also, the CIGPP supercapacitor exhibits a low self-discharge and maintains a stable electrochemical performance in different electrolyte environments because of gradient holes. More importantly, when the CIGPP is used as a substrate to fabricate a CIGPP-PANI hybrid, it still maintains good electrochemical properties. In addition, the CIGPP supercapacitor also shows excellent stability and sensitivity for monitoring human motion and deaf-mute voicing, showing potential application prospects. This study provides a reference and feasible way for the design of structure-engineered integrated paper-based energy storage devices with outstanding comprehensive electrochemical performance.
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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 | 0.000 | 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".