Fenton Reaction Doubled Biomass Carbon Activation Efficiency for High‐Performance Supercapacitors
Bibliographic record
Abstract
Abstract The huge consumption of alkali during biomass‐derived porous carbon production leads to pollution and high carbon‐emission. This study employs the concept of Fenton chemistry to achieve hierarchical porous biomass carbon materials with a remarkably high specific surface area of 3440 m 2 g −1 with double activation efficiency compared to traditional activation process. The optimized carbon electrode demonstrates exceptional specific capacitance of 425.2 F g −1 at a current density of 0.1 A g −1 and great rate performance (286.1 F g −1 at 100 A g −1 ) in 6 m KOH electrolyte. The enabled supercapacitor demonstrates remarkable cycling stability, retaining up to 99.74% of its initial capacitance after undergoing 20 000 charge–discharge cycles. In addition, the electrolyte ion distribution in different pore structures is simulated using Molecular Dynamics, which confirms that the structure is conducive to the rapid diffusion of ions, thus matching the excellent electrochemical properties. The assembled symmetric supercapacitors achieve a maximum energy density of 42.1 Wh kg −1 (12.1 Wh kg −1 based on cell stack mass) in TEABF 4 /AN electrolyte. This work presents an effective technique for the formation of porous structures from biomass precursors. The novel methodology can be applied to many other similar systems for energy storage and beyond.
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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".