Petaloid-shaped hierarchical porous carbon sub-microspheres derived from lignin for high-performance supercapacitor electrodes
Bibliographic record
Abstract
In this study, alkali lignin (AL), a byproduct of the sulfate pulping process, is utilized as a precursor to synthesize hierarchical porous carbon sub-microspheres (HALCS) with high specific surface area through high shear homogenization and chemically activated carbonization. The implementation of high-speed mechanical shear effectively reduces the particle size of lignin, leading to the formation of sub-microparticles. HALCS with distinct grooves and petal-like structures are obtained through a chemical activation-assisted high-temperature carbonization process. HALCS exhibits a hierarchical pore structure comprising macropores, mesopores, and micropores, with a specific surface area reaching 898.8 m 2 /g and a pore volume of 0.634 cm 3 /g, significantly surpassing alkali lignin-derived carbon (ALC) produced under similar carbonization conditions. In the 1 M H 2 SO 4 electrolyte environment, HALCS exhibits a specific capacitance of 433 F/g at a current density of 0.8 A/g. Moreover, a symmetric supercapacitor based on HALCS achieves maximum energy density and corresponding power density values of 17.2 Wh/kg and 100 W/kg, respectively, while maintaining excellent cycling stability undergoing 5000 charge-discharge cycles. Consequently, this straightforward and environmentally friendly synthesis strategy for alkali lignin-derived hierarchical porous carbon sub-microspheres opens up new avenues for the utilization of carbon materials derived from biomass in high-performance energy storage devices.
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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".