Bio-carbon composite for supercapacitor electrodes: Harnessing hydrochar frameworks and bio-tar polymerization
Bibliographic record
Abstract
Bio-tar, a promising renewable carbon precursor, has garnered significant attention for its potential in supercapacitor electrode applications. However, the polymerization of bio-tar into carbon presents challenges, particularly in achieving a dense, interconnected pore structure essential for optimal electrochemical performance. This study introduced an innovative approach using hydrochar as a framework combined with bio-tar as the carbon source to synthesize bio-carbon composite. The results showed that the prepared bio-carbon exhibited a stable morphological structure in which the hydrochar skeleton supported the wrapping of bio-tar originated carbon, specifically at a hydrochar to bio-tar ratio of 1:6. And it also showed a maximum specific surface area of 2714.27 m 2 /g, with a mesopore ratio of 68.79 % at an activation temperature of 800 °C. The optimal electrochemical properties were observed at the highest specific capacitance of 340.4 F/g in a three-electrode system under a current density of 0.5 A/g. When assembled into a supercapacitor, the single-pole specific capacitance reached 213.3 F/g at 0.5 A/g. The structure-property relationship suggested that the water contact angle is a key factor influencing the specific capacitance, particularly at high specific surface areas. This study demonstrated an innovative way to prepare sustainable composite bio-carbon material with excellent electrochemical performance. • Highly interconnected pore structure observed in the activated bio-carbon composite. • Potential mechanism of pore development in bio-carbon composite was proposed. • Possessing a high specific capacitance of 340.4 F/g and excellent rate capability. • Relationship between surface area and water contact angle on capacitance was revealed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".