Bio-carbon composite for supercapacitor electrodes: Harnessing hydrochar frameworks and bio-tar polymerization
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it