Preparation of N-doped porous biocarbon with high surface area via controlled-burning for Zn-ion hybrid supercapacitor applications
Bibliographic record
Abstract
A novel strategy has been developed for synthesizing high-surface area nitrogen-doped biocarbon derived from rotten wood, tailored for efficient zinc-ion hybrid supercapacitor applications. By employing a Borax-K₂CO₃ system that enables controlled combustion in an air environment, the intrinsic interconnected channels and pores of natural wood are effectively preserved and further enhanced. This molten salt system not only prevents the collapse of the inherent porous structure but also promotes additional pore formation through activation. Consequently, a porous biocarbon with a hierarchical structure is produced, achieving an impressive specific surface area of 2196 m²·g⁻¹ . The optimized biocarbon material (RW-15) was integrated into a zinc-ion hybrid supercapacitor, delivering a notable capacitance of 175 F·g⁻¹ at 0.5 A·g⁻¹ . Additionally, the device retained 97 % of its initial capacitance over 10,000 cycles and achieved a notable energy density of 79 Wh·kg⁻¹ within a wide operating voltage window of 1.8 V. This work presents a sustainable, cost-effective, and scalable method for producing advanced porous carbon materials, offering strong potential for next-generation energy storage technologies. • Developed a Borax-K₂CO₃ assisted method to convert rotten wood into N-doped carbon via controlled burning in ambient air. • Produced hierarchical porous biocarbon with an exceptionally high specific surface area of 2196 m²·g⁻¹ . • Delivered capacitance of 175 F·g⁻¹ at 0.5 A·g⁻¹ and 79 Wh·kg⁻¹ energy density in zinc-ion hybrid supercapacitor . • Demonstrated exceptional cycling stability with 97 % capacitance retention over 10,000 charge-discharge cycles.
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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) | 0.000 | 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 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".