Advanced nitrogen-doped wood-derived biocarbon for supercapacitor electrode applications
Bibliographic record
Abstract
Biomass-derived carbon materials are receiving much attention for supercapacitor applications due to their well-developed porous structures, large specific surface areas, good conductivity, and environmental sustainability. In this study, we present a simple and scalable green strategy to prepare nitrogen (N)-doped biocarbon materials from naturally decayed wood (rotten wood, RW), which is to take advantage of the intrinsic porous nature of wood and the structural modifications induced by microbial activation (e.g., surface oxidation and nitrogen incorporation). To further increase the doped nitrogen content, we used aqueous ethylenediamine (EDA) solution for activating and immersing pre-treated RW. The N-doped biocarbon samples show uniform nitrogen distribution and favorable graphitization, resulting in outstanding supercapacitor performance. The optimized sample, RW-1000, exhibits a high specific surface area of 1204 m 2 ·g -1 with a rational pore structure. When applied as a supercapacitor electrode, RW-1000 demonstrates excellent electrochemical properties, including a specific capacitance of 448 F·g -1 at 0.2 A·g -1 and remarkable stability, retaining 95% of its capacitance after 10,000 cycles. This exceptional energy storage performance is attributed to the RW-1000-derived electrode’s high specific surface area, optimal pore size distribution, and well-dispersed nitrogen content. Thus, our work offers a sustainable, scalable and facile strategy for transforming biomass waste into valuable biocarbon materials for high performance supercapacitors.
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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".