Mesoporous Carbon Microfibers for Electroactive Materials Derived from Lignocellulose Nanofibrils
Bibliographic record
Abstract
Abstract The growing adoption of biobased materials for electronic, energy conversion, and storage devices has relied on high-grade or refined cellulosic compositions. Herein, lignocellulose nanofibrils (LCNF), obtained from simple mechanical fibrillation of wood, are proposed as a source of continuous carbon microfibers obtained by wet spinning followed by single-step carbonization at 900 °C. The high lignin content of LCNF (∼28% based on dry mass), similar to that of the original wood, allowed the synthesis of carbon microfibers with a high carbon yield (29%) and electrical conductivity (66 S cm–1). The incorporation of anionic cellulose nanofibrils (TOCNF) enhanced the spinnability and the porous morphology of the carbon microfibers, making them suitable platforms for electrochemical double layer capacitance (EDLC). The increased loading of LCNF in the spinning dope resulted in carbon microfibers of enhanced carbon yield and conductivity. Meanwhile, TOCNF influenced the pore evolution and specific surface area after carbonization, which significantly improved the electrochemical double layer capacitance. When the carbon microfibers were directly applied as fiber-shaped supercapacitors (25 F cm–3), they displayed a remarkably long-term electrochemical stability (>93% of the initial capacitance after 10 000 cycles). Solid-state symmetric fiber supercapacitors were assembled using a PVA/H2SO4 gel electrolyte and resulted in an energy and power density of 0.25 mW h cm–3 and 65.1 mW cm–3, respectively. Overall, the results indicate a green and facile route to convert wood into carbon microfibers suitable for integration in wearables and energy storage devices and for potential applications in the field of bioelectronics.
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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) | 0.002 | 0.001 |
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".