Design of B/N Co-doped micro/meso porous carbon electrodes from CNF/BNNS/ZIF-8 nanocomposites for advanced supercapacitors
Bibliographic record
Abstract
Boron (B) and nitrogen (N) co-doped 3D hierarchical micro/meso porous carbon (BNPC) were successfully fabricated from cellulose nanofiber (CNF)/ boron nitride nanosheets (BNNS)/ zinc-methylimidazolate framework-8 (ZIF-8) nanocomposites prepared by 2D BNNS, ZIF-8 nanoparticles, and wheat straw based CNFs. Herein, CNF/ZIF-8 acts as versatile skeleton and imparts partial N dopant into porous carbon structure, while the introduced BNNS can help strengthen the hierarchical porous superstructure and endow abundant B/N co-dopants within BNPC matrix. The obtained BNPC electrode possesses a high specific surface area of 505.4 m2/g, high B/N co-doping content, and desirable hydrophilicity. Supercapacitors assembled with BNPC-2 (B/N co-doped porous carbon with a CNF/BNNS mass ratio of 1꞉2) electrodes exhibited exceptional electrochemical performance, demonstrating high capacitance stability even after 5 000 charge-discharge cycles. The devices exhibited outstanding energy density and power density, as well as the highest specific capacitance of 433.4 F/g at 1.0 A/g, when compared with other similar reports. This study proposes a facile and sustainable strategy for efficiently fabrication of rich B/N co-doped hierarchical micro/meso porous carbon electrodes from agricultural waste biomass for advanced supercapacitor performance.
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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".