Wood-derived biochar as thick electrodes for high-rate performance supercapacitors
Bibliographic record
Abstract
Abstract Developing effective electrodes with commercial-level active mass-loading (> 10 mg cm −2 ) is vital for the practical application of supercapacitors. However, high active mass-loading usually requires thick active mass layer, which severely hinders the ion/electron transport and results in poor capacitive performance. Herein, a self-standing biochar electrode with active mass-loading of ca. 40 mg cm −2 and thickness of 800 µm has been developed from basswood. The basswood was treated with formamide to incorporate N/O in the carbon structure, followed by mild KOH activation to ameliorate the pore size and introduce more O species in the carbon matrix. The as-prepared carbon monoliths possess well conductive carbon skeleton, abundant N/O dopant and 3D porous structure, which are favorable for the ion/electron transport and promoting capacitance performance. The self-standing carbon electrode not only exhibits the maximum areal/mass/volumetric specific capacitance of 5037.5 mF cm −2 /172.5 F g −1 /63.0 F cm −3 at 2 mA cm −2 (0.05 A g −1 ), but also displays excellent rate performance with 76% capacitance retention at 500 mA cm −2 (12.5 A g −1 ) in a symmetric supercapacitor, surpassing the state-of-art biomass-based thick carbon electrode. The assembled model can power typical electron devices including a fan, a digital watch and a logo made up of 34 light-emitting diodes for a proper period, revealing its practical application potential. This study not only puts forward a commercial-level high active mass-loading electrode from biomass for supercapacitor, but also bridges the gap between the experimental research and practical application. Graphical abstract
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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".