Green Carbon Nanofiber Networks for Advanced Energy Storage
Bibliographic record
Abstract
Abstract Energy storage devices such as supercapacitors of high performance are in great need due to the continuous expansion of digitalization and related devices for mobile electronics, autonomous sensors, and vehicles of different kinds. However, the nonrenewable resources and often complex preparation processes associated with electrode materials and structures pose limited scale-up in production and difficulties in versatile utilization of the devices. Here, free-standing and flexible carbon nanofiber networks derived from renewable and abundant bioresources are demonstrated. By a simple optimization of carbonization, the carbon nanofiber networks reach a large surface area of 1670 m2 g–1 and excellent specific gravimetric capacitance of ∼240 F g–1, outperforming many other nanostructured carbon, activated carbon, and even those decorated with metal oxides. The remarkable electrochemical performance and flexibility of the green carbon networks enable an all-solid-state supercapacitor device, which displays a device capacitance of 60.4 F g–1 with a corresponding gravimetric energy density of 8.4 Wh kg–1 while maintaining good mechanical properties.
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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) | 0.003 | 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".