Green Carbon Nanofiber Networks for Advanced Energy Storage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it