Interconnected Carbon Nanosheets Derived from Hemp for Ultrafast Supercapacitors with High Energy
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
We created unique interconnected partially graphitic carbon nanosheets (10-30 nm in thickness) with high specific surface area (up to 2287 m(2) g(-1)), significant volume fraction of mesoporosity (up to 58%), and good electrical conductivity (211-226 S m(-1)) from hemp bast fiber. The nanosheets are ideally suited for low (down to 0 °C) through high (100 °C) temperature ionic-liquid-based supercapacitor applications: At 0 °C and a current density of 10 A g(-1), the electrode maintains a remarkable capacitance of 106 F g(-1). At 20, 60, and 100 °C and an extreme current density of 100 A g(-1), there is excellent capacitance retention (72-92%) with the specific capacitances being 113, 144, and 142 F g(-1), respectively. These characteristics favorably place the materials on a Ragone chart providing among the best power-energy characteristics (on an active mass normalized basis) ever reported for an electrochemical capacitor: At a very high power density of 20 kW kg(-1) and 20, 60, and 100 °C, the energy densities are 19, 34, and 40 Wh kg(-1), respectively. Moreover the assembled supercapacitor device yields a maximum energy density of 12 Wh kg(-1), which is higher than that of commercially available supercapacitors. By taking advantage of the complex multilayered structure of a hemp bast fiber precursor, such exquisite carbons were able to be achieved by simple hydrothermal carbonization combined with activation. This novel precursor-synthesis route presents a great potential for facile large-scale production of high-performance carbons for a variety of diverse applications including energy storage.
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) | 0.000 | 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) | 0.001 | 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