Oriented Carbon Fiber Networks by Design from Renewables for Electrochemical Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide With the explosion of global demands for electrified mobility systems and a surge in rural energy transport mechanisms augmented by the scarcity of key metals, carbon by design has become a transformational pathway to fill the gap as an energy material of choice. The development of functional carbon from renewables with outstanding electrostatic double-layer capacitance is still in its infancy, as there is a significant gap in understanding the relationship between the tunable structure and properties of the bioresources both before and after their controlled carbonization. Herein, we report carbon fiber networks (CFNs) with highly controllable intact structure manufactured from four functional lignins originating from different types of processing residues, demonstrating excellent electrochemical efficacies, which makes them promising self-standing electrodes in supercapacitors. This study also underpins the feasibility and importance of preparing CFNs with highly oriented structure, which endows superior specific capacitance and cycle stability compared to the CFNs with randomly oriented fibers. The randomly oriented CFNs reached a specific capacitance value of 456 F g –1 under current densities of 1 A g –1 and a cycle stability of 73.6%, while the CFNs with an orientation factor of 0.87 exhibited significant improvement of the specific capacitance by approximately 15% (529 F g –1 ) and the cycle stability reached 95% after 10 000 charge–discharge cycles. The high specific capacitance and excellent overall electrochemical properties of the highly oriented CFNs make them a cost-effective and greener material of choice for energy storage devices.
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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) | 0.001 | 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 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".