Facile Preparation of Self-Standing Hierarchical Porous Nitrogen-Doped Carbon Fibers for Supercapacitors from Plant Protein–Lignin Electrospun Fibers
Bibliographic record
Abstract
This research aims to develop self-standing nitrogen-doped carbon fiber networks from plant protein-lignin electrospun fibrous mats for supercapacitors. The challenge in preparing carbon fiber from protein is to maintain a fibrous structure during carbonization process. Thus, lignin was incorporated with protein. At protein-to-lignin ratio of 50:50 to 20:80, the electrospun fibers maintained their structure after carbonization and formed self-standing carbon fiber mats. Stacked graphene layer structure was developed in the carbon fibers at a relatively low carbonization temperature (<1000 °C) without the use of catalysts, which might be derived from both lignin and protein. Graphene layer structure conferred the carbon fibers with superior conductivity. The optimized carbon fiber networks possessed excellent capacitance performance in 6 M KOH of 410 F/g at 1 A/g and good cyclic stability. Such good electrochemical properties were due to the well-engineered characteristics of the materials, including a hierarchical porous texture, heteroatoms (nitrogen and oxygen), and the stacked graphene layer structure. This research not only has provided a convenient way to develop carbon fibers from plant protein-lignin for N-doped supercapacitor electrodes, but also opportunity to add value to plant proteins and lignin as by-products of agricultural industry processing.
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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".