Facile Preparation of Self-Standing Hierarchical Porous Nitrogen-Doped Carbon Fibers for Supercapacitors from Plant Protein–Lignin Electrospun Fibers
Why this work is in the frame
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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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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) | 0.000 | 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