Processing, Carbonization, and Characterization of Lignin Based Electrospun Carbon Fibers: A Review
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
Greenhouse gas emissions and environmental impacts of petroleum-based fuels and materials have necessitated the development of renewable resource-based alternatives. In the process to extract cellulose for converting it to bioethanol (bio-based gasoline) large quantities of lignin are produced as the main byproduct-making it useful for further processing application for sustainable materials. Lignin is the second abundant source of renewable carbon with an aromatic structure which makes it a potential candidate for carbon fiber production. Since lignin can be dissolved in a variety of organic and non-organic solvents, electrospinning has been used to produce precursor fibers for carbon nanofiber production. These carbon nanofibers have been tested as a potentially sustainable alternative for the current non-renewable electrodes in energy storage and conversion devices such as supercapacitors. Using lignin by-products from the fuel energy sector in making devices for electrical energy sector provides a great opportunity for promoting a circular economy from sustainable materials while also contributing to researching alternative sustainable materials in light of a global pandemic. This review presents a summary of the processing conditions for electrospinning different varieties of lignin, characterization of the electrospun fibers and the carbonization conditions for converting fibers. Different techniques that of the structural properties of the precursor fibers, characteristics of carbon nanofibers and their performance in energy storage devices are discussed. Compared to the other published reviews in this field, this review aims to present the current knowledge on material-processing-lignin-carbon fibers properties relationship.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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