Processing, Carbonization, and Characterization of Lignin Based Electrospun Carbon Fibers: A Review
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".