Optimization of high‐quality carbon fiber production from electrospun aligned lignin fibers
Bibliographic record
Abstract
Abstract In recent years, investigating lignin as an alternative carbon fiber precursor has received immense research attention as a way to reduce the cost and replace the unsustainable conventional petroleum‐based precursors for carbon fiber production. The predominant challenge for lignin‐based carbon fibers is its low mechanical performance compared to conventional ones. In this work, mechanical properties of electrospun lignin carbon fiber mats were shown to be considerably enhanced via alignment of the submicron fibers. Over 60% of the fibers were aligned via a rotating drum collector utilized during fiber production. The main electrospinning parameters, namely electric field, rotating speed, and flow rate were optimized with the Box–Behnken method to enhance mechanical properties with reduced fiber diameter and improved fiber alignment. The optimal electrospinning process parameter was achieved at 2000 rpm collection speed, 80 kV/m electric field, and 440 nl/s flow rate. The lignin carbon fibers produced under the optimized condition exhibited elastic modulus of 3145.47 ± 917.75 MPa, tensile strength of 18.78 ± 6.11 MPa, and average fiber diameter of 697.07 ± 96.41 nm. The analysis on the interactions between electrospinning parameters has laid a solid foundation for the production of high‐quality carbon fibers from lignin precursor.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".