Bio‐cleaned lignin‐based carbon fiber and its application in adsorptive water treatment
Bibliographic record
Abstract
Abstract Although an agricultural byproduct, lignin can be a felicitous choice serving as a carbon fiber precursor upon bio‐cleaning with Pseudomonas fluorescence. In this study, carbon fiber produced from electrospun bio‐cleaned lignin (Bio‐KLB) was demonstrated to be a novel efficient adsorbent for methylene blue in wastewater treatment. Bio‐cleaning effectively changed lignin from un‐electrospinnable to easily‐electrospinnable by removing impurities. Bio‐KLB carbon fiber mats showed average fiber diameter of 278.95 ± 49.89 nm, and randomly dispersed fiber mats showed average elastic modulus of 1532.87 ± 439.63 MPa and tensile strength of 16.72 ± 5.21 MPa. Adsorption of methylene blue on Bio‐KLB carbon fibers was analyzed at pH = 9, where optimal adsorption and decent reusability were observed. Kinetic adsorption was fitted well with pseudo‐first‐order model while adsorption isotherms were fitted to both Langmuir and Freundlich models. The highest adsorption capacity was 548.65 mg/g based on Langmuir model. The results provide clear evidence that electrospun bio‐cleaned lignin‐based carbon fibers can be a strong alternative material for dye treatment.
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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) | 0.001 | 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".