A sustainable approach for developing biocarbon from lignin and its utilization in recycled ocean nylon based biocomposites
Bibliographic record
Abstract
Nylon/polyamide (PA6) is a major cause of ocean plastic pollution because of its extended use in commercial fishing activities. Recovery of this nylon from oceans and its use in manufacturing new materials or composites is urgently required to promote sustainability and circularity. In this work, unlike higher-density mineral fillers, lignin from the forestry industry was converted into biocarbon, which was used as a lightweight filler to manufacture recycled-ocean nylon (RN) based composites. Biocarbon is a highly stable, competitive, and sustainable filler for high-performance engineering plastics such as nylon. Lignin was pyrolyzed at 600°C followed by further treatment at 1200°C (with and without cobalt (II) nitrate catalyst) to induce graphitization in the produced biocarbon. Among the three types of biocarbon samples, such as pyrolyzed at 600°C, 1200°C and 1200°C catalyzed lignin biocarbon, the catalyzed biocarbon showed the maximum electrical conductivity. Catalyzed lignin biocarbon pyrolyzed at 1200°C showed an increase of 85% in electrical conductivity compared to commercial mineral graphite. The biocomposites consisting of 600°C biocarbon were manufactured by injection molding at different filler contents up to 40 wt.%. The biocomposites consisting of 40% of pyrolyzed lignin at 600°C showed increased flexural strength, flexural modulus, and heat deflection temperature by 41, 76 and 76%, respectively, compared to neat RN. Improved properties of the prepared biocarbons and biocomposites showed the potential of RN-based composites in the automotive industries.
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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".