Sustainable biocomposites from pyrolyzed lignin and recycled nylon 6 with enhanced <scp>flame retardant</scp> behavior: Studies on manufacturing and quality performance evaluation
Bibliographic record
Abstract
Abstract The recycled nylon (RN)‐based biocomposites were fabricated by adding 25% lignin biocarbon. Lignin was pyrolyzed at 300, 600, and 900°C to produce Lig300, Lig600, and Lig900 biocarbon (BioC) samples, respectively. Higher functionality of Lig600 (unlike Lig900) allowed for improved interfacial interaction with the polar nylon matrix. Mechanical properties were further enhanced for RN_Lig600 composite with enhanced flexural and tensile strength by 18% and 8%, respectively, compared to neat polymer (RN). RN_Lig900 composite showed enhancement in tensile and flexural modulus by 32.6% and 51.1%, respectively, compared to RN. Incorporation of Lig900 in RN matrix resulted in 77.9% reduction in burning rate compared to RN. These results show the potential of lignin BioC as a filler in RN composites for flame retardant applications and mechanical enhancement, such as in the automotive industry. Highlights Effect of pyrolysis temperatures (300, 600, and 900°C) on lignin biomass. Composites prepared from recycled polyamide 6 from carpet waste and biocarbon. Improved interfacial adhesion of 600°C biocarbon with recycled nylon matrix. Enhanced thermal, mechanical properties, reduced flammability of biocomposites. Sustainable biocomposites with 900°C biocarbon reduced burning rate by 78%.
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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".