Effects of Kraft lignin and corn cob agro-residue on the properties of injected-moulded biocomposites
Bibliographic record
Abstract
Lignocellulosic by-products are frequently disposed by means of combustion. This study investigates an alternative route for corn cob and Kraft lignin resources in order to support circular economy . The respective plant-based fibres and filler were compounded for the first time together with a poly(lactic acid) (PLA) matrix. Consecutively, seven different biocomposites were processed by injection-moulding and further characterized. The biocomposite containing a mixture of Kraft lignin and corn cob (12 wt% in total) exhibited the highest flexural strength (84 MPa). A proper wetting of PLA onto the corn cob particles demonstrated a good compatibility at matrix/fibre interface. PLA molecular structure changed in presence of 20 wt% lignin filler, with effect on the glass transition temperature and on the composite mechanical strength . The fibres moderately influenced composites surface tension, while Kraft lignin contributed to a slight increase of surface hydrophobicity . Surface energy (σsTotal) of composites have been estimated at 27.6, 28.7 and 27.8 mN/m for PLA/KL-20, PLA/CC-10 and PLA/KL-15/CC-5 respectively. While the polar component (σsPolar) have been estimated at 17.8, 20.0 and 18.7 mN/m for PLA/KL-20, PLA/CC-10 and PLA/KL-15/CC-5 respectively. Unlike the PLA/corn cob composite, those containing Kraft lignin were entirely biodegraded within 2 months in industrial composting conditions study. The materials could be utilized for end-use products thanks to their good mechanical and thermal properties. By adding wood-lignin and corn by-products, materials cost and carbon footprint shall decrease in comparison to pure PLA, while being a biodegradable and sustainable replacement of polyolefins .
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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".