Comparison between polyethylene glycol and tributyl citrate to modify the properties of wood fiber/polylactic acid biocomposites
Bibliographic record
Abstract
In this work, polyethylene glycol (PEG) and tributyl citrate (TBC) are proposed to modify the properties of wood fiber (WF, 20 wt%)‐reinforced polylactide (PLA, 80 wt%) biocomposites. The reinforcing and toughening effects of these additives were systematically investigated by comparing their mechanical, thermal and rheological properties. It was found that 5 wt% TBC improved the compatibility between WF and PLA and resulted in increased tensile strength (15%) and thermal stability (4%) of the biocomposites compared with those of unmodified biocomposites. The glass transition temperature, melting temperature, and crystallinity of TBC‐modified biocomposites were lower than those of PEG biocomposites. In addition, the storage modulus, loss modulus and complex viscosity of TBC‐modified biocomposites were effectively improved at low TBC content (5–10 wt%), whereas reduced properties were obtained when the same PEG content was added. As wettability is always a problem in biocomposites, the contact angle was not changed with TBC content (up to 20 wt%), and a linear decrease was observed with a PEG content up to 30 wt%. POLYM. COMPOS., 40:1384–1394, 2019. © 2018 Society of Plastics Engineers
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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) | 0.001 | 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) | 0.000 | 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 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".