Novel bio-nanocomposite hybrids made from polylactide/nanoclay nanocomposites and short flax fibers
Bibliographic record
Abstract
This article aims to formulations and properties of novel hybrid biomaterials containing unique four-phase combinations of polylactide (PLA), nanoclays, flax fibers, and coupling agents. A PLA-grafted maleic anhydride (PLA- g-MA) masterbatch containing 10 wt% PLA- g-MA was obtained by reactive extrusion and was further used, after dilution, as a coupling agent. In addition, three PLA masterbatches containing 10 wt% of three different grades of nanoclays, one untreated nanoclay and two organonanoclays, were also compounded. In a subsequent extrusion step, the nanoclay masterbatches were diluted in PLA down to 4 and 2 wt% while simultaneously incorporating in each one 20 wt% of short flax fibers. Those bio-nanocomposites were compounded without and with an equivalent content of PLA- g-MA, that is, with 4 and 2 wt%, respectively, through the dilution of 10 wt% PLA- g-MA masterbatch. The effects of the nanoclay chemistries, PLA- g-MA, and of flax fibers presence on the properties of bio-nanocomposite hybrid materials were investigated. X-ray diffraction, scanning electron microscopy, transmission electron microscopy, rheology, mechanical properties (tension, flexural, and Izod impact), and reprocess ability tests were used to characterize the bio-nanocomposite hybrid materials. In a second step, PLA- g-MA was replaced by an epoxy/styrene/acrylic copolymer for comparison purpose of their respective effect in bio-nanocomposite performances. Mechanical properties of bio-nanocomposites containing the second coupling agent were also evaluated. The effect of the epoxy/styrene/acrylic copolymer is discussed in comparison with the effect of PLA- g-MA.
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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".