Foamability of all‐green polylactide/rice straw pulp biocomposites through continuous extrusion process: Effects of pulping and reactive compatibilization
Bibliographic record
Abstract
Abstract In the present work, the processability of poly(lactic acid) (PLA) in the extrusion foaming process was improved by using rice straw (RS) as an agricultural waste. In order to extrude a sustainable lightweight foam, the soda‐pulping and bleaching modification were performed on the RS particles, which resulted in the attainment of purified micron‐sized cellulosic fibre with higher aspect ratio. The addition of these fibres, with larger interfacial area between filler and matrix, to the PLA melt enhanced the foam void fraction and cell density more than one order of magnitude. The use of a reactive compatibilizer in the biocomposite showed further beneficial effects on the PLA foamability. The used reactive compatibilizer, with the ability of simultaneous reactions with the end groups of PLA macromolecules and hydroxyl groups of the lignocellulosic pulp, in the biocomposite noticeably improved the viscoelastic properties of melt and lengthened the macromolecule relaxation times. As a result, the biocomposite melt with higher melt strength was obtained, which kept the melt integrity during the bubble growth stage, hindered the cell coalescence, and retained a larger volume of gas inside the melt. Contrary to these influences, the compatibilization activity of this additive weakened the heterogeneous nucleation role of the cellulosic fibres. However, by choosing proper pulp loading, the extrusion of a lightweight biodegradable PLA‐based biocomposite foam can be feasible, which can be used in interior construction applications.
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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".