NEW WOOD FIBER BIOCOMPOSITES BASED ON POLYLACTIDE AND POLYLACTIDE / THERMOPLASTIC STARCH BLENDS
Bibliographic record
Abstract
This paper aims at developing natural fiber biocomposites based on biopolymers reinforced with wood fibers obtained from a thermo-mechanical refining process. Polylactide (PLA) and polylactide/thermoplastic starch blends (PLA/TPS) were used as matrices. Two grades of PLA, an amorphous and a semi-crystalline one, were considered with the purpose to investigate the effect of wood fibers on the crystallinity, and therefore, on mechanical properties of composites. Two grades of thermoplastic starch (TPS), different in plasticizer content and nature, were used. TPS content in the PLA/TPS blends was 50%. Moreover, two wood fiber types were selected, a hardwood (HW) and a softwood (SW), to state the effect of the fiber type on the biocomposite properties. Finally, the impact of different additives on biocomposite properties was studied with the purpose to enhance the biopolymer/wood fiber affinity with the best impact on the final mechanical performance. The biocomposites containing 30% by weight of wood fibers were obtained by co-rotating twinscrew extrusion. The properties are described in terms of morphology, thermal, rheological, and mechanical properties. With respect to wood type, there was almost no differentiation between reinforce ability of SW and HW for the studied formulations. Similar observations were done regarding the two grades of PLA on the mechanical properties (i.e. tensile strength and elastic modulus) of biocomposites. Concerning the thermal properties, semi-crystalline PLA biocomposites shown an increase in crystallization kinetics and a decrease of the fully crystallinity due to the wood fibers presence. A twofold increase in elastic modulus and an increment in tensile strength of 11% were observed for uncompatibilized PLA/wood fiber composites. Despite good tensile results and an inherent affinity between PLA and wood fibers, further improvement was reached by increasing the adhesion in adding coupling agents and by preserving the PLA molecular weight using a branching agent. The best mechanical results were achieved for composites containing the branching agent, most probably due to the branching of the PLA chains. PLA/wood fiber biocomposites present higher mechanical properties than polypropylene counterpart and are very promising candidates for many applications, especially in construction interior 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 | 0.001 | 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".