Novel polylactide/triticale straw biocomposites: Processing, formulation, and properties
Bibliographic record
Abstract
This article aims to the development of polylactide (PLA)/triticale straw biocomposites with focus on the relationship between triticale straw content, additive presence, processing, and final properties. Prior to melt compounding, the triticale straw used in this study was chopped using the paper process to produce triticale particles that were further pelletized to assure a consistent feed rate into the extrusion line. PLA/triticale straw biocomposites were obtained for different triticale contents from 10 up to 40%vol, without and with maleic anhydride grafted polylactide (PLA‐g‐MA) as a coupling agent. As a supplementary additive, a PLA‐specific branching agent was used in some selected formulations to minimize the reduction in PLA's molecular weight. The biocomposites were characterized in terms of rheology, thermal properties, morphology, mechanical properties (tensile, flexural, and impact), and recyclability. The PLA‐g‐MA increased the tensile strength of biocomposites by 10%, whereas boosted the tensile modulus about 2.5 times at 40%vol triticale content. For the same formulation, the flexural strength was raised by 15% and flexural modulus was doubled. However, a combination of PLA‐g‐MA and branching agent proved to be the best approach to enhance PLA/triticale straw mechanical properties. When 20%vol of triticale was used as reinforcement, the presence of branching agent increased the flexural strength about 25%. The results demonstrate that the triticale straw processed in this way could offer a similar reinforcement capability as the cellulosic fibers based on the agricultural and forestry resources and can be easily recycled without losing its mechanical properties. It has a good potential in the biocomposites field with promising applications in construction, common goods, and transportation industries. POLYM. ENG. SCI., 54:446–458, 2014. © 2013 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) | 0.001 | 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".