Thermoforming of polymer from monomers in wood porous structure and characterisation for wood–polymer composite
Bibliographic record
Abstract
Inspired by the porous structure of wood, a novel bio‐based composite, wood–polymer composite, was fabricated by thermoforming polymer from monomers [methyl methacrylate (MMA) and styrene (St)] in situ in the wood’s porous structure through a catalyst thermal treatment. SEM observation indicated that polymer was generated in situ and satisfactorily filled up wood pores without noticeable lacunae. FTIR analysis suggested that MMA and St copolymerised in the wood pores, and the resultant polymer was grafted onto the wood matrix through the reaction of ester group of MMA and hydroxyl group on wood components, achieving a chemical complex, which is in agreement with SEM observations. DMA analysis showed that the graft of copolymer of MMA and St onto wood improved the interface interaction between wood matrix and polymer, which rendered both the glass transition temperature and storage modulus at normal temperature of wood–P(MMA‐co‐St) composite evidently increased. The mechanical properties of wood–P(MMA‐co‐St) composite including modulus of rupture, compressive strength, wearability and hardness were improved by 53, 42, 74 and 198% compared with those of untreated wood respectively. And there were liner positive correlation between compression strength and content of polymer loading, as well as hardness and content of polymer loading respectively. Such composite combing both advantages of wood and polymer, as well as making full use of renewable resource, may be capable of becoming a promising material which can be widely used in fields of construction, traffic, furniture and so forth.
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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".