Critical processing temperature in the manufacture of fine‐celled plastic/wood‐fiber composite foams
Bibliographic record
Abstract
Abstract The main benefits of incorporating wood fibers (WF) in plastics are the increased stiffness and lowered cost of the resulting composites. However, these improvements are usually accompanied by a reduction in the ductility and impact resistance. These shortcomings can be removed by effectively foaming and incorporating a fine‐celled structure in these composites. The volatiles released from WF during processing are known to deteriorate the cell structure. The maximum processing temperature, which affects the amount of volatiles released by the WF during extrusion of fine‐celled plastic/WF composite, affects the cell morphology. This study was undertaken to identify the critical temperature above which the cellular structure of WF composite foams is significantly deteriorated. To clearly identify the effects of the volatiles generated from WF on the cellular morphology, neither a chemical blowing agent nor a physical blowing agent was used in the foam processing. The experimental results show that regardless of the drying method, the highest processing temperature of plastic/WF composites should be minimized, preferably below 170°C, to avoid the adverse effects of the volatiles generated from the WF during processing. A method of estimating the emissions from WF during extrusion processing by using the TGA data is also proposed. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 621–629, 2004
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".