Bibliographic record
Abstract
Abstract Wood‐plastic composites (WPCs) may be one of the most dynamic sectors of the present day plastic industry. Although the technology is not new, there is growing interest in the new design possibilities that this marriage of materials offers. The formulation variations of WPCs that increase wood content offer expansion into other uses, and volume processors must produce faster, better, and cheaper materials. On the other hand, weatherability and life cycle costs are the major factors that restrict the expansion of the field of WPCs. This article gives an overview of the recent literature, covering all aspects of WPC materials and their performance as of today. It focuses on their compositions, that is, thermoplastics and thermosets, wood fiber types, and additives. Furthermore, it includes recent progress and improvements in the WPC production area. The processes (compounding, extrusion, injection molding, and compression molding) used for the manufacture of WPC products are described. The properties (mechanical, physical, and biological) of WPC are also covered. This paper proceeds to take the performance and properties of microcellular‐foamed WPC and nano‐WPC into account. Last, this paper concludes with applications, developments, and future trends of WPCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".