Preparation and Mechanical Properties of Alkali-Treated Wood Flour/Dynamic Polyurethane Composites
Bibliographic record
Abstract
In this study, alkali-treated wood flour/dynamic polyurethane composites were successfully prepared through a solvent-free one-pot method and in situ polymerization. The effects of the alkaline treatment process, changes in the flexible long-chain content in the dynamic polyurethane system, and the wood flour filling amount on the interface's bonding, mechanical, and reprocessing properties were investigated. Partial removal of lignin and hemicellulose from the alkali-treated wood flour enhanced rigidity and improved interface bonding and mechanical strength when combined with dynamic polyurethane. The tensile strength was improved from 5.65-11.00 MPa to 13.08-23.53 MPa. As the composite matrix, dynamic polyurethane could not easily infiltrate all wood flour particles when its content was low or its fluidity was poor. Conversely, excessive content or overly high fluidity led to leakage and the formation of large pores, affecting the mechanical strength. As the polyol content increased, the matrix exhibited greater fluidity, which enabled it to accommodate more wood flour and penetrate the cell cavity or even the cell wall. This improved infiltration enhanced the interface bonding performance of the composites and made their mechanical properties sensitive to changes in wood flour content. The reprocessing ability of the prepared composites decreased with the increase in wood flour content, and the interface bonding was enhanced after reprocessing. The tensile strength retention rate of the composites prepared with alkali-treated wood flour was lower. This study provides a theoretical basis for optimizing the performance of wood fiber/dynamic polyurethane composites and an exploration path for developing self-healing and recyclable wood-plastic composites, which can be applied to building materials, automotive interiors, furniture manufacturing, and other fields.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".