Sequential Dual‐Curing via Michael Addition and Free Radical Polymerization for Wood Surface Densification
Bibliographic record
Abstract
ABSTRACT The application of sequential dual‐curing systems involving Michael addition and free radical polymerization has shown promise for enhancing polymeric networks' thermal and mechanical properties. This study investigates a dual‐curing approach for the surface densification of wood, a technique that can expand wood applications by increasing surface density and hardness. A two‐step curing process was explored by leveraging the versatility of bio‐based acrylate‐ and malonate‐based formulations. The curing kinetics of this system based on carbon Michael addition followed by photopolymerization (UV)—were analyzed using real‐time Fourier transform infrared spectroscopy and photo‐differential scanning calorimetry. Additionally, polymer properties were evaluated through dynamic mechanical analysis and pendulum hardness experiments. Results for dual‐curing systems revealed superior conversion rates, glass transition temperatures, and crosslinking densities compared to a single‐cured Michael addition system. The study also assessed the effectiveness of various formulations and impregnation procedures, including the use of vacuum pressure, to optimize the densification process. The findings demonstrated that the dual‐curing approach significantly enhances surface hardness, offering a rapid method with the potential for cost‐effectiveness and environmental friendliness in wood densification.
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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 | 0.001 |
| 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".