The impact of vacuum pressure on the effectiveness of SiO2 impregnation of spruce wood
Bibliographic record
Abstract
Abstract Wood is a widely used construction material that has many advantageous properties. However, it suffers from weaknesses such as low-dimensional stability and low durability in humid environments. These issues are associated with the porous vascular structure of wood that leads to a high water uptake capacity. This research aims to reduce the water uptake capacity of spruce wood by dip-coating samples in an aqueous colloid of silicon dioxide (SiO 2 ) nanoparticles. SiO 2 is a dense ceramic material with good chemical stability. It is readily available and affordable, making it an excellent candidate for this application. This study investigates the effect of SiO 2 impregnation on the physico-mechanical properties of spruce wood. Density measurements, water uptake tests, microscopy examination, thermogravimetric analysis, and dynamic mechanical analysis were conducted on non-treated and SiO 2 -treated spruce wood samples. Quantitative and qualitative analyses demonstrated that SiO 2 impregnation performed under higher vacuum pressure was more effective compared to the atmospheric condition and exhibited a greater presence of SiO 2 in the wood’s vascular system. SiO 2 impregnation under vacuum pressure demonstrated an effective increase in the density of the wood. It also reduced the porosity, which led to a significant reduction in the water uptake of the spruce wood. The analysis of the wood viscoelastic properties revealed that SiO 2 impregnation under atmospheric and vacuum conditions triggered two different reinforcing mechanisms. The results showed that a significant improvement of the spruce wood storage and loss moduli could be achieved when impregnation was performed at the highest vacuum pressure of − 90 kPa.
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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".