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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".