Towards a New Plastination Technique for Moisture Management of Western Red Cedar Without Loss of Strength and with Enhanced Stability
Bibliographic record
Abstract
Amidst environmental concerns regarding the use of petroleum-based materials, wood and wood-based products are among the key players in the pursuit of green construction practices. However, environmental degradation of these materials remains a concern during structural design, particularly for outdoor applications. Borrowed from anatomy to preserve human body parts, this study applies and assesses a technique called 'plastination' as a new means for moisture management of Western Red Cedar (WRC). Specifically, the proposed technique includes acetone dehydration of WRC, followed by SS-151 silicone vacuum-assisted impregnation and silicone curing. To evaluate the method's effectiveness, Micro X-ray Computed Tomography (μCT), Fourier Transform Infrared (FTIR) Spectroscopy, Thermogravimetric Analysis (TGA), and static water contact angle measurements were employed. Tensile testing was also performed to quantify the treatment's effect on WRC's mechanical properties under moisture conditioning. μCT confirmed an impregnation depth of 21.5%, while FTIR and TGA results showed reduced moisture retention (3.6 wt%) in plastinated WRC due to the absence of hydroxyl groups. Mechanical testing revealed enhanced deformability in treated samples without compromising tensile strength. Upon moisture conditioning, plastinated WRC retained its tensile properties and showed 59% lower moisture absorption and 15% lower weight as compared to conditioned virgin samples.
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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".