Thermo-Mechanical Properties of a Wood Fiber Insulation Board Using a Bio-Based Adhesive as a Binder
Bibliographic record
Abstract
The goal of the present study was to develop a low-density thermal insulation board using wood fibers and a bio-based adhesive as a binder, which was prepared from a crude glycerol and citric acid mixture. The physical and mechanical properties of insulation boards manufactured using two ratios of crude glycerol and citric acid (1:0.66 and 1:1 mol/mol) and two adhesive contents (14% and 20%) were evaluated. The results show that the insulation boards with a range of density between 332 to 338 kg m−3 present thermal conductivity values between 0.064 W/m-K and 0.066 W/m-K. The effect of adhesive content was very significant for certain mechanical properties (tensile strength perpendicular to surface and compressive strength). The tensile strength (internal bond) increased between 20% and 36% with the increased adhesive content. In contrast, the compressive strength decreased between 7% and 15%. The thermo-mechanical properties obtained of insulation boards such as thermal conductivity, traverse strength, tensile strength parallel and perpendicular to surface, and compressive strength are in accordance with the requirements of the American Society for Testing and Materials C208-12 standard for different uses. The results confirm the potential of crude glycerol and citric acid mixture to be used as an adhesive in the wood fiber insulation boards’ manufacturing for sustainability purposes.
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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) | 0.000 | 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 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".