Size-structure-property relationship of wood particles in aqueous and dry insulative foams
Bibliographic record
Abstract
Three size-fractionated samples of pine beetle-killed wood particles were used to prepare lightweight insulative foams. The foams were produced by foam-forming an aqueous slurry containing wood particles (125–1000 μm), a polymer binder, and surfactant, followed by oven drying. The effect of wood particle size on the aqueous foam stability, structure, and performance of insulative foams was investigated. While all aqueous foams were highly stable, aqueous foam stability increased with decreasing particle size. For dry foams, the cell size distribution was similar for all particle sizes as it was primarily controlled by the surfactant; differences occurred within the cell wall structure. A size-structure-property relationship was identified using x-ray micro-computed tomography where smaller particles produced lighter cell wall frameworks, leading to lower densities and decreased thermal conductivity and compressive strength. Larger particles produced denser cell wall frameworks that were more resistant to deformation, although all dry foams had sufficient mechanical properties for use as insulation panels. Thermal conductivity for all wood particle size-fractionated samples was <0.047 W m−1 K−1 making the foams similar to expanded polystyrene/polyurethane and supporting their use as thermal insulation in buildings.
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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".