Manufacturing Medium-Density Particleboards from Wood–Bark Mixture and Different Adhesive Systems*
Bibliographic record
Abstract
Abstract The disposal problem of bark residues and the shortage of wood raw material supply in some regions make the use of bark an attractive option for the industry. In this study, medium-density particleboards were manufactured from mixed black spruce bark and spruce-pine-fir wood particles at a weight ratio of 50/50. Different adhesive systems were used as binders for the bark-based panels, including commercial phenol-formaldehyde (PF), urea-formaldehyde (UF), polymeric methylene diphenyl diisocyanate (pMDI) resins, and a laboratory-synthesized lignin-PF resin containing 30 wt% lignin substitution for phenol. The objective was to investigate the suitability of utilizing bark residues in the manufacture of particleboards for the floor underlayment (PBU) application. The resulting boards were evaluated according to the ANSI A208.1‐2009 standard for internal bond (IB), modulus of rupture, modulus of elasticity, hardness, thickness swell, and linear expansion. The test results indicate that all panels made with these resin systems can meet the PBU requirements in terms of IB and hardness and that those bonded with 8 percent PF or 5 percent pMDI can meet all the requirements by the ANSI A208.1‐2009 standard for the floor underlayment application in terms of the properties evaluated. Results of this study imply that it is feasible to use bark as a raw material to manufacture medium-density particleboard for the floor underlayment application.
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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) | 0.001 | 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".