Bark particleboard: pressing time, particle geometry and melamine overlay
Bibliographic record
Abstract
Previous research has shown the possibility of making particleboards from bark residues. The objective of this study is to demonstrate the possibility of making black spruce (Picea mariana) bark particleboards with a short curing time in the pressing schedule, and to assess the effect of bark particle geometry on the mechanical and physical properties of bark particleboards. Additionally, melamine films were applied on bark composite boards and assessed. Mechanical and physical properties such as internal bond, modulus of elasticity, modulus of rupture, linear expansion and thickness swell were evaluated from five to two minutes curing time with a one-minute step. Mechanical and physical properties were also evaluated for four particle combinations. Different particles were used to qualify the effect of particle geometry. Combinations were made from two types of bark particles and two types of wood particles. These particles are hammermilled bark particles, refined bark, wood particles, and wood fibres (MDF). Melamine overlay was applied on the particleboard made with these four particle combinations to evaluate the behaviour of the film with the presence of bark in the composite. Good mechanical properties were obtained with a four-minute curing time. The use of wood fibres such as fibres from the MDF industry instead of wood particles, as in the particleboard industry, helped to reduce the linear expansion results. None of the particleboards produced in this study were able to meet ANSI standard requirements for linear expansion. The pressing of white melamine overlay results in a different colour according to the colorimeter results when it is applied on a bark particleboard compared to conventional particleboards. No differences were observed when a black film was applied. Key words: black spruce bark, bark particleboards, bark particle geometry, particleboard laminations, value-added wood products
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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 | 0.001 |
| 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".