Effect of thermal treatment on the chemical composition and mechanical properties of birch and aspen
Bibliographic record
Abstract
The high temperature treatment of wood is one of the alternatives to chemical treatment. During this process, the wood is heated to higher temperatures than those of conventional drying. The wood structure changes due to decomposition of hemicelluloses, ramification of lignin, and crystallization of cellulose. The wood becomes less hygroscopic. These changes improve the dimensional stability of wood, increase its resistance to micro-organisms, darken its color, and modify its hardness. However, wood also might loose some of its elasticity. Consequently, the heat treatment conditions have to be optimized. Therefore, it is important to understand the transformation of the chemical structure of wood caused by the treatment. In this study, the modification of the surface composition of the wood was followed with Fourier transform infrared spectroscopy (FTIR) and inverse gas chromatography (IGC) under different experimental conditions. The effect of maximum treatment temperatures on the chemical composition of Canadian birch and aspen as well as the correlations between their chemical transformation and different mechanical properties are presented. FTIR analysis results showed that the heat treatment affected the chemical composition of birch more compared to that of aspen. The results of IGC tests illustrated that the surfaces of the aspen and birch became more basic with heat treatment. The mechanical properties were affected by degradation of hemicellulose, ramification of lignin and cellulose crystallization.
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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 | 0.001 | 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".