Evolution of Extractive Composition During Thermal Treatment of Jack Pine
Bibliographic record
Abstract
Abstract The thermal treatment of wood has many benefits such as better dimensional stability and attractive dark color and does not use toxic chemicals. The resistance against biological decay can be improved when wood is not in contact with ground. On the other hand, after thermal transformation, wood becomes more fragile. The changes of the wood properties are related to the modification of the wood composition. During the thermal treatment, the evaporation of the moisture content is not the only event. Volatile extractives are evacuated from the wood, while new products and by-products of different chemical reactions appear. The comparison of the extracts obtained from untreated and treated wood can help to identify thermo-chemical reactions, taking place during the heat treatment. This article presents the analysis by Gas Chromatography–Mass Spectroscopy (GC-MS), High Performance Liquid Chromatography (HPLC), and Thin Layer Chromatography (TLC) of polar and non-polar extracts of untreated and heat-treated North American Jack pine (Pinus banksiana). The study of the impact of maximum heat treatment temperature on the composition of the Jack pine extracts showed that the major part of extractives leaves the wood under 200°C whereas most of the new products appear only above 200°C. While the extractives of the untreated Jack pine are dominated by non-polar components, the thermo-transformation seems to generate mainly polar compounds. However, presence of water vapor increases the portion of polar extractives in wood. Interestingly, an important decrease of concentration of phenolic compounds (such as pinosylvin, pinosylvin monomethyl ether, and pinobanksin) in Jack pine wood was observed between 160–200°C. On the other hand, 4-hydroxy-methylfurfural and vanillin have been identified as compounds generated by the heat treatment above 200°C. The identification of other by-products will be presented in a later paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".