Surface Chemical Changes of Sugar Maple Wood Induced by Thermo-Hygromechanical (THM) Treatment
Bibliographic record
Abstract
The aim of this study was to investigate the effects of heat and steam on the chemical properties of thermo-hygromechanical (THM)-densified sugar maple wood. The THM densification process was performed at two different temperatures (180 °C and 200 °C) with and without steam. The functional groups, surface chemical composition and internal structure and components of the control and densified samples were investigated using attenuated total reflection Fourier transform infrared (ATR-FTIR), X-ray photoelectron (XPS) spectroscopy and pyrolysis gas chromatography-mass spectrometry (Py-GC/MS). The obtained results suggest that the THM densification treatment resulted in significant chemical changes on the wood surface. The results of the ATR-FTIR spectra confirmed the decomposition of hemicelluloses and the relative increase of cellulose and lignin contents on the wood surface. The Py-GC/MS and XPS results show an increase of the oxygen/carbon atomic (O/C) ratio, which indicated that chemical substances containing oxygenated functionality were formed through the densification process. The densification treatment favored the depolymerization of hemicelluloses and cellulose as indicated by an increased anhydrous sugar (levoglucosan) release during the pyrolysis process. Densification also facilitated the cleavage of the lignin side chains, resulting in increased phenyl units with short chains released during the pyrolysis process.
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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".