Quantitative characterization of chemical degradation of heat‐treated wood surfaces during artificial weathering using XPS
Bibliographic record
Abstract
The X‐ray photoelectron spectroscopy (XPS) study of three heat‐treated North American wood species (jack pine, birch and aspen) was carried out to evaluate chemical modifications occurring on the wood surface during artificial weathering for different times. The results suggest that the weathering reduces lignin content (aromatic rings) at the surface of heat‐treated wood, consequently, the carbohydrates content increases. This results in surfaces richer in cellulose and poorer in lignin. Heat‐treated wood surfaces become acidic due to weathering, and the acidity increases as the weathering time increases. Three possible reasons are given to account for the increase of acidity during weathering. The lignin content increases, whereas the hemicelluloses content decrease due to heat treatment. Heat‐treated woods have lower acidity to basicity ratios than the corresponding untreated woods for all three species because of the decrease in carboxylic acid functions mainly present in hemicelluloses. The wood composition changes induced by weathering are more significant compared to those induced by heat treatment at wood surface. Exposure to higher temperatures causes more degradation of hemicelluloses, and this characteristic is maintained during weathering. However, the wood direction has more effect on chemical composition modification during weathering compared to that of heat treatment temperature. The heat‐treated jack pine is affected most by weathering followed by heat‐treated aspen and birch. This is related to differences in content and structure of lignin of softwood and hardwood. The use of XPS technique has proved to be a reliable method for wood surface studies. Copyright © 2012 John Wiley & Sons, Ltd.
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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".