An Innovative Approach for Enhancing Wood Heat Treatment Efficiency: Exploring the Mechanistic Influence of Exogenous Phosphoric Acid on Thermal-Induced Discoloration and Dimensional Stability of Poplar Wood
Bibliographic record
Abstract
Conventional heat treatment is an industrialized and environmentally friendly wood modification method but is considered a self-catalysis-reacted modification and energy-intensive consumption process. This research uses exogenous phosphoric acid (EPA) to facilitate the thermal reaction of wood and thoroughly investigates the catalytic mechanism of acid treatment during the heat treatment process. Poplar wood was first impregnated with a 0.05 and 0.5 mol/L EPA solution and then subjected to 120∼180 °C for 1 and 2 h. The results revealed that EPA enlarged the reaction of wood to heat, acid-heat-treated (AHT) wood’s color deepened, and dimensional stability enhancement was superior to conventional heat-treated (CHT) wood. The antiswelling rate of the AHT wood (AHT 0.05–180–2 and AHT 0.5–180–2 ) was 2.72- and 4.26-fold compared to CHT 180–2, and the color difference was 8.8% and 614.2% higher. The AHT 0.5–120–1 wood’s antiswelling rate reached the same level as that of CHT 180–2, with a 1/3 reduction of treatment temperature and a 50% decline in holding time. Pyrolysis kinetics analysis revealed that the hemicellulose’s activation energy ( E a ) declined, whereas the E a of lignin changed according to EPA concentration. Interestingly, the EPA increased the E a of wood and cellulose because of its carbonization effect at a higher temperature, which endowed a potential fire resistance capacity to the AHT wood. EPA reduced the initial thermal degradation temperature of wood and wood components, providing an efficient way to induce discoloration and improve wood dimensional stability. This novel modification technique requires less energy consumption and endows multifunctionality in wood. The outcomes offer a new sustainable and high-efficiency chemical combined thermal processing technology for wood.
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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 | 0.001 |
| 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".