Catalytic heat treatment of wood with exogenous phosphoric acid: Enhancing thermo-chemical modification efficiency and fire resistance
Bibliographic record
Abstract
Heat treatment is an eco-friendly technique for improving wood properties without chemical additives. However, conventional heat treatment (CHT) typically requires high temperatures and long durations, offering limited enhancement in fire resistance. This study investigates an efficiency-enhanced acid-heat treatment (AHT), examining the effects of exogenous phosphoric acid concentration, temperature, and holding time. Results reveal that exogenous acid serves as a catalyst, accelerating wood degradation at 120–180°C and promoting carbonization at 285–325°C. AHT significantly reduces moisture absorption and enhances fire resistance. The mass loss of AHT 0.5–180–1 wood (0.5 mol/L acid pretreated and 180°C heat-treated for 1 h) was 5.67 %, nine times greater than that of CHT 180–2 wood (180°C heat-treated for 2 h), despite a 50 % shorter duration. Increased reducing sugar content during AHT confirms acid-catalyzed hemicellulose degradation, while higher residual mass after pyrolysis suggests improved thermal stability. Under mild AHT conditions (0.05 mol/L, <180°C), mechanical strength was preserved, while the limiting oxygen index increased and smoke density decreased. In contrast, under severe AHT conditions (0.5 mol/L, 180°C), the heat release rate, total heat release, and fire growth index decreased by 35.55 %, 35.57 %, and 56.76 %, respectively, compared to CHT 180–2 . Among influencing factors, acid concentration had the greatest impact on mechanical and fire-resistant properties, followed by temperature and holding time. Overall, mild AHT conditions are recommended to balance fire performance enhancement with structural integrity and energy efficiency. These findings provide insight into AHT mechanisms and demonstrate its potential as a sustainable and effective wood modification approach for enhancing fire performance while minimizing structural compromise. ● Acid-induced heat treatment improving modification efficiency and wood fire resistance was proven. ● Both acid impregnation and acid-heat treatment significantly enhanced the thermal stability of wood. ● The catalytic effect of exogenous phosphoric acid on the wood heat treatment and pyrolysis processes was demonstrated.
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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".