Fire Performance of Intumescent Waterborne Coatings with Encapsulated APP for Wood Constructions
Bibliographic record
Abstract
In this work, intumescent coatings were prepared for protection of wood from fire. The fire-retardant chemical ammonium polyphosphate (APP) is known to have poor resistance to water and high humidity as it is hygroscopic in nature. To improve the water resistance, durability and fire resistance of the intumescent coating, APP was modified using a hybrid organic-inorganic polysiloxane encapsulation shell prepared by the sol–gel method. The physical and chemical properties of the intumescent mix containing microencapsulated ammonium polyphosphate (EAPP) particles were characterized by X-ray fluorescence (XRF), Fourier transform infrared spectroscopy (FTIR), water absorption, dynamic vapor sorption (DVS) and thermogravimetric analysis (TGA). The EAPP mix showed 50% reduction in water absorption, 75% reduction in water vapor sorption and increased thermal stability when compared to the APP mix. The intumescent coatings were applied on wood samples, and their fire performance was evaluated using a cone calorimeter test. The intumescent coatings containing EAPP mix showed better fire retarding properties with longer time to ignition, lower heat release rate and shorter heat release peak when compared to the coating without EAPP mix. The prepared intumescent coating shows higher resistance to water and moisture, and it has great potential to be used in bio-based construction industry for enhancing the fire resistance of 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 | 0.000 |
| 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 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".