Hydrophobic Modification of Ammonium Polyphosphate and Its Application in Flame Retardant Polypropylene Composites
Bibliographic record
Abstract
Ammonium polyphosphate(APP) surface was modified with(3-aminopropyl) triethoxysilane(KH550),then microencapsulated by silicone in situ polymerization of tetraethyl orthosilicate(TEOS),finally surface-modified with(heptadecafluoro-1,1,2,2-tetradecyl) trimethoxysilane to prepare the hydrophobic APP(M-APP).The structure and surface elemental composition of M-APP were characterized by Fourier transform infrared spectroscopy(FTIR) and X-ray photoelectron spectroscopy(XPS),and the results indicated that M-APP was the target product.The wettability of the modified APP was measured by water contact angle(CA)test and the CA reached 134°,which demonstrate the modified APP possessed obviously hydrophobic property.The modified APP mixed with triazine char foaming agent(CFA) with the mass ratio of 4∶ 1 to prepare intumescent flame retardant(IFR) and afterwards IFR was incorporated into polypropylene(PP) to prepare flame retardant PP composites.The flame retardancy and thermal degradation behavior of PP composites were evaluated by limiting oxygen index(LOI),vertical burning(UL-94) and thermal gravimetric analysis(TGA)tests.The water resistant property of the flame retarded PP materials were evaluated by water resistant test,and the mechanical properties were measured by the tensile,flexural and impact strength test,the compatibility between the flame retardant and polymer was investigated by scanning electron microscopy(SEM).The results indicated that the modified APP/CFA/PP composite reached UL-94 V-0 flammability rating when the loading amount of IFR was 23%,the PP composite after water resistance test still successfully passed UL-94V-0 rating and the mass loss was only 0.92%.Under the same experimental conditions,the APP/CFA/PP composite after water resistance test was no rating in vertical burning test and the mass loss was 2.45%.It can be deduced that hydrophobic modification of APP can greatly improve the water resistance of the flame retardant PP material.The TGA results indicated that the incorporation of IFR into PP matrix improved the thermal stability and char-forming ability of PP composite,the formed intumescent char layer during combustion protect the underlying matrix from further combustion and degradation,consequently enhanced the flame retardancy of PP materials.The results of SEM and mechanical test revealed that the modification of APP improved the compatibility between the flame retardant and PP matrix,which resulting in the increase of the mechanical properties of the flame retarded PP composites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".