Effect of fire retardants on mechanical properties of a green bio‐epoxy composite
Bibliographic record
Abstract
ABSTRACT To improve fire retardant behavior of bio‐epoxy resin, composites were prepared with three fire retardants (FRs); ammonium polyphosphate, aluminum trihydrate, and magnesium hydroxide. Fractured surfaces of prepared composites were observed with the scanning electron microscope (SEM). Tensile strength and Charpy toughness were evaluated and analyzed statistically using analysis of variance (ANOVA). Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to determine the thermal stability. SEM analysis results revealed fractured surfaces were altered with addition of FRs. Adding FRs can be regarded as a decrease in tensile strength and toughness complemented by improved stiffness. ANOVA analysis showed FR/bio‐resin composites have a statistically significant loss in tensile strength, stiffness, elongation, and Charpy toughness. The DSC results showed the glass transition temperature was not affected significantly by adding FRs and ranged from 66 to 69 °C. TGA showed the initial, midway, and maximum decomposition temperatures for composites and their ability to form improved ash yields compared to pure resin. Aluminum trihydrate and Mg(OH)2 had higher T50 suggesting an increase in thermal stability compared to pure bio‐epoxy. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47398.
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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".