Fabrication and evaluation of acrylated epoxidized castor oil‐toughened diglycidyl ether of bisphenol A nanocomposites
Bibliographic record
Abstract
Abstract Castor oil‐based epoxy monomer was prepared using a two‐step approach: epoxidation of castor oil, followed by acrylation of epoxidized castor oil. A bio‐based diglycidyl ether of bisphenol A (DGEBA) epoxy nanocomposites was prepared by the incorporation of organo‐modified montmorillonite (OMMT) clay to the DGEBA/AECO system. The cured bio‐based DGEBA epoxy nanocomposites at 0.80:0.2:0.001 g/g exhibited higher tensile strength (56 MPa), tensile modulus (1933 MPa), flexural strength (132 MPa), flexural modulus (2518 MPa), elongation (23.1 %), and impact strength (34 kJ/m2). The nanocomposites at 0.6:0.4:0.001 g/g can easily bend up to 360° without any damage; similarly, the 0.8:0.2:0.001 g/g system can bend up to 180°. Thermal behaviour of the bio‐based epoxy systems was characterized by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and dynamic mechanical analysis (DMA). The obtained nanocomposites displayed a higher char yield (8.9 %) at 700 °C and glass transition temperature (108 °C) than the DGEBA/AECO systems. Further SEM analysis was used to study the morphological changes in the fractured surfaces of bio‐based epoxy systems, which reveals that crack propagation decreases with addition of AECO to the DGEBA epoxy system.
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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) | 0.001 | 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".