Optimising Nanoparticle Dispersion Time for Enhanced Thermomechanical Properties in DGEBA-Based Shape Memory Polymer Composites
Bibliographic record
Abstract
Shape-memory polymers (SMPs) are smart materials that can change shape upon an external stimulus. This phenomenon is called the shape memory effect (SME), which is caused by entropy change due to rapid molecular motion in the polymer segments. Due to the inherently weak thermomechanical properties, use of SMPs is limited in many engineering applications. Therefore, SMPs are often reinforced with fibres and nanoparticles (NPs). NPs offered greater flexibility due to their superior physical, chemical, electrical, mechanical, and thermal properties. However, the homogeneous distribution of NPs is crucial for composition’s stability and enhancement of the base material’s properties. Among the different techniques used for dispersing NPs, ultrasonic irradiation has shown excellent emulsifying and crushing performance. The sonication process is essential for mitigating agglomerates; however, prolonged sonication time probably increases epoxy temperature, micro-bubbles, cavitation, breaking apart molecules and finally degrading the epoxy resin performances. This paper provides critical insight of nanoparticle dispersion into diglycidyl ether of bisphenol A epoxies (DGEBA). DGEBA epoxy resin was added to TiO2 NPs and sonicated for 60 min with 5 min intervals while the temperature of epoxy was maintained below 60oC by using a water cooling throughout the sonication process. The process parameters such as amplitude, mode, epoxy volume and the weight percentage of NPs were kept constant. After each sonication step, Fourier-transform infrared spectroscopy (FTIR) was performed using Thermo Scientific™ and analysed through OMNIC™ Professional quantitation software. In accordance with FTIR results, until 30 min of the sonication, DGEBA resin was not degraded. In order to confirm the performances and the reinforcing effect of NPs, thermo-mechanical and shape memory properties were compared with the neat specimen. The outcomes of this research have suggested quick guidance to find optimum NP dispersion time for DGEBA resins, which has been hardly studied before.
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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".