Aspects of nanoparticles dispersion and interaction in polymer nanocomposites
Bibliographic record
Abstract
Polymer nanocomposites have attracted a great deal of attention during the past few decades. Benefiting from the nanoscale geometry, immense surface-to-volume ratio, and exceptional chemical and physical properties, nanoparticles are theoretically expected to enhance the performance of polymer systems. In practice, the preparation of polymer nanocomposites brings its own challenges. To achieve the best reinforcing effect, nanoparticles should be uniformly dispersed within the polymer matrix, and effectively interact with the polymer chains. The strong attractive forces that exist at molecular levels between the nanoparticles through their extensive interfacial area make it very challenging to overcome these issues. Furthermore, the presence of nanoparticles in the polymer matrix significantly alters the processing condition of the polymer system. Therefore, the main objective of this thesis is to investigate the dispersion and interfacial interactions in polymer nanocomposites. To avoid the uncertainties associated with the visual and qualitative analysis of dispersion, which is commonly used by the researchers, a robust algorithm was developed to automatically quantify the state of dispersion in optical and electron micrographs. Several experiments were carried out to perceive the influence of surface modification of nanoparticles on the performance of polymer nanocomposites. Two different material systems were examined: organoclay-modified polylactide (PLA), and carbon nanotube (CNT)-modified epoxy. It was shown that the proper surface modification of nanoparticles could facilitate the dispersion, and consequently, enhance the mechanical and physical performance of the host polymer. To understand the effect of processing conditions on the dispersion quality of nanocomposites, a systematic study on the dispersion stability of CNT-modified epoxy resin was performed. It was found out that at elevated temperatures, CNTs show little affinity for the resin; thereby, any factors that promote the possibility of contacts between the nanotubes may result in their reagglomeration. Consequently, during processing, the dispersion stability is highly influenced with the flow-induced mobility of nanotubes resulting from external shear forces or a change in the viscosity. The insights gained throughout this investigation can be used to optimize the processing conditions and manufacturing methods for maintaining a stable dispersion during processing, and consequently obtaining nanocomposites with enhanced performance.
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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 | 0.000 | 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 | 0.001 |
| 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".