Chiangraeng, Natthiti and Armstrong, Michael and Manokruang, Kiattikhun and Lee, Vannajan Sanghiran and Jiranusornkul, Supat and Nimmanpipug, Piyarat (2021) Characteristic structural knowledge for morphological identification and classification in meso-scale simulations using principal component analysis. Polymers, 13 (16). ISSN 2073-4360, DOI https://doi.org/10.3390/polym13162581.
Full text not available from this repository.Abstract
Meso-scale simulations have been widely used to probe aggregation caused by structural formation in macromolecular systems. However, the limitations of the long-length scale, resulting from its simulation box, cause difficulties in terms of morphological identification and insufficient classification. In this study, structural knowledge derived from meso-scale simulations based on parameters from atomistic simulations were analyzed in dissipative particle dynamic (DPD) simulations of PS-b-PI diblock copolymers. The radial distribution function and its Fourier-space counterpart or structure factor were proposed using principal component analysis (PCA) as key characteristics for morphological identification and classification. Disorder, discrete clusters, hexagonally packed cylinders, connected clusters, defected lamellae, lamellae and connected cylinders were effectively grouped.
| Item Type: | Article |
|---|---|
| Funders: | Chiang Mai University (CMU) (R000026614), Science Achievement Scholarship of Thailand (SAST) |
| Uncontrolled Keywords: | Polystyrene; Polyisoprene; Morphology; Copolymer; Structure factor; PCA |
| Subjects: | Q Science > QC Physics |
| Divisions: | Faculty of Science |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 05 Mar 2022 05:37 |
| Last Modified: | 05 Mar 2022 05:37 |
| URI: | http://eprints.um.edu.my/id/eprint/28200 |
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