PhD Thesis Defence Presentations - Flora Tsourtou
In soft nanostructured materials based on chain molecules (typical examples include organic semiconducting polymers and polypeptides), chain self-organization at the nanoscale and mesoscale completely controls their macroscopic behavior and functionality. Although the equilibrium structure of many of these systems is rather well-known today thanks to advanced experimental techniques, their molecular modelling remains challenging, currently preventing the use of computer simulations as a tool for the rational design of new nanostructured materials with tailored or modulated properties.
The objective of the current dissertation is to investigate self-organization in soft nanostructured materials by means of atomistic simulations based on Molecular Dynamics (MD) and Monte Carlo (MC) methods. The latter involves the design and implementation of new stochastic (i.e., non-dynamic) algorithms capable of overcoming the problem of long relaxation times governing chain dynamics and plaguing MD methods. Through the utilization of some very powerful ‘unhysical’ moves, MC can increase dramatically the rate of sampling new microstructures. State-of-the-art MC moves, originally proposed for simpler polymer structures, were thus redesigned for two important classes of soft nanostructured materials: (a) thiophene-based semiconducting oligomers and polymers and (b) polypeptides, which exhibit spectacular structures at the nanoscale and have tremendous technological applications.
First, a powerful MC algorithm was developed for the simulation of bulk models of α-unsubstituted oligo- and poly-thiophenes using moves that account for the rigid ring structure of the thiophene moiety. We also introduced two new united-atom models for the simulation of these materials: a rigid model for MC simulations and a more flexible model for MD simulations. We were able to predict the high temperature phase behavior of an important α-unsubstituted oligo-thiophene (α-nΤ with n denoting the number of rings), α-sexithiophene (α-6T), in good agreement with available literature data. Furthermore, the MD simulations were extended to other members of the family of α-nTs (n = 5, 7 and 8) to gain an insight into the dependence of their phase behavior on chain length. Upon cooling from the isotropic phase, spontaneous successive phase transitions were observed giving rise to liquid crystalline phases; an odd-even structural phenomenon was also observed. For the design of a MC algorithm in the future for the simulation of alkyl-substituted poly-thiophenes such as regioregular poly(3-hexylthiophene) (or RR-P3HT), the availability of a promising force field (FF) will also be of importance. We thus carried out a systematic evaluation of available all-atom FFs that can reliably describe the physical properties of RR-P3HT oligomers and polymers in their amorphous phase. By selecting the most accurate FF, large scale MD simulations of RR-P3HT with quite long chains were conducted in order to shed light into their structural behavior in the amorphous phase. Our results indicated that relatively short RR-P3HT chains are semiflexible but adopt random coil conformations at higher temperatures and for higher molecular weights.
As far as the polypeptides are concerned, a new MC algorithm was designed and implemented using an all-atom approximation for homo-polypeptides based on the L-alanine amino acid residue. The new methodology was capable of predicting the secondary structure of homo-polypeptides consisting of a few decades of residues, characterized by the formation of a significant population of α-helix secondary structure elements under vacuum, starting from a random configuration as an initial condition. These first simulation results are very promising rendering possible the extension of the proposed methodology to melts and solutions of poly-L-alanine peptides.
Speakers Short CV (Σύντομο Βιογραφικό Ομιλητή)
Flora Tsourtou received her Diploma in Chemical Engineering from the University of Patras in 2010 and her Master in Materials Science and Technology also from the same University in 2014. Since then, she is a PhD student in the same Department under the advisement of Prof. Vlasis G. Mavrantzas. She has been working on the design and efficient implementation of state-of-the-art Monte Carlo algorithms for the simulation of self-assembly and chain self-organization in Soft Matter systems. In her PhD, she has been working on: (1) the prediction of nano-scale morphology in thiophene-based semiconducting polymers, and (2) the prediction of the secondary structure in polypeptides. From her master studies, the obtained results have been published in international scientific journals. She has accomplished five publications and one of them was selected for a Cover Page in Soft Matter Journal. She was granted with a Ph.D.-ELIDEK scholarship and she has participated in several research projects (ARISTEIA 2011, FORCE, HPC-Europa3).