Scientific Program

Conference Series LLC Ltd invites all the participants across the globe to attend 10th International Conference on Physical and Theoretical Chemistry London, UK.

  • Material Science and Engineering
Location: Webinar

Osman Adiguzel graduated from Department of Physics, Ankara University, Turkey in 1974 and received PhD- degree from Dicle University, Diyarbakir-Turkey. He has studied at Surrey University, Guildford, UK, as a post-doctoral research scientist in 1986-1987, and studied on shape memory alloys. He worked as research assistant, 1975-80, at Dicle University and shifted to Firat University, Elazig, Turkey in 1980. He became a professor in 1996, and he has been retired on November 28, 2019, due to the age limit of 67, following academic life of 45 years. He published over 80 papers in international and national journals; He joined over 120 conferences and symposia in international and national level as participant, invited speaker or keynote speaker with contributions of oral or poster. He served the program chair or conference chair/co-chair in some of these activities. In particular, he joined in last six years (2014 - 2019) over 60 conferences as Keynote Speaker and Conference Co-Chair organized by different companies. Also, he joined over 120 online conferences in the same way in pandemic period of 2020-2022. He supervised 5 PhD- theses and 3 M. Sc- theses. 


Shape memory effect is a peculiar property exhibited a series of alloy system called shape memory alloys, which have dual characteristics called thermoelasticity and superelasticity, from viewpoint of memory behavior. These alloys take place in class of advanced novel materials with these properties and response to the external conditions. Shape memory effect is initiated with thermomechanical processes, on cooling and deformation, and performed on heating and cooling, with which shape of the materials cycles between original and deformed shapes in reversible way. Therefore, this behavior can be called thermoelasticity. This phenomenon is governed by two successive crystallographic transformations, thermal and stress induced martensitic transformations. Thermal induced martensitic transformation occurs along with lattice twinning and ordered parent phase structures turn into multivariant twinned martensite structures in self-accommodating manner, and twinned martensite structures turn into detwinned martensite by means of stress induced martensitic transformation on stressing.

Thermal induced martensitic transformation occurs on cooling, with cooperative movement of atoms in <110 > -type directions on the {110} - type planes of austenite matrix, along with lattice twinning and ordered parent phase structures turn into twinned martensite structures. The twinned structures turn into detwinned structures by means of stress induced transformation by stressing the material in the martensitic condition. Martensitic transformations are diffusionless transformations, and movements of atoms are confined into neighbor atom distances.


Catherine Vasnetsov is focused on interface of chemistry and biology, and she developed her expertise in application of advanced computational methods to modeling complex biopolymers. Recognizing the limitations of existing computer models, Catherine has turned to Victor Vasnetsov for a deeper understanding of field theory as a tool to derive new insights and enhance the accuracy of predictions. The authors devised a rigorous method to validate initial predictions using experimental data, thus ensuring the practical relevance of the research work.


Statement of the Problem: Thermo-responsive polymers attract significant interest in academic and applied chemistry fields, improving effectiveness in drug delivery within human organs. Underlying mechanisms governing their functionality in practical scenarios are very complex, requiring better understanding of their behavior at a molecular level. Our research was focused on investigating co-solvency in long-chain polymers using the Flory-Huggins mean field theory. By employing Monte Carlo molecular simulations on the high-performance computing platforms of Princeton University, we accurately simulated polymer-solvent interactions under various energetic conditions.

Methodology and Theoretical Orientation: Our research was based on theoretical frameworks of field theory and statistical mechanics to develop an initial computer model in predicting polymer behavior through spinodal graphs and ternary plots. Afterwards molecular dynamic simulations were conducted and supplemented with machine learning techniques to model real-life polymers behavior under a range of different condition.