Seminar khoa học của ThS. Đặng Lâm Tuấn Cường, ThS.Vũ Minh Đức, ThS. Đỗ Thanh Hùng và CN. Thái Quỳnh Mai

Vào 14h00, ngày 5/9/2024 Viện IAST tổ chức buổi trao đổi học thuật tại Phòng họp C với nội dung chi tiết như sau: 

ThS. Đặng Lâm Tuấn Cường trình bày về "One-pot preparation of graphene oxide-cellulose aerogels for high-performance elimination of antibiotics"

Tóm tắt:

In this study, we report the one-pot preparation of graphene oxide-cellulose aerogels using sonication-assisted technique and lyophilization, and then investigate the adsorption studies of antibiotics. The materials were characterized by X-ray powder diffraction, Fourier-transform infrared spectroscopy, Raman spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Brunauer-Emmett-Teller analysis. The adsorption studies consisted of the effects of solution pH, adsorbent dose, adsorption isotherms, and adsorption kinetics on the adsorption performance of the graphene oxide-cellulose aerogels. The adsorption isotherms results indicate that the experimental data well aligned with the Freundlich isotherm model, while the adsorption kinetics were analyzed and found to satisfy the Elovich kinetic model, showing that antibiotics adsorption was in favor of the heterogeneous surface of the as-prepared aerogels. The findings signify that the aerogels featured the outstanding properties of eco-friendliness, economical preparation, and high-performance antibiotics adsorption. Hence, treating wastewater containing antibiotics could be alleviated by means of the graphene oxide-cellulose aerogels.

ThS. Vũ Minh Đức trình bày về "Nonlinear thermo-mechanical axisymmetric stability of FG-GPLRC spherical shells and circular plates resting on nonlinear elastic medium"

Tóm tắt:

An analytical approach for nonlinear thermo-mechanical buckling of functionally graded graphene platelet reinforced composite (FG-GPLRC) circular plates and shallow spherical shells resting on nonlinear elastic medium using the higher-order shear deformation theory (HSDT) and the nonlinearities of von Kármán is established in the present work. Assuming that the structures are axisymmetrically displaced for the central axis and external pressure and thermal loads are applied. The nonlinear elastic medium is used to model the behaviour of hardening or softening medium depending on the positive and negative values of the nonlinear parameter, respectively. By applying the Ritz energy method, the relation expressions of load-deflection are archived to investigate the postbuckling response and critical buckling load of the structures. Special effects on the nonlinear thermo-mechanical behaviour of plates and shells with different medium stiffnesses, different material parameters, and different geometrical dimensions are explored and discussed in numerical results.

ThS. Đỗ Thanh Hùng trình bày về "The discovery of a new diphenyl ether compound from the endolichenic fungus Graphis cf. handelii cultivated in vitro"

Tóm tắt:

This study led to the isolation and structural elucidation of a new compound, graphinone A (1), alongside three known compounds: handelone (2), 4-O-methylhiascic acid (3), and ethyl orsellinate (4). The chemical structures of these compounds were confirmed through extensive spectroscopic analysis, including 1D- and 2D-NMR and high-resolution electrospray ionization mass spectrometry (HRESIMS). Subsequently, compounds 1-4 were evaluated for their antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA) and their α-glucosidase inhibitory potential.

CN. Thái Quỳnh Mai trình bày về "Using machine learning and atomistic computations for evaluating AChE inhibitors from the MCE database"

Tóm tắt:

Acetylcholinesterase (AChE) is one of the most paramount objectives for treating Alzheimer’s disease (AD) possibly prevented by inhibiting AChE. Herein,  to identify potential AChE inhibitors from the MedChemExpress (MCE) database, machine learning (ML) model, molecular docking, and molecular dynamics (MD) computations were utilized. The trained ML model was first applied to envisage the inhibitory potential of compounds from the MCE database. Subsequently, atomistic computations, including molecular docking and MD, were performed to validate the ML predictions. Specifically, the computations provided a profound understanding of the physical aspects of the binding process.