Seminar khoa học của TS. Mẫn Minh Tân, TS. Đinh Thanh Bình và TS. Lê Duy Mạnh

Vào 14h00, ngày 18/04/2025 Viện IAST tổ chức buổi trao đổi học thuật tại Phòng họp C với nội dung chi tiết như sau 

1/ TS. Mẫn Minh Tân trình bày về "Wide-gap photoluminescence control of CdxZn1-xTe quantum dots with atomic interdiffusion and bandgap renormalization"
Abstract: Bandgap and photoluminescence (PL) energy control of epitaxial grown II–VI quantum dots (QDs) are highly desirable for applications in optoelectronic devices, yet few work has been studied to date. Here, we present a wide tunability of PL emission for CdTe/ZnTe QDs through an impurity-free vacancy disordering method. To induce compressive stress at the interface of dielectric layer/ZnTe, SiO2 film is deposited on the samples and followed by rapid thermal annealing for atomic interdiffusion. After heat treatment, the PL spectra of the intermixed QDs show pronounced blueshifts in peak energy up to ~200 meV, due to the bandgap renormalization and modified quantum confinement effects. In addition, we present a thorough investigation on modified physical properties of the intermixed QDs, such as lattice structure, thermal escape energy, and carrier dynamics through quantitative X-ray and optical characterizations.

 2/ TS. Đinh Thanh Bình trình bày về "Study of Neutrino radiation  and energy loss rate  in stellar process"
Abstract: Neutrinos play a role in nuclear & particle physics; Neutrinos provide a tool to study the structure of nucleons (protons and neutrinos). Neutrinos are produced in nuclear reactors while  cosmics neutrinos are produced in nuclear fusion processes that power the sun and stars. In this talk we will review some of neutrinos characteristics and application. We will also calculate the energy loss rate by neutrino radiation by neutrino pair in the framework of beyond standard model.

3/ TS. Lê Duy Mạnh trình bày về "Introduction to Bivariate Time Series Analysis in Complex Systems"
Abstract: Understanding the intricate dependencies between time-dependent variables is essential in studying complex systems. Bivariate time series analysis offers powerful tools to uncover hidden relationships, predict interactions, and extract meaningful information across various fields such as finance, climate science, biology, etc. In this report, I will introduce multiple analytical techniques to explore bivariate time series (assumed to have relations), including Cross-Correlation Function, Fourier Transform, Wavelet Analysis, Transfer Entropy, and Empirical Mode Decomposition with Hilbert-Huang Transform (EMD-HHT). By evaluating these methods, we highlight their strengths, limitations, and applications in capturing dynamic relationships and causal interactions between two time series.