Understanding Spatio-Temporal Dynamics at the Atomic Level through Machine-Learning andData Science Methods for Electron Microscopy

Data:      04/10/2024 (sexta-feira)
Horário:  11:00 h
Local:  sala 201 da torre antiga

publicado: 01/10/2024 23h07,
última modificação: 07/10/2024 13h23

Data:      04/10/2024 (sexta-feira)

Horário:  11:00 h

Local:      sala 201 da torre antiga  

Caros estudantes e docentes,

O próximo seminário do grupo de matéria condensada ocorrerá com a apresentação do Prof. Peter Crozier (Arizona State University-USA). O título e abstract seguem abaixo.

Tarik em nome da comissão organizadora. 

TítuloUnderstanding Spatio-Temporal Dynamics at the Atomic Level through Machine-Learning andData Science Methods for Electron MicroscopyResumo:

Localized atomic structural dynamics may be associated with some materials functionalities, such as
transport or surface reactivity. For example, oxygen ion or electron transport in oxides cause localized
lattice distortions as charged species move through the crystal. Surface processes, including catalysis and
diffusion, are also associated with localized atomic reconfigurations resulting from bond making and
breaking. In these processes, the overall crystal structure does not change but local spatio-temporal
fluctuations occur. While rapid electron transfer ultimately triggers these events, the nuclear re-
arrangements occur on much longer times scales and may give rise to longer-lived metastable
intermediate states. Modern in situ transmission electron microscopy and new electron detection systems
allow atomic resolution imaging to be performed with millisecond time resolution offering the ability to
probe the time evolution of longer-lived metastable states.
In practice, signal-to-noise considerations impose an inverse relationship between the achievable temporal and spatial precision. Processing methods that are robust in the presence of high levels of noise, e.g. blob detection or denoising techniques based on neural networks are essential. Nanoparticles showing high degrees of fluxionality behave in a stochastic manner where the system transitions from a stationary metastable state to a series of rapidly changing unstable configurations. The structures of the metastable states and the triggers the lead to intense instability are not well described or understood and must be characterized. We have performed time dependent measurements of structural dynamics in metal nanoparticle and non-stoichiometric oxides related to transport and catalytic functionalities. We observe interesting gas-induced structural transformation pathways operating in small nanoparticles. The
behaviors are complex but are driven by strain and surface diffusion resulting in vacancy creation/annihilation, formation of stacking faults, and temporary loss of long-range order.

Pular para o conteúdo