Global Particle Physics Excellence Awards

Numerical evaluation of virtual mass force for homogeneous liquid-solid fluidization Numerical evaluation of virtual mass force for homogeneous liquid-solid fluidization is a complex problem that involves computational fluid dynamics (CFD) and multiphase flow modeling. The virtual mass force is a crucial aspect of fluidization, as it affects the behavior of particles in a fluidized bed. In a homogeneous liquid-solid fluidization system, the virtual mass force plays a significant role in determining the particle distribution, velocity, and pressure drop. To numerically evaluate the virtual mass force, researchers often employ CFD simulations, which involve solving the Navier-Stokes equations for the fluid phase and the equations of motion for the solid particles. The simulations can provide detailed insights into the fluid dynamics and particle behavior, allowing for the evaluation of the virtual mass force and its effects on the system. Global Particle Physics Excellence Awards More In...

                        Best Scholar Award We are thrilled to announce the recipient of this year’s Best Scholar Award —a shining example of academic excellence, dedication, and intellectual growth. This prestigious recognition isn’t just about grades; it's about perseverance, passion, curiosity, and the drive to go above and beyond. From the first day of the academic year, this remarkable student has displayed not only a deep understanding of their subjects but also a consistent hunger for knowledge. Their academic record is outstanding, their research contributions inspiring, and their engagement in extracurricular activities nothing short of admirable. Whether tackling complex scientific concepts, engaging in thoughtful debates, leading group projects, or mentoring fellow students, our Best Scholar has done it all—with humility and grace. This award celebrates not only their intellect but their integrity, leadership, and com...

  Physics-Informed Deep Learning for Virtual Rail Train Trajectory Following Control Physics-Informed Deep Learning for Virtual Rail Train Trajectory Following Control In recent years, advancements in artificial intelligence have reshaped the way we approach complex control systems, especially in the transportation sector. One of the most promising applications is the use of Physics-Informed Deep Learning (PIDL) for Virtual Rail Train Trajectory Following Control . This approach combines the predictive power of machine learning with the reliability of physical laws, providing a robust solution for real-time trajectory control in autonomous rail systems. Virtual Rail Technology Virtual rail technology refers to a system where trains follow a digitally defined path without requiring physical rails for guidance. Instead, trains use sensors, GPS, and control algorithms to stay within a virtual corridor, much like how autonomous cars follow lanes using camera and sensor data. This inn...

                     Women Researcher Award Women researchers have played a pivotal role in shaping the world of science, technology, medicine, and innovation, yet their contributions have often been overlooked or underappreciated. Recognizing the need for gender equity and the importance of diversity in research, numerous awards have been established worldwide to honor and celebrate the achievements of women researchers. These awards serve not only to acknowledge excellence but also to inspire future generations of women to pursue careers in research and academia. The Women Researcher Award is a significant milestone in this journey, highlighting groundbreaking discoveries, leadership, and contributions across various disciplines.  Women researchers have made remarkable contributions in fields such as medicine, engineering, environmental science, physics, chemistry, and artificial intelligence, often overcoming gender-based b...

 A physics-informed 3D surrogate model for elastic fields in polycrystals Polycrystalline materials, which consist of numerous crystallites or grains, exhibit complex mechanical responses due to the interaction of their microstructural features. Accurately modeling the elastic fields in polycrystals is crucial for predicting material behavior under stress.  Traditional methods, such as finite element analysis (FEA), are computationally expensive, particularly when dealing with three-dimensional (3D) microstructures. To address this challenge, physics-informed 3D surrogate models offer a promising alternative by integrating physical principles directly into machine learning frameworks. Physics-informed neural networks (PINNs) bridge the gap between data-driven approaches and traditional physics-based models. These models encode governing equations, such as the Navier-Cauchy equations for elasticity, as part of the loss function. By enforcing physical laws during training, PINNs...

  Precisely constructing asymmetric triple atoms for highly efficient electrocatalysis Triple-atom catalysts (TACs) are promising for surpassing the activity of normal single-atom and dual-atom catalysts. However, the rational design and construction of TACs remain challenging. Herein, we developed asymmetric Pt-Ru-Co triple atoms (TAs) by using selective atomic layer deposition technology. Compared with the corresponding single-atom and dual-atom counterparts, they demonstrate superior electrocatalytic performance in both the hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR).  Operando  X-ray absorption spectroscopy (XAS) revealed that the heterogeneous atoms within Pt-Ru-Co TAs have strong interactions and serve as active centers, synergistically accelerating reaction kinetics.  Additionally, theoretical calculations indicate that introducing Co atoms effectively optimizes the  d  orbital electronic structure of Pt and Ru, endowing e...