arXiv subjects

Zibo Chen

Publications and source records attributed to Zibo Chen.

3 recordsLinked to original sources

arXiv2026

Structure-Dependent Chemical Order Modification in Strained Alloy Nanoparticles

Alloy nanoparticles (nanoalloys) exhibit tuneable physicochemical properties that depend sensitively on their atomic arrangement, making control over chemical ordering a central challenge in nanomaterials design. While most theoretical studies consider nanoalloys in vacuum, practical systems are typically supported, where strong cluster-substrate interactions can introduce significant lattice strain. Here, we investigate strain as a control parameter for chemical ordering in bimetallic nanoalloys using atomistic molecular dynamics and Monte Carlo simulations. By imposing controlled tensile and compressive strain through an implicit anchored interface, we systematically probe the response of NiPt nanoparticles with distinct structural motifs. For truncated octahedral particles, we find that chemical ordering and segregation behaviour remain remarkably robust even under large strains, indicating that intrinsic thermodynamic preferences dominate. In contrast, icosahedral nanoparticles exhibit pronounced strain-induced chemical redistribution, with a significant increase in surface Ni concentration under tensile strain. This behaviour is attributed to the combined effects of intrinsic geometric frustration and a high fraction of undercoordinated sites in icosahedral structures. Our results demonstrate that strain can selectively modulate chemical ordering in nanoalloys in a structure-dependent manner, establishing a general framework for understanding strain-induced chemical ordering in nanoalloys.

cond-mat.mtrl-sci↗

arXiv2024

Y-Configuration Active Bridge (YAB) Converter: A DAB-Type Single-Stage Isolated Three-Phase AC-DC Converter with Simple Sinusoidal Control

This paper reviews commonly used three-phase isolated AC-DC converters and introduces a novel Y-configuration Active Bridge (YAB) converter for single-stage isolated AC-DC power conversion. The proposed YAB addresses the limitations of multi-stage designs by eliminating bulky electrolytic capacitor banks and input boost inductors, thereby enabling a simplified start-up process without the risk of inrush current. It retains the advantages of single-stage AC-DC Dual Active Bridge (AC-DC DAB) converters while significantly reducing control complexity. Across its entire operating range, the YAB achieves low total harmonic distortion, maintains relatively low current stress, and exhibits excellent soft-switching performance. The operating principle of the proposed converter is detailed, and an equivalent circuit model is presented. System performance is evaluated using a numerical Fast Fourier Transform (FFT) model. To validate the performance of the proposed converter, a three-phase 6 kW, 480 V YAB prototype is designed and tested in the laboratory. Experimental results demonstrate a maximum efficiency of 97.1\%.

eess.SY↗

arXiv2024

Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection

For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way. However, this way explicitly or implicitly forces the simulated data to adapt to the noisy real data when training grasp detectors, where the positional drift and structural distortion within the camera noise will harm the grasp learning. In this work, we propose a Real-to-Sim framework for 6-DoF Grasp detection, named R2SGrasp, with the key insight of bridging this gap in a real-to-sim way, which directly bypasses the camera noise in grasp detector training through an inference-time real-to-sim adaption. To achieve this real-to-sim adaptation, our R2SGrasp designs the Real-to-Sim Data Repairer (R2SRepairer) to mitigate the camera noise of real depth maps in data-level, and the Real-to-Sim Feature Enhancer (R2SEnhancer) to enhance real features with precise simulated geometric primitives in feature-level. To endow our framework with the generalization ability, we construct a large-scale simulated dataset cost-efficiently to train our grasp detector, which includes 64,000 RGB-D images with 14.4 million grasp annotations. Sufficient experiments show that R2SGrasp is powerful and our real-to-sim perspective is effective. The real-world experiments further show great generalization ability of R2SGrasp. Project page is available on https://isee-laboratory.github.io/R2SGrasp.

cs.RO↗