Liu Tun-dong

Morphology and structural stability of Pt?Pd bimetallic nanoparticles

The morphologies and structures of Pt–Pd bimetallic nanoparticles determine their chemical and physical properties. Therefore, a fundamental understanding of their morphologies and structural stabilities is of crucial importance to their applications. In this article, we have performed Monte Carlo simulations to systematically explore the structural stability and structural features of Pt–Pd alloy nanoparticles. Different Pt/Pd ratios, and particle sizes and shapes were considered. The simulated results reveal that the truncated octahedron, which has the remarkably lowest energy among all the considered shapes, exhibits the best structural stability while the tetrahedron has the worst invariably. Furthermore, all the structures of Pt–Pd alloy nanoparticles present Pd-rich in the outmost layer but Pt-rich in the sub-outmost layer. Especially, atomic distribution and chemical short-range order parameter were applied to further characterize the structural features of Pt–Pd alloy nanoparticles. This study provides a significant insight not only into the structural stability of Pt–Pd alloy nanoparticles with different compositions, and particle sizes and shapes but also to the design of bimetallic nanoparticles.

Solving TSP via fuzzy dynamic PSO and HNN algorithm

Since the Hopfield network often suffers from slow rate of convergence and low accuracy and being trapped in local extremes when used to solve the traveling salesman problem, this paper combines the fuzzy dynamic particle swarm optimization (PSO) and Hopfield neural networks (HNN) to form a novel algorithm, FDPSO-HNN. Experiments show that the proposed methods outperform the algorithm of EPSO and N-EPSO in terms of both global convergence rate and computation time.