Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Removal of Molecular Adsorbates on Gold Nanoparticles Using Sodium Borohydride in Water

Abstract: The mechanism of sodium borohydride removal of organothiols from gold nanoparticles (AuNPs) was studied using an experimental investigation and computational modeling. Organothiols and other AuNP surface adsorbates such as thiophene, adenine, rhodamine, small anions (Br– and I–), and a polymer (PVP, poly(N-vinylpyrrolidone)) can all be rapidly and completely removed from the AuNP surfaces. A computational study showed that hydride derived from sodium borohydride has a higher binding affinity to AuNPs than organothiols. Thus, it can displace organothiols and all the other adsorbates tested from AuNPs. Sodium borohydride may be used as a hazard-free, general-purpose detergent that should find utility in a variety of AuNP applications including catalysis, biosensing, surface enhanced Raman spectroscopy, and AuNP recycle and reuse.

An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods

Abstract: Particle swarm optimization (PSO) has attracted much attention and has been applied to many scientific and engineering applications in the last decade. Most recently, an intelligent augmented particle swarm optimization with multiple adaptive methods (PSO-MAM) was proposed and was demonstrated to be effective for diverse functions. However, inherited from PSO, the performance of PSO-MAM heavily depends on the settings of three parameters: the two learning factors and the inertia weight. In this paper, we propose a parameter control mechanism to adaptively change the parameters and thus improve the robustness of PSO-MAM. A new method, adaptive PSO-MAM (APSO-MAM) is developed that is expected to be more robust than PSO-MAM. We comprehensively evaluate the performance of APSO-MAM by comparing it with PSO-MAM and several state-of-the-art PSO algorithms and evolutionary algorithms. The proposed parameter control method is also compared with several existing parameter control methods. The experimental results demonstrate that APSO-MAM outperforms the compared PSO algorithms and evolutionary algorithms, and is more robust than PSO-MAM.