Size-dependent phase transformation of catalytically active nanoparticles captured in situ.

The utilization of metal nanoparticles traverses across disciplines and we continue to explore the intrinsic size-dependent properties that make them so unique. Ideal nanoparticle formulation to improve a process's efficiency is classically presented as exposing a greater surface area to volume ratio through decreasing the nanoparticle size. Although, the physiochemical characteristics of the nanoparticles, such as phase, structure, or behavior, may be influenced by the nature of the environment in which the nanoparticles are subjected1, 2 and, in some cases, could potentially lead to unwanted side effects. The degree of this influence on the particle properties can be size-dependent, which is seldom highlighted in research. Herein we reveal such an effect in an industrially valuable cobalt Fischer-Tropsch synthesis (FTS) catalyst using novel in situ characterization. We expose a direct correlation that exists between the cobalt nanoparticle's size and a phase transformation, which ultimately leads to catalyst deactivation.

The determination of oxide surface charging parameters for a predictive metal adsorption model.

The procurement of oxide surface charging parameters has been a widely researched topic in recent years [1-30]. In this study, a one-site, two-pK surface charging mechanism is used in combination with a diffuse double-layer description of the electric double-layer to fit pH shift data over silica and alumina. From these fits of pH data, with no further adjustment of parameters, metal adsorption can be predicted over both supports to a reasonable degree of accuracy. A multi-dimensional optimization procedure employing a Nelder-Mead simplex algorithm is used to optimize the DeltapK (pK(2)-pK(1)) parameter to obtain a best fit of the pH shift data with fixed PZC and hydroxyl density (N(s)). The resulting set of parameters is then used with no adjustment in a purely electrostatic adsorption model (the Revised Physical Adsorption or RPA model) in order to predict anionic chloroplatinic acid (CPA, [PtCl(6)](-2)) adsorption on alumina and cationic platinum tetraammine (PTA, [Pt(NH(3))(4)](+2)) adsorption on alumina and silica. The optimization procedure developed in this study gives reasonable values of the DeltapK compared to other values reported in the literature, with fits to the pH shift data at various oxide loadings with relative errors below 2.8%.