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The development of interpretable structure/property relationships is a cornerstone of nanoscience, but can be challenging when the structural diversity and complexity exceeds our ability to characterise it. This is often the case for imperfect, disordered and amorphous nanoparticles, where even the nomenclature can be unspecific. Disordered platinum nanoparticles have exhibited superior performance for some reactions, which makes a systematic way of describing them highly desirable. In this study we have used a diverse set of disorder platinum nanoparticles and machine learning to identify the pure and representative structures based on their similarity in 121 dimensions. We identify two prototypes that are representative of separable classes, and seven archetypes that are the pure structures on the convex hull with which all other possibilities can be described. Together these nine nanoparticles can explain all of the variance in the set, and can be described as either single crystal, twinned, spherical or branched; with or without roughened surfaces. This forms a robust sub-set of platinum nanoparticle upon which to base further work, and provides a theoretical basis for discussing structure/property relationships of platinum nanoparticles that are not geometrically ideal. [ABSTRACT FROM AUTHOR]

Previous experimental studies have indicated that the controlled formation of anisotropic pyrite nanoparticles, such as nanorods or nanowires, is dependent on the right combination of solution chemistry and temperature. Similarly, the morphology of the individual nanocrystals during intracellular biomineralization of single nanocrystals has been attributed to the local environmental conditions, as well as the species of the micro-organism. Although there are obvious similarities, using the lessons from biomineralization to assist the laboratory synthesis of anisotropic pyrite nanostructures, and in the anticipation of environmental stability, requires a more detailed understanding of the role played by individual environmental parameters. In the present study we use a multi-scale thermodynamic model, combined with parameters obtained from first principles calculations, to investigate the formation and stability of pyrite nanorods as a function of temperature and chemical environment. The results of our systematic modeling of parameter space predict that the morphology of pyrite nanorods grown in the laboratory, or associated with biomineralization, is more likely to be a function of surface ligands and the biology of the organisms than a function of simpler environmental parameters such as temperature, pressure, concentration of sulfur and adsorption of water.