Abstract
Though theoretical and computational studies typically agree on the low energy, equilibrium structure of metallic nanoparticles, experimental studies report on samples with a distribution of shapes; including high-index, non-equilibrium morphologies. This apparent inconsistency is not due to inaccuracy on either side, nor the result of unquantifiable competition between thermodynamic and kinetic influences, but rather a lack of clarity about what is being inferred. The thermodynamic stability, statistical probability, and the observed population of a given structure are all straightforward to determine, provided an ensemble of possible configurations is included at the outset. To clarify this relationship, a combination of electronic structure simulations and mathematical models will be used to predict the relative stabilities, probability and population of various shapes of Ag, Au, Pd and Pt nanoparticles, and provide some explanation for the observation of high-index, non-equilibrium morphologies. As we will see, a nanoparticle can be in the ground-state, and therefore most thermodynamically stable, but can still be in the minority.
Highlights
Over the past decade our ability to generate, probe and model nanoscale systems has been rapidly improving, and we have seen a convergence in the availability and suitability of research tools.[1,2,3,4,5,6,7] Thanks to the advent of high performance computing and the increased resolution of characterization methods, we can observe and simulate on the same scale; working together on the same thing.[8,9] This has been a remarkable catalyst for collaboration in nanoscience and nanotechnology
The results show that even though there is only one ‘groundstate’ morphology in each case, all other possibilities have a non-zero probability of observation, and will have a statistically signi cant population
When characterizing colloidal samples one should not be overly concerned that the predicted low energy morphology appears to be under-represented
Summary
Over the past decade our ability to generate, probe and model nanoscale systems has been rapidly improving, and we have seen a convergence in the availability and suitability of research tools.[1,2,3,4,5,6,7] Thanks to the advent of high performance computing and the increased resolution of characterization methods, we can observe and simulate on the same scale; working together on the same thing.[8,9] This has been a remarkable catalyst for collaboration in nanoscience and nanotechnology This convergence and collaboration has highlighted a number of inconsistencies that can erode con dence. These (seemingly contradictory) studies may have been designed to achieve very different objectives, and so the results may not be entirely representative; but this does not mean they are wrong
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