Abstract
A novel approach for finding and evaluating structural models of small metallic nanoparticles is presented. Rather than fitting a single model with many degrees of freedom, libraries of clusters from multiple structural motifs are built algorithmically and individually refined against experimental pair distribution functions. Each cluster fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles.
Highlights
Advances in the synthesis of metallic nanoparticles have given researchers a great deal of control in tailoring their functionalities for many applications including catalysis (Lewis, 1993; Somorjai & Park, 2008), plasmonics (Atwater & Polman, 2010; Linic et al, 2011), energy conversion (Aricoet al., 2005) and biomedicine (Rosi & Mirkin, 2005; Ackerson et al, 2006; Nune et al, 2009)
The distinct properties of nanoparticles can be attributed to the increased role of their external surfaces, which can be manipulated by changing experimental parameters in a synthesis to obtain particles of a certain size, shape and composition
A76, 24–31 research papers structures have long been reported in electron microscopic studies of metallic nanoparticles (Ino, 1966; 1969; Marks & Howie, 1979; Sun & Xia, 2002; Chen et al, 2013) and it is established that growth mechanisms across a diversity of synthesis methods are directed by the size-dependent formation and rearrangement of multiply twinned domains, in addition to thermodynamic stabilization of nanoparticle surfaces by capping agents (Lofton & Sigmund, 2005; Langille et al, 2012; Marks & Peng, 2016)
Summary
Advances in the synthesis of metallic nanoparticles have given researchers a great deal of control in tailoring their functionalities for many applications including catalysis (Lewis, 1993; Somorjai & Park, 2008), plasmonics (Atwater & Polman, 2010; Linic et al, 2011), energy conversion (Aricoet al., 2005) and biomedicine (Rosi & Mirkin, 2005; Ackerson et al, 2006; Nune et al, 2009). A76, 24–31 research papers structures have long been reported in electron microscopic studies of metallic nanoparticles (Ino, 1966; 1969; Marks & Howie, 1979; Sun & Xia, 2002; Chen et al, 2013) and it is established that growth mechanisms across a diversity of synthesis methods are directed by the size-dependent formation and rearrangement of multiply twinned domains, in addition to thermodynamic stabilization of nanoparticle surfaces by capping agents (Lofton & Sigmund, 2005; Langille et al, 2012; Marks & Peng, 2016) Despite this evidence, atomic models built from face-centered cubic (f.c.c.) cores, which do not account for the multi-domain nature of these materials, are still commonly used in atomic pair distribution function (PDF) analysis of metallic nanostructures (Petkov & Shastri, 2010; Page et al, 2011; Kumara et al, 2014; Fleury et al, 2015; Wu et al, 2015; Poulain et al, 2016; Petkov et al, 2018). This approach uses many structure models and highly constrained refinements to screen libraries of discrete clusters against experimental PDF data, with the aim of finding the most representative cluster structures for the ensemble average nanoparticle from any given synthesis
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