ABSTRACT
Molecular surgery provides the opportunity to study relatively large molecules encapsulated within a fullerene cage. Here we determine the location of an H2O molecule isolated within an adsorbed buckminsterfullerene cage, and compare this to the intrafullerene position of HF. Using normal incidence X-ray standing wave (NIXSW) analysis, coupled with density functional theory and molecular dynamics simulations, we show that both H2O and HF are located at an off-centre position within the fullerene cage, caused by substantial intra-cage electrostatic fields generated by surface adsorption of the fullerene. The atomistic and electronic structure simulations also reveal significant internal rotational motion consistent with the NIXSW data. Despite this substantial intra-cage interaction, we find that neither HF or H2O contribute to the endofullerene frontier orbitals, confirming the chemical isolation of the encapsulated molecules. We also show that our experimental NIXSW measurements and theoretical data are best described by a mixed adsorption site model.
ABSTRACT
Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organization. Here, we use a combination of Monte Carlo simulations, general statistics, and machine learning to automatically distinguish several spatially correlated patterns in a mixed, highly varied data set of real AFM images of self-organized nanoparticles. We do this regardless of feature-scale and without the need for manually labeled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organized systems and data sets.
