One of the largest obstacles facing scanning probe microscopy is the constant need to correct flaws in the scanning probe in situ. This is currently a manual, time-consuming process that would benefit greatly from automation. Here we introduce a convolutional neural network protocol that enables automated recognition of a variety of desirable and undesirable scanning probe tip states on both metal and non-metal surfaces. By combining the best performing models into majority voting ensembles, we find that the desirable states of H:Si(100) can be distinguished with a mean precision of 0.89 and an average receiver-operator-characteristic curve area of 0.95. More generally, high and low-quality tips can be distinguished with a mean precision of 0.96 and near perfect area-under-curve of 0.98. With trivial modifications, we also successfully automatically identify undesirable, non-surface-specific states on surfaces of Au(111) and Cu(111). In these cases we find mean precisions of 0.95 and 0.75 and area-under-curves of 0.98 and 0.94, respectively.
Scanning probe microscopists generally do not rely on complete images to assess the quality of data acquired during a scan. Instead, assessments of the state of the tip apex, which not only determines the resolution in any scanning probe technique, but can also generate a wide array of frustrating artefacts, are carried out in real time on the basis of a few lines of an image (and, typically, their associated line profiles.) The very small number of machine learning approaches to probe microscopy published to date, however, involve classifications based on full images. Given that data acquisition is the most time-consuming task during routine tip conditioning, automated methods are thus currently extremely slow in comparison to the tried-and-trusted strategies and heuristics used routinely by probe microscopists. Here, we explore various strategies by which different STM image classes (arising from changes in the tip state) can be correctly identified from partial scans. By employing a secondary temporal network and a rolling window of a small group of individual scanlines, we find that tip assessment is possible with a small fraction of a complete image. We achieve this with little-to-no performance penalty-or, indeed, markedly improved performance in some cases-and introduce a protocol to detect the state of the tip apex in real time.
The on-surface synthesis of covalently bonded materials differs from solution-phase synthesis in several respects. The transition from a three-dimensional reaction volume to quasi-two-dimensional confinement, as is the case for on-surface synthesis, has the potential to facilitate alternative reaction pathways to those available in solution. Ullmann-type reactions, where the surface plays a role in the coupling of aryl-halide functionalised species, has been shown to facilitate extended one- and two-dimensional structures. Here we employ a combination of scanning tunnelling microscopy (STM), X-ray photoelectron spectroscopy (XPS) and X-ray standing wave (XSW) analysis to perform a chemical and structural characterisation of the Ullmann-type coupling of two iodine functionalised species on a Ag(111) surface held under ultra-high vacuum (UHV) conditions. Our results allow characterisation of molecular conformations and adsorption geometries within an on-surface reaction and provide insight into the incorporation of metal adatoms within the intermediate structures of the reaction.
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.
Motivated by the quest for experimental procedures capable of controlled manipulation of single atoms on surfaces, we set up a computational strategy that explores the cyclical vertical manipulation of a broad set of single atoms on the GaAs(110) surface. Firstprinciples simulations of atomic force microscope tip−sample interactions were performed considering families of GaAs and Au-terminated tip apexes with varying crystalline termination. We identified a subset of tips capable of both picking up and depositing an adatom (Ga, As, Al, and Au) any number of times via a modify−restore cycle that "resets" the apex of the scanning probe to its original structure at the end of each cycle. Manipulation becomes successful within a certain window of lateral and vertical tip distances that are observed to be different for extracting and depositing each atom. A practical experimental protocol of special utility for potential cyclical manipulation of single atoms on a nonmetallic surface is proposed.
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