Visualizing the 'invisible'

The ability of scientists to image and manipulate matter at the (sub)atomic scale is a result of stunning advances in microscopy. Foremost amongst these was the invention of the scanning probe microscope, which, despite its classification as a microscope, does not rely on optics to generate images. Instead, images are produced via the interaction of an atomically sharp probe with a surface. Here the author considers to what extent those images represent an accurate picture of ‘reality’ at a size regime where quantum physics holds sway, and where the image data can be acquired and manipulated in a variety of ways

Anisotropic Assembly of Colloidal Nanoparticles: Exploiting Substrate Crystallinity

We show that the crystal structure of a substrate can be exploited to drive the anisotropic assembly of colloidal nanoparticles. Pentanethiol-passivated Au particles of approximately 2 nm diameter deposited from toluene onto hydrogen-passivated Si(111) surfaces form linear assemblies (rods) with a narrow width distribution. The rod orientations mirror the substrate symmetry, with a high degree of alignment along principal crystallographic axes of the Si(111) surface. There is a strong preference for anisotropic growth with rod widths substantially more tightly distributed than lengths. Entropic trapping of nanoparticles provides a plausible explanation for the formation of the anisotropic assemblies we observe

Formation routes and structural details of the CaF1 layer on Si(111) from high-resolution noncontact atomic force microscopy data

We investigate the CaF1/Si(111) interface using a combination of high-resolution scanning tunnelling and noncontact atomic force microscopy operated at cryogenic temperature as well as x-ray photoelectron spectroscopy. Submonolayer CaF1 films grown at substrate temperatures between 550 and 600 ◦C on Si(111) surfaces reveal the existence of two island types that are distinguished by their edge topology, nucleation position, measured height, and inner defect structure. Our data suggest a growth model where the two island types are the result of two reaction pathways during CaF1 interface formation. A key difference between these two pathways is identified to arise from the excess species during the growth process, which can be either fluorine or silicon. Structural details as a result of this difference are identified by means of high-resolution noncontact atomic force microscopy and add insights into the growth mode of this heteroepitaxial insulator-on-semiconductor system

Embedding human heuristics in machine-learning-enabled probe microscopy

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

Improving the Segmentation of Scanning Probe Microscope Images using Convolutional Neural Networks

A wide range of techniques can be considered for segmentation of images of nanostructured surfaces. Manually segmenting these images is time-consuming and results in a user-dependent segmentation bias, while there is currently no consensus on the best automated segmentation methods for particular techniques, image classes, and samples. Any image segmentation approach must minimise the noise in the images to ensure accurate and meaningful statistical analysis can be carried out. Here we develop protocols for the segmentation of images of 2D assemblies of gold nanoparticles formed on silicon surfaces via deposition from an organic solvent. The evaporation of the solvent drives far-from-equilibrium self-organisation of the particles, producing a wide variety of nano- and micro-structured patterns. We show that a segmentation strategy using the U-Net convolutional neural network outperforms traditional automated approaches and has particular potential in the processing of images of nanostructured systems.Comment: 21 pages, 10 figure

Intramolecular bonds resolved on a semiconductor surface

Noncontact atomic force microscopy (NC-AFM) is now routinely capable of obtaining submolecular resolution, readily resolving the carbon backbone structure of planar organic molecules adsorbed on metal substrates. Here we show that the same resolution may also be obtained for molecules adsorbed on a reactive semiconducting substrate. Surprisingly, this resolution is routinely obtained without the need for deliberate tip functionalization. Intriguingly, we observe two chemically distinct apex types capable of submolecular imaging. We characterize our tip apices by “inverse imaging” of the silicon adatoms of the Si(111)−7×7 surface and support our findings with detailed density functional theory (DFT) calculations. We also show that intramolecular resolution on individual molecules may be readily obtained at 78 K, rather than solely at 5 K as previously demonstrated. Our results suggest a wide range of tips may be capable of producing intramolecular contrast for molecules adsorbed on semiconductor surfaces, leading to a much broader applicability for submolecular imaging protocols

Automated Searching and Identification of Self-Organized Nanostructures

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-organisation. 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 dataset of real AFM images of self-organised nanoparticles. We do this regardless of feature-scale and without the need for manually labelled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organised systems and datasets

Unique determination of “subatomic” contrast by imaging covalent backbonding

The origin of so-called “subatomic” resolution in dynamic force microscopy has remained controversial since its first observation in 2000. A number of detailed experimental and theoretical studies have identified different possible physicochemical mechanisms potentially giving rise to subatomic contrast. In this study, for the first time we are able to assign the origin of a specific instance of subatomic contrast as being due to the back bonding of a surface atom in the tip−sample junction

Effect of Alirocumab on Lipoprotein(a) and Cardiovascular Risk After Acute Coronary Syndrome

Background: Lipoprotein(a) concentration is associated with cardiovascular events. Alirocumab, a proprotein convertase subtilisin/kexin type 9 inhibitor, lowers lipoprotein(a) and low-density lipoprotein cholesterol (LDL-C). Objectives: A pre-specified analysis of the placebo-controlled ODYSSEY Outcomes trial in patients with recent acute coronary syndrome (ACS) determined whether alirocumab-induced changes in lipoprotein(a) and LDL-C independently predicted major adverse cardiovascular events (MACE). Methods: One to 12 months after ACS, 18,924 patients on high-intensity statin therapy were randomized to alirocumab or placebo and followed for 2.8 years (median). Lipoprotein(a) was measured at randomization and 4 and 12 months thereafter. The primary MACE outcome was coronary heart disease death, nonfatal myocardial infarction, ischemic stroke, or hospitalization for unstable angina. Results: Baseline lipoprotein(a) levels (median: 21.2 mg/dl; interquartile range [IQR]: 6.7 to 59.6 mg/dl) and LDL-C [corrected for cholesterol content in lipoprotein(a)] predicted MACE. Alirocumab reduced lipoprotein(a) by 5.0 mg/dl (IQR: 0 to 13.5 mg/dl), corrected LDL-C by 51.1 mg/dl (IQR: 33.7 to 67.2 mg/dl), and reduced the risk of MACE (hazard ratio [HR]: 0.85; 95% confidence interval [CI]: 0.78 to 0.93). Alirocumab-induced reductions of lipoprotein(a) and corrected LDL-C independently predicted lower risk of MACE, after adjustment for baseline concentrations of both lipoproteins and demographic and clinical characteristics. A 1-mg/dl reduction in lipoprotein(a) with alirocumab was associated with a HR of 0.994 (95% CI: 0.990 to 0.999; p = 0.0081). Conclusions: Baseline lipoprotein(a) and corrected LDL-C levels and their reductions by alirocumab predicted the risk of MACE after recent ACS. Lipoprotein(a) lowering by alirocumab is an independent contributor to MACE reduction, which suggests that lipoprotein(a) should be an independent treatment target after ACS. (ODYSSEY Outcomes: Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab; NCT01663402)