https://iopscience.iop.org/article/10.1088/2632-2153/ab7d2f/pdf

Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

Techniques for the analysis of membrane proteins

Evaluation of the reactivity of exhaust from various biodiesel blends as a measure of possible oxidative effects: A concern for human exposure

Scanning probe microscopy (SPM) has revolutionized our ability to explore the nanoscale world, enabling the imaging, manipulation, and characterization of materials at the atomic and molecular level. However, conventional SPM techniques suffer from limitations, such as slow data acquisition, low signal-to-noise ratio, and complex data analysis. In recent years, the field of machine learning (ML) has emerged as a powerful tool for analyzing complex datasets and extracting meaningful patterns and features in multiple fields. The combination of ML with SPM techniques has the potential to overcome many of the limitations of conventional SPM methods and unlock new opportunities for nanoscale research. In this review article, we will provide an overview of the recent developments in ML-based SPM, including its applications in topography imaging, surface characterization, and secondary imaging modes, such as electrical, spectroscopic, and mechanical datasets. We will also discuss the challenges and opportunities of integrating ML with SPM techniques and highlight the potential impact of this interdisciplinary field on various fields of science and engineering.

Scanning probe microscopy in the age of machine learning

............................................................................................................................. i Acknowledgments ........................................................................................................... vi List of Tables .................................................................................................................... x List of Figures .................................................................................................................. xi List of Symbols .............................................................................................................. xiv List of Abbreviations ...................................................................................................... xv

/pdf/employment-of-crystallographic-image-processing-techniques-3yzy464uqx.pdf

Employment of Crystallographic Image Processing Techniques to Scanning Probe Microscopy Images of Two-Dimensional Periodic Objects

Crystallographic image processing (CIP) techniques may be utilized in scanning probe microscopy (SPM) to glean information that has been obscured by signals from multiple probe tips. This may be of particular importance for scanning tunneling microscopy (STM) and requires images from a sample that is periodic in two dimensions. The image-forming current for multiple tips in STM is derived in a more straightforward manner than prior approaches. The Fourier spectrum of the current for p4mm Bloch surface wave functions and a pair of delta function tips reveals the tip-separation dependence of various types of image obscurations. In particular our analyses predict that quantum interference should be visible on a macroscopic scale in the form of bands quite distinct from the basket-weave patterns a purely classical model would create at the same periodic double STM tip separations. A surface wave function that models the essential character of highly (0001) oriented pyrolytic graphite (technically known as HOPG) is introduced and used for a similar tip-separation analysis. Using a bonding H_2 tip wave function with significant spatial extent instead of this pair of infinitesimal Dirac delta function tips does not affect these outcomes in any observable way. This is explained by Pierre Curie's well known symmetry principle. Classical simulations of multiple tip effects in STM images may be understood as modeling multiple tip effects in images that were recorded with other types of SPMs). Our analysis clarifies why CIP and crystallographic averaging work well in removing the effects of a blunt SPM tip (that consist of multiple mini-tips) from the recorded 2D periodic images and also outlines the limitations of this image processing techniques for certain spatial separations of STM mini-tips.

Clarifying multiple-tip effects on Scanning Tunneling Microscopy imaging of 2D periodic objects and crystallographic averaging in the spatial frequency domain

/pdf/crystallographic-image-processing-software-for-scanning-1zlm9k2iq9.pdf

Crystallographic Image Processing Software for Scanning Probe Microscopists

Crystallographic image processing (CIP) techniques may be utilized in scanning probe microscopy (SPM) to glean information that has been obscured by signals from multiple probe tips. This may be of particular importance for scanning tunneling microscopy (STM) and requires images from samples that are periodic in two dimensions (2D). The image-forming current for double-tips in STM is derived with a slight modification of the independent-orbital approximation (IOA) to allow for two or more tips. Our analysis clarifies why crystallographic averaging works well in removing the effects of a blunt STM tip (that consists of multiple mini-tips) from recorded 2D periodic images and also outlines the limitations of this image-processing technique for certain spatial separations of STM double-tips. Simulations of multiple mini-tip effects in STM images (that ignore electron interference effects) may be understood as modeling multiple mini-tip (or tip shape) effects in images that were recorded with other types of SPMs as long as the lateral sample feature sizes to be imaged are much larger than the effective scanning probe tip sizes.

/pdf/removal-of-multiple-tip-artifacts-from-scanning-tunneling-4g2odbt5j8.pdf

Removal of Multiple-Tip Artifacts from Scanning Tunneling Microscope Images by Crystallographic Averaging

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Clarifying multiple-tip effects on Scanning Tunneling Microscopy imaging of 2D periodic objects and crystallographic averaging in the spatial frequency domain

Crystallographic Image Processing Software for Scanning Probe Microscopists

Removal of Multiple-Tip Artifacts from Scanning Tunneling Microscope Images by Crystallographic Averaging