Search

Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique. [ABSTRACT FROM AUTHOR]

Understanding the electrical double layer (EDL) structure at the solid/liquid interface is critical towards realizing the full potential of electrochemical applications using ionic liquids. In this work, the out-of-plane and in-plane EDL structures of PYR 14 -TFSI on graphite (HOPG) have been studied by in-situ electrochemical atomic force microscopy (AFM) and molecular dynamics (MD) simulation. AFM results revealed that the first adsorbed ion layer on HOPG consists of both disordered and ordered lateral domains. It has been found that the neighboring molecules in the x-y plane form intricate ordered lateral structures on length scale of hundreds of nanometers, and the out-of-plane EDL structure is independent of the in-plane structure. MD simulations under zero polarization showed that in the first adsorbed layer, cations have one preferred orientation while anions have two preferred orientations in relation to the HOPG surface, which might be the origin of the complex lateral ordering. When polarizing the surface, a hysteretic order-disorder transition of the lateral ordering in the first adsorbed layer can be observed by in-situ AFM, and the ordered domains disappear for high positive or negative voltages. Comparison with bias-dependent MD reveals that the existence of a bimodal anion distribution in the first absorbed layer can be linked to the observed transitions giving new insights into the origin of the structural domains, which can help understand unusual charge and discharge kinetics observed in similar systems. The bias-induced changes in the area coverage of structured ionic liquid at a solid interface are observed by atomic force microscopy and change hysterically. Image 1 • When in contact with graphite, 1st adsorbed PYR 14 -TFSI layer forms structural domains with ordered in-plane structure up to few hundreds of nanometers in size • In-plane structure can be controlled by bias showing a hysteretic order-disorder transition • The bimodal anion distribution could be responsible of the lateral order-disorder transition under bias [ABSTRACT FROM AUTHOR]