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Instrumental drift in atomic force microscopy (AFM) remains a critical, largely unaddressed issue that limits tip-sample stability, registration, and the signal-to-noise ratio during imaging. By scattering a laser off the apex of a commercial AFM tip, we locally measured and thereby actively controlled its three-dimensional position above a sample surface to <40 pm (Deltaf = 0.01-10 Hz) in air at room temperature. With this enhanced stability, we overcame the traditional need to scan rapidly while imaging and achieved a 5-fold increase in the image signal-to-noise ratio. Finally, we demonstrated atomic-scale ( approximately 100 pm) tip-sample stability and registration over tens of minutes with a series of AFM images on transparent substrates. The stabilization technique requires low laser power (<1 mW), imparts a minimal perturbation upon the cantilever, and is independent of the tip-sample interaction. This work extends atomic-scale tip-sample control, previously restricted to cryogenic temperatures and ultrahigh vacuum, to a wide range of perturbative operating environments.
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http://dx.doi.org/10.1021/nl803298q | DOI Listing |
J Chem Theory Comput
September 2025
Materials DX Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan.
The quantum mechanics/molecular mechanics (QM/MM) method is a powerful approach for investigating solid surfaces in contact with various types of media, since it allows for flexible modeling of complex interfaces while maintaining an all-atom representation. The mean-field QM/MM method is an average reaction field model within the QM/MM framework. The method addresses the challenges associated with the statistical sampling of interfacial atomic configurations of a medium and enables efficient calculation of free energies.
View Article and Find Full Text PDFLangmuir
September 2025
Department of Materials Science and Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States.
The surfaces of 1D layered lepidocrocite-structured titanates (1DLs) are negatively charged due to an oxygen-to-titanium atomic ratio >2. This, and their layered structure, allow for facile ion exchange and high colloidal stability, demonstrated by ζ-potentials of ≈ -85 mV at their unadjusted pH of ≈10.4.
View Article and Find Full Text PDFACS Nano
September 2025
CINBIO and Departamento de Química Orgánica. Campus Lagoas-Marcosende, Universidade de Vigo, Vigo E-36310, Spain.
Archimedean spirals are architectural motifs that are found in nature. The facial asymmetry of amphiphilic molecules or macromolecules has been a key parameter in the preparation of these well-organized two-dimensional nanostructures in the laboratory. This facial asymmetry is also present in the helical grooves of chiral helical substituted poly(phenylacetylene)s (PPAs) and poly(diphenylacetylene)s (PDPAs), making them excellent candidates for self-assembly into 2D Archimedean nanospirals or nanotoroids.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China.
Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) trained on all-atom (AA) simulations can serve as CG force fields, which have demonstrated success in CG simulations of proteins.
View Article and Find Full Text PDFPhys Chem Chem Phys
September 2025
State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
Selenium, as an important semiconductor material, exhibits significant potential for understanding lattice dynamics and thermoelectric applications through its thermal transport properties. Conventional empirical potentials are often unable to accurately describe the phonon transport properties of selenium crystals, which limits in-depth understanding of their thermal conduction mechanisms. To address this issue, this study developed a high-precision machine learning potential (MLP), with training datasets generated molecular dynamics simulations.
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