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The success of high-speed atomic force microscopy in imaging molecular motors, enzymes and microbes in liquid environments suggests that the technique could be of significant value in a variety of areas of nanotechnology. However, the majority of atomic force microscopy experiments are performed in air, and the tapping-mode detection speed of current high-speed cantilevers is an order of magnitude lower in air than in liquids. Traditional approaches to increasing the imaging rate of atomic force microscopy have involved reducing the size of the cantilever, but further reductions in size will require a fundamental change in the detection method of the microscope. Here, we show that high-speed imaging in air can instead be achieved by changing the cantilever material. We use cantilevers fabricated from polymers, which can mimic the high damping environment of liquids. With this approach, SU-8 polymer cantilevers are developed that have an imaging-in-air detection bandwidth that is 19 times faster than those of conventional cantilevers of similar size, resonance frequency and spring constant.
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http://dx.doi.org/10.1038/nnano.2015.254 | 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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