Atomic force microscopy articles from across Nature Portfolio

Using a deep neural network and statistical analyses of atomic force microscopy images of individual RNA molecules enables the mapping of RNA conformational space in solution.

HORNET, a method that uses unsupervised machine learning and deep neural networks to analyse atomic force microscopy data enables structural determination of RNA molecules in multiple conformations.

Using AFM and cryo-EM, the authors investigate how ESCRT-III-like protein Vipp1 transitions between polymorphic forms of planar sheets and helical filaments, providing insights into membrane-repair processes.

An article in Communications Engineering presents a technique for the 3D reconstruction of nanoscale objects that uses atomic force microscopy and a computer vision algorithm.

Atomic force microscopy (AFM) is coupled with time-domain two-dimensional infrared (2DIR) spectroscopy to develop AFM-2DIR nanospectroscopy, which combines the spatial precision of AFM with the rich spectroscopic information provided by 2DIR spectroscopy. Application of this method reveals the anharmonicity of a carbonyl vibrational mode and the possible energy transfer pathways of hyperbolic phonon polaritons in isotope-rich hexagonal boron nitride.

Advances in atomic force microscopy (AFM) techniques and methodologies for microbiology contribute to our understanding of the microbial cell surface. Recent studies show that AFM imaging of cells and membranes at (near) molecular resolution allows detailed visualization of membranes interacting with drugs.

An article in Nature Communications reports a 3D nanoprinting platform that combines a microfluidic atomic force microscope to precisely deposit tiny amounts of polymer ink and surface-initiated crosslinking chemistry to solidify the ink immediately upon delivery.

Applying principles from super-resolution fluorescence microscopy increases the resolution of atomic force microscopy.