On-the-fly adaptive SNR protocol to accelerate Brillouin microscopy.

This article presents an innovative method for accelerating Brillouin microscopy imaging. The proposed technique, called One-pass, dynamically adjusts the signal-to-noise ratio (SNR) during data acquisition. It identifies essential spectra in real-time and adapts the laser exposure time accordingly.

Structured illumination microscopy with extreme ultraviolet pulses.

The relentless pursuit of understanding matter at ever-finer scales has pushed optical microscopy to surpass the diffraction limit and realize super-resolution microscopy, which enables visualizing structures shorter than the wavelength of the light emitted by the sample. In the present work, we harnessed extreme ultraviolet beams to create sub-μm grating structures, which were revealed by extreme ultraviolet structured illumination microscopy. We establish that the resolution extension is achievable in the extreme ultraviolet, thereby opening the door to significant resolution enhancement, mainly defined by the wavelength employed.

Physics-informed deep neural network for image denoising.

Image enhancement deep neural networks (DNN) can improve signal to noise ratio or resolution of optically collected visual information. The literature reports a variety of approaches with varying effectiveness. All these algorithms rely on arbitrary data (the pixels' count-rate) normalization, making their performance strngly affected by dataset or user-specific data pre-manipulation.

Peptide bond detection via graphene nanogaps: a proof of principle study.

Solid-state nanopores and nanogaps are emerging as promising tools for single molecule analysis. 2D materials, such as graphene, can potentially reach the spatial resolution needed for nucleic acid and protein sequencing. In the context of the density functional theory, atomistic modeling and non-equilibrium Green's function calculation, we show that glycine based polypeptide chains translocating across a nano-gap between two semi-infinite graphene nano-ribbons leave a specific transverse current signature for each peptide bond.

Adsorption of Modified Arg, Lys, Asp, and Gln to Dry and Hydrated ZnO Surface: A Density Functional Theory Study.

The interface of biological molecules with inorganic surfaces has been the subject of several recent studies. Experimentally some amino acids are evidenced to play a critical role in the adhesion and selectivity on oxide surfaces; however, detailed information on how the water molecules on the hydrated surface are able to mediate the adsorption is still missing. Accurate total energy ab initio calculations based on dispersion-corrected density functional theory have been performed to investigate the adsorption of selected amino acids on the hydrated ZnO(101̅0) surface, and the results are presented and discussed in this paper.

Water driven adsorption of amino acids on the (101) anatase TiO₂ surface: an ab initio study.

Arg, Lys and Asp amino acids are known to play a critical role in the adhesion of the RKLPDA engineered peptide on the (101) surface of the titania anatase phase. To understand their contribution to peptide adhesion, we have considered the relevant charge states due to protonation (Arg and Lys) or deprotonation (Asp) occurring in neutral water solution, and studied their adsorption on the (101) anatase TiO2 surface by ab initio total energy calculations based on density functional theory. The adsorption configurations on the hydrated surface are compared to those on the dry surface considering also the presence of the hydration shell around amino acid side-chains.