Kelvin probe force microscopy (KPFM) allows the detection of single binding events between immunoglobulins (IgM, IgG) and their cognate antibodies (anti-IgM, anti-IgG). Here an insight into the reliability and robustness of the methodology is provided. Our method is based on imaging the surface potential shift occurring on a dense layer of ∼5 × 107 antibodies physisorbed on a 50 μm × 90 μm area when assayed with increasing concentrations of antigens in phosphate buffer saline (PBS) standard solutions, in air and at a fixed scanning location. A comprehensive investigation of the influence of the main experimental parameters that may interfere with the outcomes of KPFM immune-assay is provided, showing the robustness and reliability of our approach. The data are supported also by a thorough polarization modulation infrared reflection-absorption spectroscopy (PM-IRRAS) analysis of the physisorbed biolayer, in the spectral region of the amide I, amide II and amide A bands. Our findings demonstrate that a 10 min incubation in 500 μL PBS encompassing ≈ 30 antigens (100 zM) triggers an extended surface potential shift that involves the whole investigated area. Such a shift quickly saturates at increasing ligand concentration, showing that the developed sensing platform works as an OFF/ON detector, capable of assessing the presence of a few specific biomarkers in a given assay volume. The reliability of the developed methodology KPFM is an important asset in single molecule detections at a wide electrode interface.

In this work, we show how the structural features of photoactive azobenzene derivatives can influence the photoexcited state behavior and the yield of the trans/cis photoisomerization process. By combining high-resolution transient absorption experiments in the vis-NIR region and quantum chemistry calculations (TDDFT and RASPT2), we address the origin of the transient signals of three poly-substituted push-pull azobenzenes with an increasing strength of the intramolecular interactions stabilizing the planar trans isomer (absence of intramolecular H-bonds, methyl, and traditional H-bond, respectively, for 4-diethyl-4'-nitroazobenzene, Disperse Blue 366, and Disperse Blue 165) and a commercial red dye showing keto-enol tautomerism involving the azo group (Sudan Red G). Our results indicate that the intramolecular H-bonds can act as a "molecular lock" stabilizing the trans isomer and increasing the energy barrier along the photoreactive CNNC torsion coordinate, thus preventing photoisomerization in the Disperse Blue dyes. In contrast, the involvement of the azo group in keto-enol tautomerism can be employed as a strategy to change the nature of the lower excited state and remove the nonproductive symmetric CNN/NNC bending pathway typical of the azo group, thus favoring the productive torsional motion. Taken together, our results can provide guidelines for the structural design of azobenzene-based photoswitches with a tunable excited state behavior.

Herein, an innovative deep‐learning architecture is proposed to enhance the sensing capabilities of a microelectromechanical system (MEMS) used in fluid dynamic applications. The MEMS sensor comprises a polyvinylidene fluoride flexible (PVDF) piezoelectric flag and a bluff body, with vortex generation influenced not only by the bluff body's geometry but also by the input fluid speed. As a result, mechanical vibrations are induced in the piezoelectric flag, leading to charge displacement and the generation of electrical voltage signals. Through the developed deep learning method, accurate extraction of wind speed and successful classification of turbulence are achieved. Experimental tests in a wind tunnel, involving various wind speeds and bluff body geometries, demonstrate the robust correlation between the extracted continuous manifold in Fourier spectra and wind speed. By incorporating a feed‐forward network alongside the autoencoder, wind speed information even under strong turbulence is extracted. Moreover, the deep learning method's ability to classify different bluff bodies, independent of wind speed, is investigated. The findings reveal a unique capability to fingerprint turbulence and distinguish them for various applications. This research showcases the potential of our deep learning‐based MEMS systems for enhancing fluid dynamic sensing and classification tasks.

The detection of aflatoxin B in raw food materials represents a topic of great interest worldwide because of the huge health and economic impact of aflatoxin contamination. In this paper, we present an original approach to aflatoxin detection, using a portable instrument to acquire fluorescence images, among other spectral responses. The acquired images are processed by combining a color space conversion from the RGB scale to Y′CbCr, and a neural network approach to encode a vector of features. After a feature reduction using a Receiving Operating Curve method, two-class and three-class classification tasks of contaminated vs non-contaminated samples are accomplished. This procedure has been applied to artificially contaminated grained almond samples in the range of 0–320.2 ng/g, achieving an overall classification accuracy between 84.7% and 93.0%, depending on the number of classes. Thus, in this setting, we show that good classification performance can be achieved using only an image acquisition and analysis approach. The proposed procedure can represent a cheap, rapid, non-destructive yet sensitive method for the assessment of aflatoxin B contamination in food matrices, and its monitoring and tracing throughout the food chain.

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retention. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the noninvasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Quantum technologies have opened new perspectives for enhancing communication speed and security, particularly in challenging underwater environments, where conventional protocols rely on acoustic wave propagation. In this study, we introduce an innovative communication protocol built upon the utilization of mesoscopic twin-beam (TWB) states, entangled in the number of photons, and photon-number-resolving detectors. Our approach involves transmitting information by mixing the part of TWB that propagates through water with two signals having identical mean values but different statistical distributions. Specifically, we explore the advantages and limitations associated with employing pseudothermal states and two distinct types of superthermal states in our communication protocol. Through both theoretical analysis and experimental investigation, we assess the feasibility of accurately discriminating which state has been superimposed onto the TWB by evaluating the noise reduction factor. Our findings demonstrate promising outcomes, suggesting the practical implementation of this protocol in real-world underwater communication scenarios.

Metis on board Solar Orbiter is the space coronagraph developed by an Italian–German–Czech consortium. It is capable of observing solar corona and various coronal structures in the visible-light (VL) and UV (hydrogen Lyα) channels simultaneously for the first time. Here we present observations of a large eruptive prominence on 2021 April 25–26, in the VL, taken during the mission cruise phase, and demonstrate that apart from the broadband continuum emission, which is due to the Thomson scattering on prominence electrons, we detect a significant radiation in the neutral-helium D3 line (587.6 nm), which lies within the Metis VL passband. We show how the prominence looks like in Stokes I, Q, and U. We consider two extreme cases of the prominence magnetic field, and we separate the Stokes I and Q signals pertinent to Thomson scattering and to the D3 line. The degree of linear polarization of the D3 line (both Q and U) indicates the presence of the prominence magnetic field; hence Metis can serve as a magnetograph for eruptive prominences located high in the corona.