Antibacterial Activities of Graphene Oxide-Molybdenum Disulfide Nanocomposite Films

Polymer Nanosheets from Supramolecular Assemblies of Conjugated Linoleic Acid-High Surface Area Adsorbents from Renewable Materials

We present a strategy for robustly cross-linking self-assembled lamellar mesophases made from plant-derived materials to generate polymer nanosheets decorated with a high density of functional groups. We formulate a supramoleclar complex by hydrogen-bonding conjugated linoleic acid moieties to a structure-directing tribasic aromatic core. The resulting constructs self-assemble into a thermotropic lamellar mesophase. Photo-cross-linking the mesophase with the aid of an acrylate cross-linker yields a polymeric material with high-fidelity retention of the lamellar mesophase structure. Transmission electron microscopy images demonstrate the preservation of the large area, highly ordered layered nanostructures in the polymer. Subsequent extraction of the tribasic core and neutralization of the carboxyl groups by NaOH result in exfoliation of polymer nanosheets with a uniform thickness of ∼3 nm. The nanosheets have a large specific area of ∼800 m²/g, are decorated by negatively charged carboxylate groups at a density of 4 nm^-2, and exhibit the ability to readily adsorb positively charged colloidal particles. The strategy as presented combines supramolecular self-assembly with the use of renewable or sustainably derived materials in a scalable manner. The resulting nanosheets have potential for use as adsorbents and, with further development, rheology modifiers.

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

Membrane destruction-mediated antibacterial activity of tungsten disulfide (WS2)

Tungsten disulfide (WS₂) demonstrates clear antibacterial activity through inducing mechanical damage to the bacteria membrane integrity.