Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials.

Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms.

An Insight into Surface Topographical Parameters and Bacterial Adhesion: A Case Study of Listeria monocytogenes Scott A Attachment on 304 Stainless Steel.

Abstract: Bacterial attachment on surfaces is an important biological and industrial concern. Many parameters affect cell attachment behavior, including surface roughness and other topographical features. An understanding of these relationships is critical in the light of recent outbreaks caused by foodborne bacteria.

Capture and detection of T7 bacteriophages on a nanostructured interface.

A highly ordered array of T7 bacteriophages was created by the electrophoretic capture of phages onto a nanostructured array with wells that accommodated the phages. Electrophoresis of bacteriophages was achieved by applying a positive potential on an indium tin oxide electrode at the bottom of the nanowells. Nanoscale arrays of phages with different surface densities were obtained by changing the electric field applied to the bottom of the nanowells.