Designing Sustainable Hydrophilic Interfaces via Feature Selection from Molecular Descriptors and Time-Domain Nuclear Magnetic Resonance Relaxation Curves.

Surface modification using hydrophilic polymer coatings is a sustainable approach for preventing membrane clogging due to foulant adhesion to water treatment membranes and reducing membrane-replacement frequency. Typically, both molecular descriptors and time-domain nuclear magnetic resonance (TD-NMR) data, which reveal physicochemical properties and polymer-chain dynamics, respectively, are required to predict the properties and understand the mechanisms of hydrophilic polymer coatings. However, studies on the selection of essential components from high-dimensional data and their application to the prediction of surface properties are scarce.

Inducible transgene expression in CHO cells using an artificial transcriptional activator with estrogen-binding domain.

Biopharmaceuticals, including therapeutic antibodies, are rapidly growing products in the pharmaceutical market. Mammalian cells, such as Chinese hamster ovary (CHO) cells, are widely used as production hosts because recombinant antibodies require complex three-dimensional structures modified with sugar chains. Recombinant protein production using mammalian cells is generally performed with cell growth.

Exploring the Optimal Alloy for Nitrogen Activation by Combining Bayesian Optimization with Density Functional Theory Calculations.

Binary alloy catalysts have the potential to exhibit higher activity than monometallic catalysts in nitrogen activation reactions. However, owing to the multiple possible combinations of metal elements constituting binary alloys, an exhaustive search for the optimal combination is difficult. In this study, we searched for the optimal binary alloy catalyst for nitrogen activation reactions using a combination of Bayesian optimization and density functional theory calculations.

Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity.

The complicated structure-property relationships of materials have recently been described using a methodology of data science that is recognized as the fourth paradigm in materials science. In network polymers or elastomers, the manner of connection of the polymer chains among the crosslinking points has a significant effect on the material properties. In this study, we quantitatively evaluate the structural heterogeneity of elastomers at the mesoscopic scale based on complex network, one of the methods used in data science, to describe the elastic properties.