Design of New Inorganic Crystals with the Desired Composition Using Deep Learning.

New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new materials. Crystal generation with deep learning has been applied in various methods to discover new crystals. However, most generative models can only be applied to materials with specific elements or generate structures with random compositions. In this work, we developed a model that can generate crystals with desired compositions based on a crystal diffusion variational autoencoder. We generated crystal structures for 14 compositions of three types of materials in different applications. The generated structures were further stabilized using DFT calculations. We found the most stable structures in the existing database for all but one composition, even though eight compositions among them were not in the data set trained in a crystal diffusion variational autoencoder. This substantiates the prospect of the generation of an extensive range of compositions. Finally, 205 unique new crystal materials with energy above hull <100 meV/atom were generated. Moreover, we compared the average formation energy of the crystals generated from five compositions, two of which were hypothetical, with that of traditional methods like atom substitution and a generative model. The generated structures had lower formation energy than those of other models, except for one composition. These results demonstrate that our approach can be applied stably in various fields to design stable inorganic materials based on machine learning.

Determination of Gas Permeation Properties in Polymer Using Capacitive Electrode Sensors.

The objective of this work was to develop an effective technique for characterizing the permeation properties of various gases, including H2, He, N2, and Ar, that are absorbed in polymers. Simultaneous three-channel real-time techniques for measuring the sorption content and diffusivity of gases emitted from polymers are developed after exposure to high pressure and the subsequent decompression of the corresponding gas. These techniques are based on the volumetric measurement of released gas combined with the capacitance measurement of the water content by both semi-cylindrical and coaxial-cylindrical electrodes. This minimizes the uncertainty due to the varying temperature and pressure of laboratory environments. The gas uptake and diffusivity are determined as a function of the exposed pressure and gas spices in nitrile butadiene rubber (NBR) and ethylene propylene diene monomer (EPDM) polymers. The pressure-dependent gas transport behaviors of four different gases are presented and compared with those obtained by different techniques. A linear correlation between the logarithmic diffusivity and kinetic diameter of molecules in the gas is found between the two polymers.

Permanent magnet actuation for magnetic bead-based DNA extraction.

BACKGROUND: Recently, automatic molecular diagnostic devices to extract DNA have been extensively developed using magnetic beads. While various methods can be applied to the control of the beads, the efficiency of the control when incorporated in automatic devices has not been studied. This paper proposes a compact magnet actuation method for the control of magnetic beads for DNA extraction, and compares the efficiency to the already available magnetic bead-based DNA extraction device. A permanent magnet was preferred for its compactness, while an electro-magnet provides easy operation. After investigating various methods to actuate the magnet with perspective to the size, circuit complexity, and power requirement, we determined the solenoid actuation method to be most efficient. To further reduce the dimension of the overall actuation device, direct actuation of the permanent magnet to control the hold/release of the beads was employed in this paper. The proposed method was compared with the conventional solenoid actuator with a metal plunger. An experimental fluidics device was set up with a fluidic channel and a syringe pump. The bead holding performance against the fluid speed was tested while a fixed amount of beads was loaded into the center of the channel. The group velocity of the beads was analyzed via image processing to determine whether the magnet was sufficient to hold the beads. The required power and space was analyzed and compared qualitatively and quantitatively. RESULT: The proposed direct actuation method was capable of holding the beads at faster fluidic speed than the conventional solenoid actuator. The required power was comparable contemplating the high initial power of the solenoid actuator, and required much smaller space since no plunger was needed. CONCLUSIONS: The direct actuation of the permanent magnet using a solenoid coil showed enhanced performance in holding the beads via permanent magnet, with less complexity of the actuation circuit and space. The proposed method therefore can efficiently improve the overall performance of the bead-based DNA extraction.

High resolution metabolomics to discriminate compounds in serum of male lung cancer patients in South Korea.

BACKGROUND: The cancer death rate escalated during 20th century. In South Korea, lung cancer is expected to contribute 12,736 deaths in men, the highest amount among all cancers. Several risk factors may increase the chance to acquiring lung cancer, with mostly related to exogenous compounds found in cigarette smoke and synthetic manufacturing materials. As the mortality rate of lung cancer increases, deeper understanding is necessary to explore risk factors that may lead to this malignancy. In this regard, this study aims to apply high resolution metabolomics (HRM) using LC-MS to detect significant compounds that might contribute in inducing lung cancer and find the correlation of these compounds to the subjects' smoking habit. METHODS: The comparison was made between healthy control and lung cancer groups for metabolic differences. Further analyses to determine if these differences are related to tobacco-induced lung cancer (past-smoker control vs. past-smoker lung cancer patients (LCPs) and non-smoker control vs. current-smoker LCPs) were selected. The univariate analysis was performed, including a false discovery rate (FDR) of q = 0.05, to determine the significant metabolites between the analyses. Hierarchical clustering analysis (HCA) was done to discriminate metabolites between the control and case subjects. Selected compounds based on significant m/z features of human serum then experienced MS/MS examination, showing that for many m/z, the patterns of ion dissociation matched with standards. Then, the significant metabolites were identified using Metlin database and features were mapped on the human metabolic pathway mapping tool of the Kyoto Encyclopedia of Genes and Genomes (KEGG). RESULTS: Using metabolomics-wide association studies, metabolic changes were observed among control group and lung cancer patients. Bisphenol A (211.11, [M + H-H2O](+)), retinol (287.23, [M + H](+)) and L-proline (116.07, [M + H](+)) were among the significant compounds found to have contributed in the discrimination between these groups, suggesting that these compounds might be related in the development of lung cancer. Retinol has been seen to have a correlation with smoking while both bisphenol A and L-proline were found to be unrelated. CONCLUSIONS: Two potential biomarkers, retinol and L-proline, were identified and these findings may create opportunities for the development of new lung cancer diagnostic tools.

Electrostatics and depletion determine competition between 2D nematic and 3D bundled phases of rod-like DNA nanotubes.

Rod-like particles form solutions of technological and biological importance. In particular, biofilaments such as actin and microtubules are known to form a variety of phases, both in vivo and in vitro, whose appearance can be controlled by depletion, confinement, and electrostatic interactions. Here, we utilize DNA nanotubes to undertake a comprehensive study of the effects of those interactions on two particular rod-like phases: a 2D nematic phase consisting of aligned rods pressed against a glass surface, and a 3D bundled network phase. We experimentally measure the stability of these two phases over a range of depletant concentrations and ionic strengths, finding that the 2D phase is slightly more stable than the 3D phase. We formulate a quantitative model of phase stability based on consideration of pairwise rod-rod and rod-surface interactions; notably, we include a careful accounting of solution electrostatics interactions using an effective-charge strategy. The model is relatively simple and contains no free parameters, yet predicts phase boundaries in good agreement with the experiment. Our results indicate that electrostatic interactions, rather than depletion, are largely responsible for the enhanced stability of the 2D phase. This work provides insight into the polymorphism of rod-like solutions, indicating why certain phases appear, and providing a means (and a predictive model) for controlling those phases.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium-sulfur batteries.

During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery's electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte, [Formula: see text] in the presence of polysulfide. However, obtaining [Formula: see text] of the diverse mixtures of components in the electrolyte is a complex and expensive task that shows itself as a bottleneck in optimization of electrolytes. In this work, we present a machine-learning approach for predicting [Formula: see text] of electrolytes. The proposed architecture utilizes (1) an attention-based model (Attentive FP), a contrastive learning model (MolCLR) or morgan fingerprints to represent chemical components, and (2) transformers to account for the interactions between chemicals in the electrolyte. This architecture was not only capable of predicting electrolyte properties, including those of chemicals not used during training, but also providing insights into chemical interactions within electrolytes. It revealed that interactions with other chemicals relate to the logP and molecular weight of the chemicals.

A thin permeable-membrane device for single-molecule manipulation.

Single-molecule manipulation instruments have unparalleled abilities to interrogate the structure and elasticity of single biomolecules. Key insights are derived by measuring the system response in varying solution conditions; yet, typical solution control strategies require imposing a direct fluid flow on the measured biomolecule that perturbs the high-sensitivity measurement and/or removes interacting molecules by advection. An alternate approach is to fabricate devices that permit solution changes by diffusion of the introduced species through permeable membranes, rather than by direct solution flow through the sensing region. Prior implementations of permeable-membrane devices are relatively thick, disallowing their use in apparatus that require the simultaneous close approach of external instrumentation from two sides, as occurs in single-molecule manipulation devices like the magnetic tweezer. Here, we describe the construction and use of a thin microfluidic device appropriate for single-molecule studies. We create a flow cell of only ∼500 µm total thickness by sandwiching glass coverslips around a thin plastic gasket and then create permeable walls between laterally separated channels in situ through photo-induced cross-linking of poly(ethylene glycol) diacrylate hydrogels. We show that these membranes permit passage of ions and small molecules (thus permitting solution equilibration in the absence of direct flow), but the membranes block the passage of larger biomolecules (thus retaining precious samples). Finally, we demonstrate the suitability of the device for high-resolution magnetic-tweezer experiments by measuring the salt-dependent folding of a single RNA hairpin under force.

Leeia oryzae gen. nov., sp. nov., isolated from a rice field in Korea.

A strictly aerobic, non-spore-forming, Gram-negative bacterium, designated strain HW7(T), was isolated from a rice field in Korea. Cells of strain HW7(T) were short rod-shaped and motile with single polar flagella. The major cellular fatty acids of strain HW7(T) were C(16 : 0) and summed feature 3 (C(16 : 1)omega7c and/or iso-C(15 : 0) 2-OH). The genomic DNA G+C content was 56 mol% and the major isoprenoid quinone was Q-8. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain HW7(T) forms a distinct lineage with respect to closely related genera within the class Betaproteobacteria and that the levels of 16S rRNA gene sequence similarity with respect to the type species of related genera are less than 93 %. On the basis of the physiological and phylogenetic data, strain HW7(T) represents a novel genus and species of the Betaproteobacteria, for which the name Leeia oryzae gen. nov., sp. nov. is proposed. The type strain is HW7(T) (=KCTC 12585(T)=DSM 17879(T)).

Comparison of genotyping Helicobacter pylori directly from biopsy specimens and genotyping from bacterial cultures.

PCR for vacA and cagA genotypes of Helicobacter pylori using DNA isolated from infected gastric biopsy specimens was approximately equal to genotyping using bacterial DNA from cultures. Inconsistent results were associated with low H. pylori density in biopsies. A higher proportion of mixed infection was found when biopsies were used.

New serological assay for detection of putative Helicobacter pylori virulence factors.

We evaluated a new immunoblot assay (Helicoblot 2.1) for Helicobacter pylori infection and CagA and VacA status by using serum samples from 222 patients. The test accurately detected H. pylori infection and VacA status, but improvements in the interpretation criteria are required before it can be recommended for determination of CagA status.