Machine learning and density functional theory simulation of the electronic structural properties for novel quaternary semiconductors.

In order to accelerate the application of quaternary optoelectronic materials in the field of luminescence, it is crucial to develop new quaternary semiconductor materials with excellent properties. However, faced with vast alternative quaternary semiconductors, traditional trial-and-error methods tend to be laborious and inefficient. Here, we combined machine learning (ML) with density functional theory (DFT) calculation to predict the bandgaps of 2180 quaternary semiconductors, most of which were undeveloped but environmentally friendly. The evaluation coefficient (R2) of the model using a random forest algorithm was up to 0.93 in ML. Four novel quaternary semiconductors with direct bandgaps: Ag2InGaS4, AgZn2InS4, Ag2ZnSnS4, and AgZn2GaS4, were selected from the ML model. Then their electronic structures and optical properties were further verified and studied by DFT calculations, which demonstrated that the four quaternary semiconductors had direct bandgaps, a small effective mass, and a large exciton binding energy and Stokes shift. Our calculation could significantly speed up the discovery of novel optoelectronic semiconductors and has a certain reference value for the study of luminescent materials and devices.

A comprehensive review on 3D tissue models: Biofabrication technologies and preclinical applications.

The limitations of traditional two-dimensional (2D) cultures and animal testing, when it comes to precisely foreseeing the toxicity and clinical effectiveness of potential drug candidates, have resulted in a notable increase in the rate of failure during the process of drug discovery and development. Three-dimensional (3D) in-vitro models have arisen as substitute platforms with the capacity to accurately depict in-vivo conditions and increasing the predictivity of clinical effects and toxicity of drug candidates. It has been found that 3D models can accurately represent complex tissue structure of human body and can be used for a wide range of disease modeling purposes. Recently, substantial progress in biomedicine, materials and engineering have been made to fabricate various 3D in-vitro models, which have been exhibited better disease progression predictivity and drug effects than convention models, suggesting a promising direction in pharmaceutics. This comprehensive review highlights the recent developments in 3D in-vitro tissue models for preclinical applications including drug screening and disease modeling targeting multiple organs and tissues, like liver, bone, gastrointestinal tract, kidney, heart, brain, and cartilage. We discuss current strategies for fabricating 3D models for specific organs with their strengths and pitfalls. We expand future considerations for establishing a physiologically-relevant microenvironment for growing 3D models and also provide readers with a perspective on intellectual property, industry, and regulatory landscape.

Adsorption behavior and mechanism of p-arsanilic acid on a Fe-based metal-organic framework.

HYPOTHESIS: Organic arsenic pollutant p-arsanilic acid (p-ASA) in wastewater can be converted into highly toxic inorganic arsenic under natural conditions, causing serious harm to the environment and human health. In this study, an Fe-based metal-organic framework (MOF) material, activated MIL-88A, was synthesized as an adsorbent to remove p-ASA in water. EXPERIMENTS: Various influencing factors in the material synthesis process, including temperature, time, solution, and annealing process, were investigated to obtain the optimal reaction conditions. The synthesized activated MIL-88A had great porosity and excellent adsorption capacity for p-ASA in a wide pH range (3 âˆ¼ 10). When the pH of the solution was 6, the activated MIL-88A achieved a great adsorption capacity of 813 mg·g-1 for the p-ASA solution with an initial concentration of 0.334 mmol·L-1. In addition, it still had excellent adsorption capacity after 4 times of repeated usage and washing. FINDINGS: The adsorption kinetics of p-ASA on the activated MIL-88A followed the pseudo-second-order models, and the adsorption isotherms can be fitted by the Langmuir models well. The adsorption behavior was spontaneous and endothermic, and was dominated by Fe-O-As coordination and hydrogen bonding.