Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks.

The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN) architecture to train and model the potential energy surface of molecular clusters relevant to atmospheric new particle formation, such as sulfuric acid-ammonia clusters.

Iodine Clusters in the Atmosphere I: Computational Benchmark and Dimer Formation of Oxyacids and Oxides.

The contribution of iodine-containing compounds to atmospheric new particle formation is still not fully understood, but iodic acid and iodous acid are thought to be significant contributors. While several quantum chemical studies have been carried out on clusters containing iodine, there is no comprehensive benchmark study quantifying the accuracy of the applied methods. Here, we present the first study in a series that investigate the role of iodine species in atmospheric cluster formation.

Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid-multi-base systems.

Atmospheric molecular clusters, the onset of secondary aerosol formation, are a major part of the current uncertainty in modern climate models. Quantum chemical (QC) methods are usually employed in a funneling approach to identify the lowest free energy cluster structures. However, the funneling approach highly depends on the accuracy of low-cost methods to ensure that important low-lying minima are not missed.

On the geometry dependence of the nuclear magnetic resonance chemical shift of mercury in thiolate complexes: A relativistic density functional theory study.

Thiolate containing mercury(II) complexes of the general formula [Hg(SR) ] have been of great interest since the toxicity of mercury was recognized. Hg nuclear magnetic resonance spectroscopy (NMR) is a powerful tool for characterization of mercury complexes. In this work, the Hg shielding constants in a series of [Hg(SR) ] complexes are therefore investigated computationally with particular emphasis on their geometry dependence.

Current and future machine learning approaches for modeling atmospheric cluster formation.

The formation of strongly bound atmospheric molecular clusters is the first step towards forming new aerosol particles. Recent advances in the application of machine learning models open an enormous opportunity for complementing expensive quantum chemical calculations with efficient machine learning predictions. In this Perspective, we present how data-driven approaches can be applied to accelerate cluster configurational sampling, thereby greatly increasing the number of chemically relevant systems that can be covered.

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters.

The nucleation process leading to the formation of new atmospheric particles plays a crucial role in aerosol research. Quantum chemical (QC) calculations can be used to model the early stages of aerosol formation, where atmospheric vapor molecules interact and form stable molecular clusters. However, QC calculations heavily depend on the chosen computational method, and when dealing with large systems, striking a balance between accuracy and computational cost becomes essential.

Design, Preparation, and Performance of a Novel Bilayer Tissue-Engineered Small-Diameter Vascular Graft.

In clinical practice, the need for small-diameter vascular grafts continues to increase. Decellularized xenografts are commonly used for vascular reconstructive procedures. Here, porcine coronary arteries are decellularized, which destroys the extracellular matrix structure, leading to the decrease of vascular strength and the increase of vascular permeability.

Effects of ionic strength on removal of toxic pollutants from aqueous media with multifarious adsorbents: A review.

Adsorption is one of the most widely used and effective wastewater treatment methods. The role of ionic strength (IS) in shaping the adsorption performances is much necessary due to the ubiquity of electrolyte ions in water body and industrial effluents. The influences of IS on adsorption are rather complex, because electrolyte ions affect both adsorption kinetics and thermodynamics by changing the basic characteristics of adsorbents and adsorbates.

Immobilization of heparin/poly-l-lysine microspheres on medical grade high nitrogen nickel-free austenitic stainless steel surface to improve the biocompatibility and suppress thrombosis.

Thrombosis formation, restenosis, and delayed endothelium regeneration continue to be a challenge for coronary artery stent therapy. To improve the hemocompatibility of cardiovascular implants and to selectively direct vascular cell behavior, a novel heparin/poly-l-lysine microsphere was developed and immobilized on a dopamine-coated surface. We chose medical grade high nitrogen nickel-free austenitic stainless steel as the stent material since it has better biocompatibility.

PEG--PCL polymeric nano-micelle inhibits vascular angiogenesis by activating p53-dependent apoptosis in zebrafish.

Micro/nanoparticles could cause adverse effects on cardiovascular system and increase the risk for cardiovascular disease-related events. Nanoparticles prepared from poly(ethylene glycol) (PEG)--poly(-caprolactone) (PCL), namely PEG--PCL, a widely studied biodegradable copolymer, are promising carriers for the drug delivery systems. However, it is unknown whether polymeric PEG--PCL nano-micelles give rise to potential complications of the cardiovascular system.