Chemphyschem最新文献

Sulfide solid electrolytes have potential in practical all-solid-state batteries owing to their high formability and ionic conductivity. However, sulfide solid electrolytes are limited by the generation of toxic hydrogen sulfide and conductivity deterioration upon moisture exposure. Although numerous studies have investigated the hydrolysis degradation induced by "moisture," the influence of "atmospheric gases" during moisture exposure has not been extensively investigated despite the importance for practical fabrication. Therefore, in this study, we investigated the impact of atmospheric gases during moisture exposure on an argyrodite-type Li6PS5Cl via electrochemical impedance spectroscopy, X-ray diffraction, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. The electrolyte powder was exposed to various atmospheric gases, namely Ar, Ar + 500 ppm CO2, O2, and O2 + 500 ppm CO2, with moisture at a dew point of -20 °C, and H2S gas generation was monitored. As a result, the amount of H2S gas did not depend on the atmospheric gases. However, the atmospheric gases had a significant effect on the decrease in conductivity. Spectroscopic analyses revealed that CO2 facilitates the formation of carbonates and that O2 promotes the formation of phosphates and sulfonates. The formation of these compounds leads to surface degradation, which further decreases the conductivity.

The emergence of new SARS-CoV-2 variants of concern (VOC) is a propulsion for accelerated potential therapeutic discovery. SARS-CoV-2's main protease (Mpro), essential for host cell viral replication, is a pre-eminent druggable protein target. Here, we perform extensive drug re-profiling of the comprehensive Excelra database, which compiles various under-trial drug candidates for COVID-19 treatment. For mechanistic understanding, the most promising screened-out molecules with targets are subjected to molecular dynamics (MD) simulations. Post-MD analyses demonstrate Darunavir, Ponatinib, and Tomivosertib forming a stable complex with Mpro, characterized by less fluctuation of Cα atoms, smooth and stable root-mean-square deviation (RMSD), and robust contact with the active site residues. Likewise, they all have lower binding free energy with Mpro, demonstrating strong affinity. In free energy landscape profiles, the distances from His41 and Cys145 exhibit a single energy minima basin, implying their preponderance in proximity to Mpro's catalytic dyad. Overall, the computational assessment earmarks promising candidates from the Excelra database, emphasizing on carrying out exhaustive biochemical experiments along with clinical trials. The work lays the foundation for potential therapeutic interventions in treating COVID-19.

Manganese-based compounds have the characteristics of high theoretical capacity, low cost and stable performance, thus become a research hotspot for cathode materials of zinc-ion batteries (ZIBs). However, in the process of charging and discharging, it is accompanied by problems such as structural collapse and low conductivity, which resulted in severe capacity degration during cycles. In this paper, a kind of Zn2+ doped MnO2 hollow cube cathode material (Zn-MnO2) was prepared by self-sacrificing template method. The Zn2+ doped in MnO2 crystals can induce oxygen vacancies in the structure, thereby improving the structural stability ion diffusion coefficient and electrical conductivity of the material. After 100 cycles at 0.3 A g-1, the high specific capacity of 281.2 mA h g-1 is still maintained. Through ex-situ XPS and ex-situ XRD tests, the mechanism of charge-discharge process was discussed. The results show that the storage mechanism of Zn-MnO2 is H+ and Zn2+ insertion/removal and Mn3+/Mn2+ two-electron reaction pathway. The total state density (TDOS) and partial state density (PDOS) of Zn-MnO2 and MnO2 further demonstrated that the doping of Zn2+ enhanced the electron conductivity and is beneficial to the electron transfer during the electrochemical reaction.

The conjoint application of the voltammetry of immobilized particles (VIMP) methodology and the Mott-Schottky analysis (MS) of impedance data to studying metal corrosion patinas is described. The study is applied to copper and bronze objects exploiting the semiconducting character of cuprite and other copper corrosion products. A simplified theoretical modeling of MS analysis at microparticulate deposits extracted from metal corrosion layers attached to graphite electrodes is provided. The proposed model compensates for the disturbing effect of the regions of the basal electrode directly exposed to the electrolyte. Alternative models accounting for the variation of the density of charge carriers with depth are tested as well as the correlation between VIMP and MS data with reasonably satisfactory results.

Transition metal-based electrocatalytic materials for hydrogen production through water splitting offer advantages in terms of price and availability compared to noble metal-based catalysts, among which, Fe-, Co-, and Ni-based compounds are the most typical and widely studied materials. Utilizing the synergistic effects between composite components in compounds containing multiple metal elements is an important way to improve the catalytic performance of catalysts, so developing ternary or multiple active center catalysts containing Fe, Co, and Ni is a promising direction. In this mini-review, we provide an summary of the latest achievements of water splitting catalyst materials simultaneously containing Fe, Co, and Ni elements. It was summarized according to several groups including compounds of boron-/carbon-/nitrogen-/phosphorus-/oxygen-group elements, metal-organic framework-based compounds, and compounds in situ grown from alloy matrix. Also challenges that need to be addressed are presented at the end of the article.

A systematic investigation of the aromatic features of the electronic structures of a family of recently synthesized osmapentalene derivatives has been carried by means of indices derived from the calculated one-electron density matrix of the corresponding geometry optimized compounds, and complemented by the analysis of the valence molecular orbitals and the delocalized bonding units emerging from the adaptive natural density partitioning method. The calculated delocalization indices between consecutive atom pairs, and normalized multicenter indices are very suggestive of the aromatic character of the equatorial fused carbon rings (except triangular ones) for all the members of the family. Since the electron-delocalization based indices allow precise quantification of the aromaticity, differences of the aromatic character among the various members have also been highlighted, and have been found to be consistent with the magnetic based criteria indices reported earlier. Finally, the valence molecular orbitals along with the delocalized bonding units of the adaptive natural density partitioning indicate that the aromaticity of these compounds is sustained by either 10 or 14 π electrons, which satisfy the Hückel aromatic electron counting rule.

Molecular dynamics (MD) simulations are a powerful tool for life sciences, valuable for their ability to capture atomic-level behavior of molecules over time. To advance knowledge on reasonable timescales, researchers must optimize the amount of useful information extracted from simulation data while frugally managing computational resources. They must balance trajectory lengths and number of replicas, with the aim of maximizing conformational sampling. Identifying this balance is not always intuitive, and lack of standardization among researchers produces variability in results from MD measurements. We investigate how changes in simulation length and replica numbers impact this variability. Using a 231-residue domain, we compare measurements from independent trajectories to a benchmark trajectory of 3x1000-ns replicates. We simulate 27 protein-ligand complexes, allowing us to compare ligand-specific rankings of complexes across replicas. We reveal that some MD measurements are reliably ranked by single trajectories, while others are not. We uncover similar variability in the effects of trajectory lengths on measurements. Our findings suggest that a one-size-fits-all approach to MD simulations is not ideal, and depending on the intended measurements and research question, it is sometimes advantageous to prioritize longer trajectories over multiple replicas. This work provides important considerations for researchers while designing simulation studies.

DNA nanotechnology has emerged as a groundbreaking field, using DNA as a scaffold to create nanostructures with customizable properties. These DNA nanostructures hold potential across various domains, from biomedicine to studying ionizing radiation-matter interactions at the nanoscale. This review explores how the various types of radiation, covering a spectrum from electrons and photons at sub-excitation energies to ion beams with high-linear energy transfer influence the structural integrity of DNA origami nanostructures. We discuss both direct effects and those mediated by secondary species like low-energy electrons (LEEs) and reactive oxygen species (ROS). Further we discuss the possibilities for applying radiation in modulating and controlling structural changes. Based on experimental insights, we identify current challenges in characterizing the responses of DNA nanostructures to radiation and outline further areas for investigation. This review not only clarifies the complex dynamics between ionizing radiation and DNA origami but also suggests new strategies for designing DNA nanostructures optimized for applications exposed to various qualities of ionizing radiation and their resulting byproducts.

A holistic model for predicting yield and linear selectivity for the hydroformylation of 1-octene was developed by machine learning using the experimental data collected from literatures. Physical organic chemistry (POC) parameter-based descriptors were adopted to represent pre-catalyst molecular features. Machine learning models trained respectively by Random Forests (RF) and Extreme Gradient Boost (XGBoost) algorithm showed remarkable performance on predicting linear selectivity. The method can also comprehensively map the correlation between reaction conditions and the results. The accuracy of the prediction results was verified by experimental data.

We study the formation of hybrid polymer/inorganic colloidal particles with multicompartmentalized structure, comprising gold nanoparticles grafted with polystyrene-block-poly(methacrylic acid) (PSt-block-PMAA) diblock copolymer ligands, through experiments and molecular dynamics simulations. The PMAA blocks segregate into small satellite-like domains that are separated by the polystyrene spacer from the gold nanoparticle core. Dialysis against water leads to the re-configuration of the formed structures into unique, kinetically trapped pinned-micelle-decorated nanoparticles with internal structure.