Structure Effects of Ligands in Gold-Ligand Complexes for Controlled Formation of Gold Nanoclusters.

Metal nanoclusters (NCs) are a special class of nanoparticles composed of a precise number of metal atoms and ligands. Because the proportion of ligands to metal atoms is high in metal NCs, the ligand type determines the physical properties of metal NCs. Furthermore, ligands presumably govern the entire formation process of the metal NCs. However, their roles in the synthesis, especially as factors in the uniformity of metal NCs, are not understood. It is because the synthetic procedure of metal NCs is highly convoluted. The synthesis is initiated by the formation of various metal-ligand complexes, which have different numbers of atoms and ligands, resulting in different coordinations of metal. Moreover, these complexes, as actual precursors to metal NCs, undergo sequential transformations into a series of intermediate NCs before the formation of the desired NCs. Thus, to resolve the complicated synthesis of metal NCs and achieve their uniformity, it is important to investigate the reactivity of the complexes. Herein, we utilize a combination of mass spectrometry, density functional theory, and electrochemical measurements to understand the ligand effects on the reactivity of AuI-thiolate complexes toward the reductive formation of Au NCs. We discover that the stability of the complexes can be increased by either van der Waals interactions induced by the long carbon chain of ligands or by non-thiol functional groups in the ligands, which additionally coordinate with AuI in the complexes. Such structural effects of thiol ligands determine the reduction reactivity of the complexes and the amount of NaBH4 required for the controlled synthesis of the Au NCs.

Additive-Driven Nanoscale Architecture of Solid Electrolyte Interphase Revealed by Cryogenic Transmission Electron Microscopy.

In Li metal batteries (LMBs), which boast the highest theoretical capacity, the chemical structure of the solid electrolyte interphase (SEI) serves as the key component that governs the growth of reactive Li. Various types of additives have been developed for electrolyte optimization, representing one of the most effective strategies to enhance the SEI properties for stable Li plating. However, as advanced electrolyte systems become more chemically complicated, the use of additives is empirically optimized. Indeed, their role in SEI formation and the resulting cycle life of LMBs are not well-understood. In this study, we employed cryogenic transmission electron microscopy combined with Raman spectroscopy, theoretical studies including molecular dynamics (MD) simulations and density functional theory (DFT) calculations, and electrochemical measurements to explore the nanoscale architecture of SEI modified by the most representative additives, lithium nitrate (LiNO3) and vinylene carbonate (VC), applied in a localized high-concentration electrolyte. We found that LiNO3 and VC play distinct roles in forming the SEI, governing the solvation structure, and influencing the kinetics of electrochemical reduction. Their collaboration leads to the desired SEI, ensuring prolonged cycle performance for LMBs. Moreover, we propose mechanisms for different Li growth and cycling behaviors that are determined by the physicochemical properties of SEI, such as uniformity, elasticity, and ionic conductivity. Our findings provide critical insights into the appropriate use of additives, particularly regarding their chemical compatibility.

In situ multiscale probing of the synthesis of a Ni-rich layered oxide cathode reveals reaction heterogeneity driven by competing kinetic pathways.

Nickel-rich layered oxides are envisaged as key near-future cathode materials for high-energy lithium-ion batteries. However, their practical application has been hindered by their inferior cycle stability, which originates from chemo-mechanical failures. Here we probe the solid-state synthesis of LiNi0.6Co0.2Mn0.2O2 in real time to better understand the structural and/or morphological changes during phase evolution. Multi-length-scale observations-using aberration-corrected transmission electron microscopy, in situ heating transmission electron microscopy and in situ X-ray diffraction-reveal that the overall synthesis is governed by the kinetic competition between the intrinsic thermal decomposition of the precursor at the core and the topotactic lithiation near the interface, which results in spatially heterogeneous intermediates. The thermal decomposition leads to the formation of intergranular voids and intragranular nanopores that are detrimental to cycling stability. Furthermore, we demonstrate that promoting topotactic lithiation during synthesis can mitigate the generation of defective structures and effectively suppress the chemo-mechanical failures.

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update.

The recent terrorist attacks using Novichok agents and subsequent operations have necessitated an understanding of its physicochemical properties, such as vapor pressure and toxicity, as well as unascertained nerve agent structures. To prevent continued threats from new types of nerve agents, the organization for the prohibition of chemical weapons (OPCW) updated the chemical weapons convention (CWC) schedule 1 list. However, this information is vague and may encompass more than 10 000 possible chemical structures, which makes it almost impossible to synthesize and measure their properties and toxicity. To assist this effort, we successfully developed machine learning (ML) models to predict the vapor pressure to help with escape and removal operations. The model shows robust and high-accuracy performance with promising features for predicting vapor pressure when applied to Novichok materials and accurate predictions with reasonable errors. The ML classification model was successfully built for the swallow globally harmonized system class of organophosphorus compounds (OP) for toxicity predictions. The tuned ML model was used to predict the toxicity of Novichok agents, as described in the CWC list. Although its accuracy and linearity can be improved, this ML model is expected to be a firm basis for developing more accurate models for predicting the vapor pressure and toxicity of nerve agents in the future to help handle future terror attacks with unknown nerve agents.

Assessment of various DFT, DFT-D, and MP2 methods for studying FOX-7 detonation properties.

B3LYP, PBE, M06-2X, B2PLYP, BN2PLYP-D, ωB97X-D, and MP2 levels of theory, in combination with the 6-311++G(d,p) and cc-pVTZ basis sets were comprehensively assessed for their ability to reproduce experimental FOX-7 structural and detonation data. ωB97X-D/cc-pVTZ, B3LYP/cc-pVTZ, and M06-2X/cc-pVTZ provided highly accurate optimized structure predictions. M06-2X/cc-pVTZ and ωB97X-D/cc-pVTZ reproduced experimentally determined detonation properties (detonation velocity and detonation pressure) with high accuracy. The results of this study demonstrate that more accurate structure calculation levels provide more reliable detonation property predictions. Moreover, the results show that detonation property prediction is largely dependent on the calculation level. This investigation demonstrates that using a wide range of calculation levels enables the reliable prediction and modeling of novel types of HEDMs.