Signaling Recognition Events with Fluorescent Sensors and Switches.

2.1. Solvents 4307 2.1.1. Propylene Carbonate (PC) 4308 2.1.2. Ethers 4308 2.1.3. Ethylene Carbonate (EC) 4309 2.1.4. Linear Dialkyl Carbonates 4310 2.2. Lithium Salts 4310 2.2.1. Lithium Perchlorate (LiClO4) 4311 2.2.2. Lithium Hexafluoroarsenate (LiAsF6) 4312 2.2.3. Lithium Tetrafluoroborate (LiBF4) 4312 2.2.4. Lithium Trifluoromethanesulfonate (LiTf) 4312 2.2.5. Lithium Bis(trifluoromethanesulfonyl)imide (LiIm) and Its Derivatives 4313

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Nonaqueous liquid electrolytes for lithium-based rechargeable batteries.

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

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The Graph Neural Network Model

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial–temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

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A Comprehensive Survey on Graph Neural Networks

Electrolytes and interphases in Li-ion batteries and beyond.

A review of the literature yielded data on over 200 aromatic and heteroaromatic nitro compounds tested for mutagenicity in the Ames test using S. typhimurium TA98. From the data, a quantitative structure-activity relationship (QSAR) has been derived for 188 congeners. The main determinants of mutagenicity are the hydrophobicity (modeled by octanol/water partition coefficients) and the energies of the lowest unoccupied molecular orbitals calculated using the AM1 method. It is also shown that chemicals possessing three or more fused rings possess much greater mutagenic potency than compounds with one or two fused rings. Since the QSAR is based on a very wide range in structural variation, aromatic rings from benzene to coronene are included as well as many different types of heterocycles, it is a significant step toward a predictive toxicology of value in the design of less mutagenic bioactive compounds.

Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity.

Quantitative structure-activity relationships (QSAR) have been derived for the mutagenic activity of 88 aromatic and heteroaromatic amines acting on Salmonella typhimurium TA98 + S9 and 67 amines acting on TA100 + S9. Mutagenic activity is linearly dependent on hydrophobicity, the energy of the highest occupied molecular orbital, and the energy of the lowest unoccupied molecular orbital of the amine. The dependence of mutagenic activity on hydrophobicity and electronic effects is nearly identical for TA98 and TA100. Mutagenic activity in TA98 is also found to depend on the size of the aromatic ring system. Different QSARs are derived for the mutagenic activity of hydrophilic amines (log P < 1) acting on either TA98 or TA100. The mechanism of amine activation and reaction with DNA is considered in light of these findings.

A QSAR investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 1. Mutagenicity of aromatic and heteroaromatic amines in Salmonella typhimurium TA98 and TA100.

Reactions of benzylidene pentacarbonyltungsten with alkenes

A quantitative structure-activity relationship (QSAR) has been derived for the mutagenic activity of 117 aromatic and heteroaromatic nitro compounds acting on Salmonella typhimurium TA100. Relative mutagenic activity is bilin-early dependent on hydrophobicity, with an optimal log P of 5.44, and is linearly dependent on the energy of the lowest unoccupied molecular orbital of the nitro compound. The dependence of mutagenic activity on hydrophobicity and electronic effects is very similar for TA98 and TA100. Mutagenic activity in TA100 does not depend on the size of the aromatic ring system, as its does in TA9. The effect of the choice of assay organism, TA98 versus TA100, on nitroarene QSAR is seen to be similar to the effect previously found for aminoarenes. Lateral verification of QSARs is presented as a tool for establishing the significance of a new QSAR.

Quantitative structure-activity relationship investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 2. Mutagenicity of aromatic and heteroaromatic nitro compounds in Salmonella Typhimurium TA100.

This article describes a powerful new method for teaching students about electronic structure and its relevance to chemical phenomena. This method, developed and used for several years in general chemistry and organic chemistry courses, relies on computer-generated three-dimensional models of electron density distributions.

Alan J. Shusterman

Papers

Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity.

A QSAR investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 1. Mutagenicity of aromatic and heteroaromatic amines in Salmonella typhimurium TA98 and TA100.

Reactions of benzylidene pentacarbonyltungsten with alkenes

Quantitative structure-activity relationship investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 2. Mutagenicity of aromatic and heteroaromatic nitro compounds in Salmonella Typhimurium TA100.

Teaching Chemistry with Electron Density Models