On Wiener Polarity Index and Wiener Index of Certain Triangular Networks

A topological index of graph G is a numerical quantity which describes its topology. If it is applied to the molecular structure of chemical compounds, it reflects the theoretical properties of the chemical compounds. A number of topological indices have been introduced so far by different researchers. The Wiener index is one of the oldest molecular topological indices defined by Wiener. The Wiener index number reflects the index boiling points of alkane molecules. Quantitative structure activity relationships (QSAR) showed that they also describe other quantities including the parameters of its critical point, density, surface tension, viscosity of its liquid phase, and the van der Waals surface area of the molecule. The Wiener polarity index has been introduced by Wiener and known to be related to the cluster coefficient of networks. In this paper, the Wiener polarity index W p G and Wiener index W G of certain triangular networks are computed by using graph-theoretic analysis, combinatorial computing, and vertex-dividing technology.

Economic Cycles of Carnot Type

Originally, the Carnot cycle was a theoretical thermodynamic cycle that provided an upper limit on the efficiency that any classical thermodynamic engine can achieve during the conversion of heat into work, or conversely, the efficiency of a refrigeration system in creating a temperature difference by the application of work to the system. The first aim of this paper is to introduce and study the economic Carnot cycles concerning Roegenian economics, using our thermodynamic–economic dictionary. These cycles are described in both a Q−P diagram and a E−I diagram. An economic Carnot cycle has a maximum efficiency for a reversible economic “engine”. Three problems together with their solutions clarify the meaning of the economic Carnot cycle, in our context. Then we transform the ideal gas theory into the ideal income theory. The second aim is to analyze the economic Van der Waals equation, showing that the diffeomorphic-invariant information about the Van der Waals surface can be obtained by examining a cuspidal potential.

Molecular modeling of electrolyte and polysulfide ions for lithium-sulfur batteries

The operation of a lithium-sulfur (Li-S) battery involves the transport of Li+ ions and soluble sulfides mostly in the form of solvated ions. Key challenges in the development of Li-S battery technology are the diffusion of Li+ in micropores filled with sulfur and eliminating the “shuttling” of polysulfides. Ion dimensions in solvated and desolvated forms are key parameters determining the diffusion coefficient and the rate of transport of such ions, while constrictivity effects due to the effect of pore size compared to ion size control both transport and filling of the pores. We present molecular simulations to determine the solvation parameters of electrolyte ions and sulfides S22−, S42−, S62−, and S82− in two different electrolyte systems: LiTFSI in DOL/DME and LiPF6 in EC/DMC. The calculated parameters include the coordination number and the geometrically optimized model and dimensions, using the van der Waals surface approach, of the solvated and desolvated ions. The desolvation energy of the electrolyte ions is also calculated. Such data is useful for the modeling and design of the pore sizes of cathode host materials to be able to accommodate the different sulfides while minimizing their “shuttling” between cathode and anode.

Modeling Molecules Under Pressure with Gaussian Potentials

<div> <div> <div> <p>The computational modeling of molecules under high pressure is a growing research area that augments experimental high-pressure chemistry. Here, a new electronic structure method for modeling atoms and molecules under pressure, the <i>Gaussians On Sur</i><i>ace Tesserae Simulate HYdrostatic Pressure</i> (GOSTSHYP) approach, is introduced. In this method, a set of Gaussian potentials is distributed evenly on the van der Waals surface of the investigated chemical system, leading to a compression of the electron density and the atomic scaffold. Since no parameters other than the pressure need to be specified, GOSTSHYP allows straightforward geometry optimizations and <i>ab initio</i> Molecular Dynamics simulations of chemical systems under pressure for non-expert users. Calculated energies, bond lengths and dipole moments under pressure fall within the range of established computational methods for high-pressure chemistry. A Diels-Alder reaction and the cyclotrimerization of acetylene showcase the ability of GOSTSHYP to model pressure-induced chemical reactions. The connection to mechanochemistry is pointed out. </p> </div> </div> </div>

Modeling Molecules Under Pressure with Gaussian Potentials

<div> <div> <div> <p>The computational modeling of molecules under high pressure is a growing research area that augments experimental high-pressure chemistry. Here, a new electronic structure method for modeling atoms and molecules under pressure, the <i>Gaussians On Sur</i><i>ace Tesserae Simulate HYdrostatic Pressure</i> (GOSTSHYP) approach, is introduced. In this method, a set of Gaussian potentials is distributed evenly on the van der Waals surface of the investigated chemical system, leading to a compression of the electron density and the atomic scaffold. Since no parameters other than the pressure need to be specified, GOSTSHYP allows straightforward geometry optimizations and <i>ab initio</i> Molecular Dynamics simulations of chemical systems under pressure for non-expert users. Calculated energies, bond lengths and dipole moments under pressure fall within the range of established computational methods for high-pressure chemistry. A Diels-Alder reaction and the cyclotrimerization of acetylene showcase the ability of GOSTSHYP to model pressure-induced chemical reactions. The connection to mechanochemistry is pointed out. </p> </div> </div> </div>

Statistical Analysis of the Relationship Between Antioxidant Activity and the Structure of Flavonoid Compounds

Using different methods of statistics, this paper aims to highlight the potential link between the antioxidant activity of flavonoids and the corresponding molecular descriptors. By calculating the descriptors (van der Waals surface (A), molar volume (V), partition coefficient (LogP), refractivity (R), polarizability (a), forming heat (Hformation), hydration energy (Ehidr), the dipole moment (mt)), together with antioxidant activities (RSA) calculated or taken from the literature, number of phenolic -OH groups and the presence (2) or absence (1) of C2=C3 double bond) for 29 flavonoid compounds and by intercorrelation between the studied parameters, the link between the number of phenolic groups grafted to the basic structure of flavonoids and their antioxidant activity was confirmed. Simultaneously, by using the chi-squared test and the intercorrelations matrix, a satisfactorily correlation coefficient (r2=0.5678; r=0.7536) between the structure of the flavonoids and their activity was obtained, fact that confirms the correlation of the antioxidant activity with the number of -OH phenolic groups.

Development of Predictive Linear and Non-linear QSTR Models for Aliivibrio Fischeri Toxicity of Deep Eutectic Solvents

Deep eutectic solvents (DESs) have emerged as a very important group of chemicals in recent years. Although generally considered as environment friendly or ‘green,' recent investigations reported toxic behaviors of some DESs towards various biological species. In this work, quantitative structure toxicity relationship analysis was performed on a dataset containing 72 DESs and their components to find the structural determinants responsible for higher DES mediated toxicity. Additionally, efficiencies of various machine learning tools as well as different feature selection algorithms were estimated. To understand the true predictivity of the derived models, three external validation strategies, namely ‘points out,' ‘mixtures out,' and ‘compounds out' were applied along with an ‘all out' technique, a modification of the earlier reported ‘everything out' validation. The models highlight importance of the number of nitrogen atoms, the van der Waals surface area, molar refractivity, dipole moment and molecular mass for shaping the toxicity of DESs and their components towards A. fischeri.

QSAR Analysis of Multimodal Antidepressants Vortioxetine Analogs Using Physicochemical Descriptors and MLR Modeling

Background: Vortioxetine is a multimodal antidepressant drug with combined effects on SERT as an inhibitor, 5-HT1A as agonist and 5-HT3A as an antagonist. Series of vortioxetine analogs have been reported as multi antidepressant compounds and they block serotonin transport into the neuronal cells, activate the postsynaptic 5-HT1A receptors and eliminate the low activity of 5-HT3A receptors. Objective: To explore the important properties of vortioxetine analogs involved in antidepressant activity by developing 2D QSAR models. Methods: Selections of significant descriptors were performed by Least Absolute Shrinkage and Selection Operator (LASSO) method and, the Multiple Linear Regression (MLR) method and All Subsets and GA algorithm included in QSARINS software were used for generating QSAR models. Further, the virtual screening was performed based on bioactivity and structure similarity using the PubChem database. Results: The four descriptor model of complementary information content (CIC2), solubility (bcutp3), mass (bcutm8) and partial charge in van der Waals surface area (PEOEVSA7) of the molecules is obtained for SERT inhibition with the significant statistics of R2= 0.69, RMSEtr= 0.44, R2 ext= 0.62 and CCCext= 0.78. For 5-HT1A agonist, the two descriptor model of molecular shape (Kappm3) and van der Waals volume of the atoms (bcutv11) with R2= 0.78, RMSEtr= 0.33, R2 ext = 0.83, and CCCext= 0.87 is established. The three descriptor model of information content (IC3), solubility (bcutp9) and electronegativity (GATSe5) of the molecules with R2= 0.61, RMSEtr= 0.34, R2 ext= 0.69 and CCCext= 0.72 is obtained for 5-HT3A antagonist. The antidepressant activities of 16 virtual screened compounds were predicted using the developed models. Conclusion: The developed QSAR models may be useful to predict antidepressant activity for the newly synthesized vortioxetine analogs.