Solvation Thermodynamics

Ben‐Naim, Arieh

doi:10.1007/978-1-4757-6550-2

Cited by 555 publications

(726 citation statements)

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“…We see that the present model, with the parameter values chosen to fit the experimental solubility of methane, gives correct magnitude of v A for methane at the room temperature. 31 We were unable to find the experimental data on the temperature dependence of v A for methane; but the results given by this theoretical model seem different from those obtained from experiments for other hydrophobic solutes, 23,25 6. The theoretical prediction is qualitatively in good agreement with the experimental result for alkylbenzenes in water: 25 at fixed low temperatures, v A initially increases and then reaches a maximum with increasing P whereas at fixed high temperatures, it decreases monotonically with increasing P.…”

Section: ͑38͒contrasting

confidence: 56%

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Hydrophobic effect in the pressure-temperature plane

Koga

2004

The Journal of Chemical Physics

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The free energy of the hydrophobic hydration and the strength of the solvent-mediated attraction between hydrophobic solute molecules are calculated in the pressure-temperature plane. This is done in the framework of an exactly soluble model that is an extension of the lattice model proposed by Kolomeisky and Widom ͓A. B. Kolomeisky and B. Widom, Faraday Discuss. 112, 81 ͑1999͔͒. The model takes into account both the mechanism of the hydrophobic effect dominant at low temperatures and the opposite mechanism of solvation appearing at high temperatures and has the pressure as a second thermodynamic variable. With this model, two boundaries are identified in the pressure-temperature plane: the first one within which the solubility, or the Ostwald absorption coefficient, decreases with increasing temperature at fixed pressure and the second one within which the strength of solvent-mediated attraction increases with increasing temperature. The two are nearly linear and parallel to each other, and the second boundary lies in the low-temperature and low-pressure side of the first boundary. It is found that a single, near-linear relation between the hydration free energy and the strength of the hydrophobic attraction holds over the entire area within the second boundary in the pressure-temperature plane.

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Section: ͑38͒contrasting

confidence: 56%

“…For methane, the partial molar volume at 298 K at infinite dilution in water is 36.17 cm 3 mol Ϫ1 . 31 This means that the fractional change ‫ץ(‬ ex ‫ץ/‬ P)/ ex is 4ϫ10 Ϫ4 bar Ϫ1 ; e.g., raising P from 1 bar to 100 bars causes only 4% increase in ⌬G* or ex .…”

Section: Thermodynamics Of the Hydrophobic Effectmentioning

confidence: 99%

Hydrophobic effect in the pressure-temperature plane

Koga

2004

The Journal of Chemical Physics

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“…All interactions, including Coulombic, van der Waals and hydrogen-bond interactions, are then defined in terms of these s-profiles. One can use this formalism to compute the partition function, the Gibbs free energy and many other thermodynamic quantities, including pseudo-chemical potentials (m*) 14 …”

Section: Resultsmentioning

confidence: 99%

New materials for methane capture from dilute and medium-concentration sources

et al. 2013

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Methane (CH 4 ) is an important greenhouse gas, second only to CO 2 , and is emitted into the atmosphere at different concentrations from a variety of sources. However, unlike CO 2 , which has a quadrupole moment and can be captured both physically and chemically in a variety of solvents and porous solids, methane is completely non-polar and interacts very weakly with most materials. Thus, methane capture poses a challenge that can only be addressed through extensive material screening and ingenious molecular-level designs. Here we report systematic in silico studies on the methane capture effectiveness of two different materials systems, that is, liquid solvents (including ionic liquids) and nanoporous zeolites. Although none of the liquid solvents appears effective as methane sorbents, systematic screening of over 87,000 zeolite structures led to the discovery of a handful of candidates that have sufficient methane sorption capacity as well as appropriate CH 4 /CO 2 and/or CH 4 /N 2 selectivity to be technologically promising.

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“…5 Different approaches have been proposed in the literature for predicting ∆ solv G°. They can be classified as theory-based computational chemistry approaches and QSAR/QSPR approaches.…”

Section: Prediction Of the Solvation Free Energymentioning

confidence: 99%

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

Bernazzani

Duce

Micheli

et al. 2006

J. Chem. Inf. Model.

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In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/ quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, ∆ solv G°, has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the ∆ solv G°in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R ) 0.9985, standard deviation S ) 0.68 kJ mol -1 , and mean absolute error MAE ) 0.46 kJ mol -1 . The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

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Solvation Thermodynamics

Cited by 555 publications

References 0 publications

Hydrophobic effect in the pressure-temperature plane

Hydrophobic effect in the pressure-temperature plane

New materials for methane capture from dilute and medium-concentration sources

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

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