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Tong, Qing; Gao, Guang‐Hua; Han, Maohua; Yu, Yang−Xin

doi:10.1023/a:1015446919301

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…5b–d) presumably because of cavitation produced by the laser pulse in the liquid. 16 Because the bubble generated by a single laser pulse collapses after a period of hundreds of microseconds, 17 semi-adiabatic compression occurs during bubble collapse, resulting in a hot spot with a temperature of the order of 10 000 K. At the end of this period, the bubble can emit a short pulse of light for a picosecond–nanosecond duration, which is known as sonoluminescence. 18,19 This phenomenon typically has broad spectral emission, which can be attributed to ion-electron recombination occurring at the high temperature of the compressed bubbles.…”

Section: Resultsmentioning

confidence: 99%

Rapid photodegradation of methylene blue by laser-induced plasma

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Rapid photodegradation of methylene blue by laser-induced plasma

et al. 2022

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“…In a previous study of hydrogen-bonding systems the activation energy, E b , in eq 23 was attributed to the energy barrier that molecules have to take when detaching and rejoining clusters of hydrogen-bonded molecules. 79 Furthermore, in a SAFT study of Tong et al, 80 the activation energy was assumed to be related to the bonded fraction (1 -X A ), i.e.,…”

Section: Resultsmentioning

confidence: 99%

Coarse-Grained Molecular Dynamics Modeling of Strongly Associating Fluids: Thermodynamics, Liquid Structure, and Dynamics of Symmetric Binary Mixture Fluids

Nies

2007

J. Phys. Chem. B

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Symmetric binary mixtures capable of strong association via a highly directional and saturable specific interaction between unlike molecules are investigated by canonical molecular dynamics simulations. The specific interaction of the molecules is defined in a new coarse-grained pair potential that can be applied in continuous molecular dynamics as well as in Monte Carlo simulations. The thermodynamic, structural, and dynamic properties of the associating mixture fluids are investigated as a function of density, temperature, and association strength of the specific interaction. Detailed analysis of the simulation data confirms a two-stage mechanism in the formation of specific bonds with increasing interaction strength, including a fast dimerization process and a subsequent stage of perfecting the bonds. A large heat capacity peak is found during the formation or breaking of the bonds, reflecting the large energy fluctuation introduced by the strong association. The fractions of nonbonded molecules obtained from the simulations as a function of density, temperature, and interaction strength are in excellent agreement with the predictions of Wertheim's thermodynamic perturbation theory. The translational and rotational dynamics of the Tmer mixture are effectively retarded with increasing association strength and are analyzed in terms of autocorrelation functions and a non-Gaussian parameter for the translational dynamics. The lifetimes of molecules in bonded and nonbonded states provide detailed information about the transformation of molecules between the bonded state and the nonbonded state. Finally, simulation sampling problems inherent to strongly interacting systems are easily overcome using the parallel tempering simulation technique. This latter result confirms that with the new continuous coarse-grained simulation potential we have a versatile and flexible interaction potential that can be used with many available molecular dynamics and Monte Carlo algorithms under various ensembles.

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“…The kinetic gas theory allows the derivation of a model for the self-diffusion coefficients of gases at infinite dilution. For low-density gases, the Chapman–Enskog equation satisfactorily describes the self-diffusion coefficients; however, for high-density and nonspherical chain fluids, this equation requires modification by combining other theories, such as the contrasting volume model, rough hard sphere model, and free-volume model, or by choosing the potential energy model and introducing correction terms, such as the hard sphere chain model and Lennard-Jones chain model . The universal coefficients of the correction terms can be obtained by fitting data from molecular dynamics simulations, , and the parameters of the structural properties can be obtained by fitting the experimental data of the self-diffusion coefficients.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Zeng

Ren

Xiao

et al. 2022

Ind. Eng. Chem. Res.

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The self-diffusion coefficient of pure liquids, a fundamental transport property, is involved in a wide range of applications. Many methods have been employed to study the self-diffusion coefficient, with the most popular being semiempirical models. The quantitative structure–property relationship (QSPR) has been widely used to predict various physicochemical properties of substances, but the appropriate molecular descriptors must be selected first. In this study, the charge density distribution area of molecules at a specific interval (S σi ) and cavity volume (V COSMO) was determined based on the conductor-like screening model for the segment activity coefficient (COSMO-SAC). Using these molecular descriptors, a backpropagation artificial neural network (BP-ANN) method was employed to construct a nonlinear QSPR model that can predict the self-diffusion coefficients of pure liquids under normal pressure. The data set used included 2596 data points for 238 compounds, covering a self-diffusion coefficient range of 8.74 × 10–13 to 8.66 × 10–9 m2·s–1 and a temperature range of 90.5–475.1 K. The coefficients of determination (R 2) of the BP-ANN model on the training, validation, and testing sets were all greater than 0.99. For the entire data set, the R 2, absolute average relative deviation (AARD), and root mean square error (RMSE) were 0.9940, 7.09%, and 0.1106, respectively. In an application domain (AD) analysis, 94.67% of the data were within the AD range of the model. Consequently, the model developed in this study can satisfactorily predict the self-diffusion coefficients of liquids.

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Untitled

Cited by 6 publications

References 30 publications

Rapid photodegradation of methylene blue by laser-induced plasma

Rapid photodegradation of methylene blue by laser-induced plasma

Coarse-Grained Molecular Dynamics Modeling of Strongly Associating Fluids: Thermodynamics, Liquid Structure, and Dynamics of Symmetric Binary Mixture Fluids

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

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