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Sulfolane is an important aprotic polar solvent. Liquid-liquid equilibrium (LLE) data for the ternary systems of water + 1,2-dichloroethane + sulfolane were measured at temperatures of 288.15, 298.15 and 308.15 K under the atmospheric pressure. The distribution coefficient and selectivity were determined from the measured LLE data, which showed that 1,2-dichloroethane is a suitable extractant for the recovery of sulfolane from its aqueous solution. The nonrandom two-liquid (NRTL) model and the universal quasi-chemical (UNIQUAC) model were utilized to correlate the experimental LLE data. The low values of RMSD indicated that the ternary system could be fitted well by the NRTL and UNIQUAC models. The consistency of the binary interaction parameters for the two thermodynamic models obtained was confirmed by the topological information contained in the Gibbs energy of mixing function (GM/RT).

The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set ofgt;130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.

The reliability of molecular mechanics simulations to predict effects of ion binding to proteins depends on their ability to simultaneously describe ion-protein, ion-water, and protein-water interactions. Force fields (FFs) to describe protein-water and ion-water interactions have been constructed carefully and have also been refined routinely to improve accuracy. Descriptions for ion-protein interactions have also been refined, although in an

The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free-energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with30% identity to the target protein from the training set. We observed that in most cases, the ΔΔ

The Hoogsteen (HG) base pairing conformation, commonly observed in damaged and mutated DNA helices, facilitates DNA repair and DNA recognition. The free energy difference between HG and Watson-Crick (WC) base pairs has been computed in previous studies. However, the mechanism of the conformational transition is not well understood. A detailed understanding of the process of WC to HG base pair transition can provide a deeper understanding of DNA repair and recognition. In an earlier study, we explored the free energy landscape for this process using extensive computer simulation with the CHARMM36 force field. In this work, we study the impact of force field models in describing the WC to HG base pairing transition using meta-eABF enhanced sampling, quasi-harmonic entropy calculation, and nonbonded energy analysis. The secondary structures of both base pairing forms and the topology of the free energy landscapes were consistent over different force field models, although the relative free energy, entropy, and the interaction energies tend to vary. The relative stability of the WC and HG conformations is dictated by a delicate balance between the enthalpic stabilization and the reduced entropy of the structurally rigid HG structure. These findings highlight the impact that subtleties in force field models can have on accurately modeling DNA base pair dynamics and should stimulate further computational investigations into other dynamically important motions in DNA.

Generalized force fields (FFs) act as extensions to biomolecular FFs to provide a wide coverage of organic molecules. However, their precise application to an arbitrary molecule presents a separate challenge. We show that MATCH assigns different atom types and bonded and nonbonded parameters than CGenFF, and the AM1-BCC charge model, commonly used with GAFF/GAFF2, does not exactly reproduce the performance of the RESP charge model. The results indicate the need for caution when employing FFs to ensure their integrity with respect to their implementation and validation.

International audience; In their seminal paper of 1923, Debye and Hückel provided the first appropriate description of the effect of ionic strength on the thermodynamic properties of dilute electrolyte solutions. This landmark work paved the way for what was later called the primitive model of electrolytes. At this level, an ionic solution is modeled as a collection of charged hard spheres in a dielectric continuum that manifests itself only through its dielectric constant. Numerical simulations have been reported in the literature for salts in a continuous solvent. In the present work, results obtained using various analytical theories are compared with Monte-Carlo (MC) simulation data (expected to be 'exact') taken from the literature, in the case of binary electrolytes in a continuous solvent mimicking water. The theories include the Debye-Hückel theory, the mean-spherical approximation (MSA), and the Pitzer approach. The MC data are about mean salt activity and osmotic coefficients, and also individual ion activity coefficients. Moreover, some remarks are made about the assumptions underlying the Debye-Hückel framework, and new formulas are derived for individual ion activity coefficients by following the method of Pitzer.

2,4(5)-Dinitroimidazole (2,4(5)-DNI) is an important energetic material, and it is also an important precursor for the preparation of drugs and energetic materials. In this study, the solubility of 2,4(5)-DNI in eleven pure solvents (chlorobenzene, benzene, 1,2-dichloroethane, toluene, water, isopropyl alcohol, ethyl acetate, acetonitrile, methanol, 1,4-dioxane and acetone) were measured by using a dynamic test method from 278.15 K to 323.15 K under 101.1 kPa. Four solubility models were used to fit the experimental data, which were ideal model, modified Apelblat equation, polynomial empirical equation, and λh equation. Meanwhile, the relative average deviation and root-mean-square deviation between the experimental data and the fitted data were also calculated. Furthermore, the three thermodynamic parameters, i.e., dissolution enthalpy, dissolution entropy and Gibbs energy were obtained based on solubility data. Finally, the crude product of 2,4(5)-DNI was crystallized with acetone as solvent, and the purity of the crystalline product was greater than 99.5%.

There is increasing interest in kinetic models of catalytic refining processes, because of their importance in developing and improving these processes. In fact, the models can be used to predict process performance parameters and to determine the optimum operating conditions. In this work, a detailed reaction kinetic model was developed to describe light naphtha isomerisation reactions over different Pt/zeolite catalysts (Pt/mordenite, Pt/ZSM5, and Pt/BETA). The proposed kinetic model included 66 isomerisation, 28 ring opening, 1 olefin saturation, 22 dehydrogenation, and 90 hydrocracking reactions for 52 real light naphtha components graded from methane to n-octane. A power law reaction kinetics was assumed for all reactions, and 414 kinetic parameters were fine-tuned by comparing the predictions of the model with the results of light naphtha isomerisation experiments at six different temperatures (290, 310, 330, 350, 370, and 390 °C). The Matlab software (version 2015a) was used to perform all the calculations in the current study, and the optimum set of kinetic parameters was estimated using genetic algorithm optimisation. The mean absolute errors for all component compositions at different temperatures were found to be 0.003608, 0.004841, and 0.004757 for the Pt/mordenite, Pt/ZSM5, and Pt/BETA catalysts, respectively. The values of the mean absolute errors estimated from the comparison between the experimental and predicted results showed a very good agreement between the model predictions and the experimental data.

In this work, thermodynamic and kinetic factors that affect formation and phase transformation process of different solid forms of levofloxacin hydrochloride were investigated in detail. Dynamic vapor sorption experiments (DVS) and varying temperature-powder X-ray diffraction (VT-PXRD) experiments were carried out to study moisture-dependent stability, thermal stability as well as the transformation process between Form I and Form II. Critical water activities of levofloxacin hydrochloride were determined in temperature range of 5.0–45.0 °C. Raman spectroscopy was applied to in situ monitor the temperature-induced phase transformation and the hydration process of levofloxacin hydrochloride, and one possible mechanism consisting of multiple chemical equilibria was proposed to analyze the effect of thermodynamic and kinetic factors on the formation and transformation of different solid forms. Results show that the anhydrate, Form II would transform to the monohydrate, Form I more easily with the increasing water content. And the transition point moves toward higher water contents as the temperature increases. The results suggested that, except for thermodynamic factors, kinetic factors also play an essential role in controlling the solid forms of polymorphic compounds.

Trichloroethylene (TCE) is largely used in industries as a cleaning and degreasing solvent. TCE is a potential carcinogen and is known to cause organ damage when exposed to prolonged higher concentrations. Numerical simulation of fixed and fluidized bed adsorption of TCE can help in the development of efficient adsorption processes to prevent industrial workers in the vicinity from acute TCE exposure. In the present work, a parametric optimization based numerical experimentation algorithm is implemented by open-source computational solvers to model fixed and fluidized bed adsorption of trichloroethylene vapors on activated carbon. The algorithm optimizes four parameters pertaining to linear driving force (LDF) formulation of surface barrier and microporous diffusion. The optimized parameters were utilized to evaluate ζ ¯ , a dimensionless number defined as the temporal and spatial average ratio of surface barrier diffusion resistance to microporous diffusion resistance. The average value of ζ ¯ is 0.139 for fixed bed operation (u/umf = 0.7), 1.130 for fluidized bed operation (u/umf = 1), 4.436 for fluidized bed operation (u/umf = 1.5) and 6.317 for fluidized bed operation (u/umf = 2). Therefore, the dimensionless number ζ ¯ may serve to predict the extent of change in amount adsorbed per unit adsorbent mass with change in fluidization velocity.

The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end-state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end-state potential energies. The scheme introduces nonuniform scaling into the electrostatic potential as used in Replica Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.

The removal of color from dye wastewater is crucial, since dyes are extremely toxic and can cause cancer in a variety of life forms. Studies must be done to use cost-effective adsorbents for the removal of color from dye effluents to protect the environment. To our knowledge, virtually no research has been done to describe the possibility of using Calotropisgigantea leaf extract zinc hydroxide nanoparticles (CG-Zn(OH)2NPs) as an adsorbent for the decolorization of Coomassie violet (CV) from the aqueous emulsion, either in batch mode or continuously. In the present batch investigation, CV dye is removed from the synthetic aqueous phase using CG-Zn(OH)2NPs as an adsorbent. The synthesized nanoparticles were characterized using various instrumental techniques such as Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDS) and Brunauer–Emmett–Teller (BET) surface area and pore volume, a particle size analyser, and zero-point charge. The decolorization efficacy of CV dye from an aqueous phase by the adsorbent was examined in batch mode by varying process parameters. The consequences of various experimental variables were optimized using response surface methodology (RSM) to achieve the maximum decolorization efficiency (90.74%) and equilibrium dye uptake, qe (35.12 mg g−1). The optimum pH, dye concentration, CG-Zn(OH)2NPs adsorbent dosage, and particle size were found to be 1.8, 225 mg L−1, 5 g L−1, and 78 μm, respectively for CV dye adsorption capacity at equilibrium. The adsorbent zero-point charge was found to be at pH 8.5. The Langmuir isotherm model provided a good representation of the equilibrium data in aqueous solutions, with a maximum monolayer adsorption capability (qmax) of 40.25 mg g−1 at 299 K. The dye adsorption rate follows a pseudo-second-order kinetic model at various dye concentrations, which indicated that the reaction is more chemisorption than physisorption. The negative values of ΔG and positive values of ΔH at different temperatures indicate that the adsorption process is spontaneous and endothermic, respectively. Reusability tests revealed that the prepared nanoparticles may be used for up to three runs, indicating that the novel CG-Zn(OH)2NPs seems to be a very promising adsorbent for the removal of Coomassie violet dye from wastewater.

In this work, a set of 12-6-4 force fields (FFs) parameters were developed for the actinyl molecular cations, AnO

Dynamic allostery refers to one important class of allosteric regulation that does not involve noticeable conformational changes upon effector binding. In recent years, many "quasi"-dynamic allosteric proteins have been found to only experience subtle conformational changes during allosteric regulation. However, as enthalpic and entropic contributions are coupled to each other and even tiny conformational changes could bring in noticeable free energy changes, a quantitative description is essential to understand the contribution of pure dynamic allostery. Here, by developing a unified anisotropic elastic network model (uANM) considering both side-chain information and ligand heavy atoms, we quantitatively estimated the contribution of pure dynamic allostery in a dataset of known allosteric proteins by excluding the conformational changes upon ligand binding. We found that the contribution of pure dynamic allostery is generally small (much weaker than previously expected) and robustly exhibits an allosteric activation effect, which exponentially decays with the distance between the substrate and the allosteric ligand. We further constructed toy models to study the determinant factors of dynamic allostery in monomeric and oligomeric proteins using the uANM. Analysis of the toy models revealed that a short distance, a small angle between the two ligands, strong protein-ligand interactions, and weak protein internal interactions lead to strong dynamic allostery. Our study provides a quantitative estimation of pure dynamic allostery and facilitates the understanding of dynamic-allostery-controlled biological processes and the design of allosteric drugs and proteins.

We characterize the folding-unfolding thermodynamics of two mutants of the miniprotein Trp-cage by combining extended molecular dynamics simulations and an advanced statistical-mechanical-based approach. From a set of molecular dynamics simulations in an explicit solvent performed along a reference isobar, we evaluated the structural and thermodynamic behaviors of a mesophilic and a thermophilic mutant of the Trp-cage and their temperature dependence. In the case of the thermophilic mutant, computational data confirm that our theoretical-computational approach is able to reproduce the available experimental estimate with rather good accuracy. On the other hand, the mesophilic mutant does not show a clear two-state (folded and unfolded) behavior, preventing us from reconstructing its thermodynamics; thus, an analysis of its structural behavior along a reference isobar is presented. Our results show that an extended sampling of these kinds of systems coupled to an advanced statistical-mechanical-based treatment of the data can provide an accurate description of the folding-unfolding thermodynamics along a reference isobar, rationalizing the discrepancies between the simulated and experimental systems.

Two bi-part combined models containing Reaction Equilibrium Thermodynamic Model and an equation of state (EoS) including cubic plus association (CPA) or modified Soave-Redlich-Kwong (SRK), were employed to correlate H2S solubility in ionic liquids [emim][Lac], [bmim][Ac] and [emim][Pro]. RETM and CPA/mSRK are responsible for chemical and phase equilibria calculations, respectively. The RETM defines a complex formation reaction between an H2S and two IL molecules into the liquid phase so that a complex in the form of H2S(IL)2 is probable to produce. On the other side, CPA/mSRK EoSs perform phase equilibria computations to find pressure and vapor phase concentrations. In the case of CPA, both of H2S and the ionic liquids were considered as self-associating components which pursue 4C and 2B schemes, respectively. This model computes the pure IL parameters using experimental density data. In this work, critical properties were estimated through the modified Lydersen-Joback-Reid method, Eotvos and Guggenheim relations. Afterward, the binary systems were investigated by applying RETM. CPA presents average absolute deviations (AADs) equal to 2.41%, 13.42% and 3.52% for [emim][Lac], [bmim][Ac], and [emim][Pro], respectively. Moreover, the AADs obtained by mSRK are 3.75%, 5.07%, and 6.06%, respectively. As it is evident from the results, the combination of RETM with both CPA and mSRK EoSs will result in good correlation accuracy.

Aiming at solving the problem of high global warming potential of R134a, a new mixed refrigerant R13I1/R152a (molar fraction ratio of 35:65) with no ozone depletion potential and low global warming potential was proposed as a substitute for R134a in automotive air conditioning. The computational models for the thermodynamic properties of R13I1/R152a were established by using the PR equation of state combined with the vdW mixing rule. Based on these models, the cycle performance of this working fluid was calculated, which was also compared with that of R134a and R1234yf under the different operating conditions. The results show that R13I1/R152a is a near azeotropic refrigerant whose temperature glide is approximately 0, and the saturated vapor pressure curve of which is equivalent to that of R134a. Moreover, compared to R134a, R13I1/R152a has an average 5.7% improvement in coefficient of performance as well as similar volumetric cooling capacity. The average coefficient of performance and volumetric cooling capacity of R13I1/R152a are significantly higher than those of R1234yf by 13.8% and 12.0%, respectively. However, the average discharge temperature of R13I1/R152a is approximately 13.3 K higher than that of R134a, but it is also within reasonable limits. Hence, the application of the proposed refrigerant R13I1/R152a in automotive air conditioning system is technically feasible.

The equilibrium solubility of 2,4-diaminobenzenesulfonic acid and super solubility as well as metastable zone width were measured in (H2SO4+ H2O) system by the laser dynamic method at elevate temperature range from 298.15 K to 338.15 K. 2,4-diaminobenzenesulfonic acid solubility dependence on the temperature and solvent composition were correlated by the modified Apelblat equation, (CNIBS)/Redlich-Kister model and Jouyban-Acree model. The correlated results by three correlation models were in good accord with the experimental values according to relative average deviations (RD), root-mean-square deviations (RMSD), and correlation coefficients (R2). The metastable zone width increased with temperature and sulfuric acid content. The dissolution enthalpy, dissolution entropy and the Gibbs energy were calculated from the experimental values, which indicated that dissolution process of the 2,4-diaminobenzenesulfonic acid was endothermic. The solubility and calculation models of 2,4-diaminobenzenesulfonic acid in (sulfuric acid +water) system could provide the basic data to the crystallization and purifying of the 2,4-diaminobenzenesulfonic acid.

Non-equilibrium thermodynamics theory is used to analyze the transmembrane heat and moisture transfer process, which can be observed in a membrane-type total heat exchanger (THX). A theoretical model is developed to simulate the coupled heat and mass transfer across a membrane, total coupling equations and the expressions for the four characteristic parameters including the heat transfer coefficient, molar-driven heat transfer coefficient, thermal-driven mass transfer coefficient, and mass transfer coefficient are derived and provided, with the Onsager’s reciprocal relation being confirmed to verify the rationality of the model. Calculations are conducted to investigate the effects of the membrane property and air state on the coupling transport process. The results show that the four characteristic parameters directly affect the transmembrane heat and mass fluxes: the heat and mass transfer coefficients are both positive, meaning that the temperature difference has a positive contribution to the heat transfer and the humidity ratio difference has a positive contribution to the mass transfer. The molar-driven heat transfer and thermal-driven mass transfer coefficients are both negative, implying that the humidity ratio difference acts to reduce the heat transfer and the temperature difference works to diminish the mass transfer. The mass transfer affects the heat transfer by 1-2% while the heat transfer influences the mass transfer by 7%-14%. The entropy generation caused by the temperature difference-induced heat transfer is much larger than that by the humidity difference-induced mass transfer.

Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent developments in generative deep neural networks rendered possible the parallel, statistically independent sampling of molecular configurations. To be able to accurately generate samples of large molecular systems from a high-dimensional multimodal equilibrium distribution function, we developed a hierarchical approach based on expressive normalizing flows with rational quadratic neural splines and coarse-grained representation. Furthermore, system specific priors and adaptive and property-based controlled learning was designed to diminish the likelihood for the generation of high-energy structures during sampling. Finally, backmapping from a coarse-grained to fully atomistic representation is performed through an equivariant transformer model. We demonstrate the applicability of the method on the one-shot configurational sampling of a protein system with more than a hundred amino acids. The results show enhanced expressivity that diminish the invertibility constraints inherent in the normalizing flow framework. Moreover, the capacity of the hierarchical normalizing flow model was tested on a challenging case study of the folding/unfolding dynamics of the peptide chignolin.

Removing impurities such as carbon dioxide and nitrogen from natural gas is a technical challenge and one of the major concerns in natural gas treatment process. In this study, adsorption of CH4, N2, and CO2 on the Faujasite (FAU) zeolite has been studied using molecular dynamics simulation at temperatures of 293, 308, and 323 K and pressures up to 1 MPa. COMPASS force field was used to model the interactions between zeolite and guest molecules. Ewald and atom-based summation methods were used for the calculation of electrostatic and van der Waals forces, respectively. Simulated results were modeled using Langmuir, Freundlich, Toth, and Sips adsorption isotherms. Sips isotherm for CO2, and Toth isotherm for CH4 and N2 pure compounds showed the best performance. Heat of adsorption for CH4, CO2, and N2 were calculated to be -15.48, -24.1, and -13.31 kJ.mol−1, respectively. A comparative study showed that the simulation model was successful in predicting the overall trend of the adsorption with acceptable accuracy.

Excess adsorption of CO2, CH4, N2, and H2 on ZIF-8 was measured gravimetrically in the pressure range ranging from vacuum to 30 MPa at 298.15, 313.15, 333.15, 353.15, and 394.15 K using a magnetic suspension balance. The textural properties of the adsorbent material─i.e., skeletal density, surface area, pore volume, and pore-size distribution─were estimated by helium gravimetry and N2 (77 K) physisorption. The adsorption isotherms were fitted with the Sips isotherm model and the virial equation, and the values of isosteric heat of adsorption and Henry constants for the gases were determined using the latter.

Vapor–liquid, liquid–liquid, and vapor–liquid–liquid equilibria of the binary system nitrogen + ethane are investigated systematically for temperatures between 105 and 260 K and pressures between 15 and 150 bar. Force field models are used that have been parametrized beforehand solely to vapor–liquid equilibrium data and are augmented by a single binary parameter ξ = 0.974 that has also been adjusted in a preceding work to vapor–liquid equilibrium data at 260 K. The molecular mixture model is tested with a focus on its predictive power with respect to liquid–liquid equilibria. For that purpose, more than 4000 state points are sampled around the three-phase curve with molecular simulations in the isobaric–isothermal (NpT) and canonic (NVT) ensemble. Despite the large distance from its original adjustment point, the mixture model yields sound results for vapor–liquid equilibria at low temperatures and is capable of predicting the large miscibility gap of nitrogen + ethane with an average deviation of ±0.025 mol mol–1. Furthermore, the thermodynamic factor is sampled with Kirkwood–Buff integration and is also used for phase equilibrium calculations. The simulation results are compared with the high-accuracy multifluid GERG-2008 equation of state (EOS) and the Peng–Robinson EOS.

Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy. Importantly, our method also provides an uncertainty estimate for the predicted profiles without any additional calculations. In this report, we show that TorsionNet can accurately identify the preferred dihedral geometries observed in crystal structures. Our TorsionNet-based analysis of a diverse set of protein-ligand complexes with measured binding affinity shows a strong association between high ligand strain and low potency. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. TorsionNet500, a benchmark data set comprising 500 chemically diverse fragments with DFT torsion profiles (12k MM- and DFT-optimized geometries and energies), has been created and is made publicly available.

Effective distribution coefficients of 9 impurities in 1,2-Diphenylethane have been calculated by directional crystallization under different ambient frozen temperature. The effect of varied zone size, temperature difference between the melt and ambient frozen environment, number of zone on purity of 1,2-Diphenylethane have been also investigated during the process of zone refining. The results indicate that the product purity in the intermediate purified region with varied zone size is higher 0.04%−0.2% than that with constant zone size. The product purity increases with temperature difference between the melt and ambient frozen environment. The appropriate temperature difference is adopted 50℃.The product purity in the intermediate region of sample bar with 2 molten zones is higher 0.05%−0.43% than that with 1 molten zone. In addition, the change of enthalpy and entropy between impurities and 1,2-Diphenylethane have been determined.

The outstanding advantages of methanol such as low pollutant emissions of nitrogen oxides (NOx) and carbon monoxide (CO) make it a promising clean-burning fuel. Despite, the latent heat of vaporization of methanol is 3.70 times that of gasoline, the low heating value of methanol is one of the most critical barriers to its effective utilization in industrial applications. Thus, the methanol burner needs to be effectively designed to determine the desired combustion characteristics and the optimal design of this type of clean burner. Hence, this study presents a computational fluid dynamic analysis on the combustion characteristics of a methanol swirling burner with two layers of swirling blades. A particular focus of this study is emphasized on the effects of different swirling blade angles (45°+45°, 60°+60°, and 45°+60°) and various equivalence ratios (0.5, 0.75, 1.0, 1.25, and 1.75) on the combustion characteristics and pollutants formation of the swirl burner. The velocity and temperature profiles, combustion characteristics, and concentrations of major combustion species are analyzed in detail. The results show that the blade angle arrangement of 45°+60° exhibits the best combustion characteristics in comparison with the other blade angle arrangements. It is also found that the BA3 with an equivalent ratio in the range of 1.0~1.25 shows the best performance in the emissions of NOx and CO compared with other combinations of swirling blade arrangements and equivalence ratios. More specifically, the optimal equivalence ratio ranges from 1.0 to 1.25 at which the NOx and CO emissions are measured to be 27.0 and 11.0 mg/m3, respectively.

In order to remove N2 from low quality natural gas, a mathematical model has been established by Aspen adsorption, using the CH4-selective sorbent silicalite-1 pellets. The dynamic adsorption isotherm was first simulated by breakthrough simulation of a CH4/N2 mixture at different adsorption pressures and feed flow rates based on breakthrough experiments. The resulting simulated CH4 dynamic adsorption amounts were very close to the experimental data at three different adsorption pressures (100, 200, and 300 kPa). Moreover, a single-bed, three-step pressure swing adsorption (PSA) experiment was performed, and the results were in good agreement with the simulated data, further corroborating the accuracy of the gas dynamic adsorption isotherm obtained by the simulation method. Finally, based on the simulated dynamic adsorption isotherm of CH4 and N2, a four-bed, eight-step PSA process has been designed, which enriched 75% (vol) CH4 and 80% (vol) CH4 to 95% (vol) and 99% (vol), and provided 99% (vol) recovery.

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain while the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20,253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the three models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software packages, and source code. ispartof: JOURNAL OF CHEMICAL INFORMATION AND MODELING vol:62 issue:3 pages:433-446 ispartof: location:United States status: published

The dimerization of transactive response DNA-binding protein of 43 kDa (TDP-43) is crucial for the RNA metabolism, and the higher-order aggregation of TDP-43 would induce several neurodegenerative diseases. The dimerization and aggregation of TDP-43 are regulated by the phosphorylation on its N-terminal domain (NTD). Understanding the regulation mechanism of TDP-43 NTD dimerization is crucial for the preventing of harmful aggregation and the associated diseases. In this study, the dimerization processes of wild-type (WT), phosphorylated S48 (pS48), and phosphomimic S48E mutation (S48E) of TDP-43 NTD are characterized by the enhanced sampling technology. Our results show that the phosphorylation not only shift the conformation population of bound and unbound state of TDP-43 NTD, but also would regulate the dimerization processes, including increase the binding free-energy barrier. The phosphomimic mutation would also shift the conformational space of TDP-43 NTD dimer to the unbound structures; however, the thermodynamic and kinetic properties of the dimerization processes between the phosphorylated and phosphomimic mutant systems are distinct, which reminds us to carefully study the phosphorylation regulation by using the phosphomimic mutations.

Ionic liquids are a significant type of organic salts with attractive and superior physicochemical features for employing in a number of chemical processes. Ionic liquids include one large and asymmetric cationic or anionic functional group. The properties of cationic functional group are almost similar and it is main motivation of this study. In the present investigation, we extended various semi-empirical correlations to predict some temperature-based properties of ionic liquids mixture (IL-mixture). These properties are density, viscosity, surface tension and bubble point pressure. In the case of IL-mixture density, modified Rackett equation and Taylor equation with three scenarios, modified Gardas and Coutinho equation were considered and the obtained results were compared with Peng-Robinson equation of state (EoS), Patel-Teja EoS and perturbed-chain statistical associating fluid theory EoS. In the case of IL-mixture viscosity, two types of activation energy concept models were used and also, Lee-Kesler method and modified Brock and Bird were applied to predict IL-mixture bubble point pressure and IL-mixture surface tension, respectively. A used dataset was collected from reliable literature including 2731 data points of bubble pressure (23 binary system), 4643 data points of density (102 binary system), 1011 data points of viscosity (32 binary system) and 391 data points of surface tension (16 binary system). These data relevant to 12 cation-family and 36 solvents. The all of the data was haphazardly separated in two subsets of train and test with a computer program. The obtained results indicated all used semi-empirical correlations gives the best match with corresponding experimental data and the values of statistical parameters demonstrated this hypothesis.

A Single-Event MicroKinetic (SEMK) model has been extended towards the simulation of Steady State Isotopic Transient Kinetic Analysis (SSITKA) data for Co catalyzed Fischer-Tropsch Synthesis (FTS). The extended model considers two types of sites and both direct and H-assisted CO dissociation. Regression of the model parameters to the data obtained from 17 steady state and 11 SSITKA experiments resulted in physicochemically meaningful estimates for the activation energies and atomic chemisorption enthalpies. The application of the phenomenological UBI-QEP method allows to physically interpret the nature of the two site types considered in the model, i.e., terrace and step sites. A reaction path analysis shows that over 80 percent of the CO reacts on the step sites. Furthermore, chain growth exclusively occurs on these sites. The terrace sites are less reactive for CO dissociation and are identified as responsible for methane production. A fraction of the alkenes, produced on the step sites, is hydrogenated to alkanes on the terrace sites. Based on model simulations, the metal particle size effect, i.e., a lower TOF, higher methane selectivity and increasing alkane to alkene ratio with decreasing metal particle size, is attributed to an increasing relative importance of the terrace sites on the reaction kinetics.

Nonaqueous amine-based system is an attractive solution to overcome high-energy-intensive CO2 capture process using the conventional aqueous amines. Advanced nonaqueous absorbent of 2-(butylamino)ethanol (BAE) with 2-butoxyethanol (2-BE) has been recently proposed for low-energy-consumption CO2 capture. In this work, Henry’s law constants of CO2 in the BAE/2-BE blend were obtained by N2O/CO2 analogy, and correlated in the temperature range of (283−333) K. Vapor-liquid equilibrium (VLE) data for the BAE+CO2+2-BE system at 65.4% (mass) BAE were also determined in a stirred equilibrium cell at temperatures of (313−393) K and CO2 partial pressures up to 275 kPa. A single apparent equilibrium constant KCO2,app was proposed for this system and correlated as a function of temperature, carbonated degree of amine and CO2 loading. Solubility data were well represented by the modified Kent-Eisenberg model with an average absolute relative deviation (AARD) of 13%.

A size-by-association kinetic characterization for galena-chalcopyrite flotation is presented. The size-by-size maximum recovery R∞ and flotation rate distribution f(k) are first estimated by least-squares estimation (LSE) in four size classes. Gamma f(k)s are assumed in all cases. LSE is also used in the association categories subject to constraints to match/approach the size-by-size R∞-f(k)s pairs from the association classes. The methodology allows the size-by-size f(k)s to be explained from the size-by-association f(k)s. These size-by-association f(k)s do not converge to deterministic rate constants, which agree with the effect of other particle properties on the flotation response. The f(k)s in the −20 μm class are approximately 80–85% justified by liberated particles. These particles explain the fast-floating material in all size classes. Binary and complex middlings typically concentrate to slower flotation rates. From the f(k) results, slow-rate fractions are estimated from a low f(k) percentile. These fractions are not consistently attributed to complex middlings.

A modelling framework is proposed to simulate the co-precipitation of Ni-Mn-Co hydroxide as precursor of cathode material for lithium-ion batteries. It integrates a population balance equation with computational fluid dynamics to describe the evolution of the particle size in (particularly continuous) co-precipitation processes. The population balance equation is solved by employing the quadrature method of moments. In addition, a multi-environment micromixing model is employed to consider the potential effect of molecular mixing on the fast co-precipitation reaction. The modelling framework is used to investigate the co-precipitation of Ni 0.8 Mn 0.1 Co 0.1 (OH) 2 in a multi-inlet vortex micromixer, as a suitable candidate for the study of fast co-precipitation processes in continuous mode. Finally, the simulation results are discussed, and the role of the different phenomena involved in the formation and evolution of particles is identified by inspecting the predicted trends.

We explored such issues as the formation mechanism, structure and propriety of the solid solutions of anthracene (ANT)–phenanthrene (PHE). Solution crystallization and solid-state grinding were employed to prepare solid solutions under different conditions. The thermal behavior and PXRD scanning results revealed the formation of discontinuous solid solutions, whose melting points and crystal lattices varied linearly with mixed ratio. Combing with Materials Studio, the formation possibility of solid solutions were investigated by evaluating the change of the energy. The crystal morphology of the solid solutions have a positive correlation with the change of the major part. Finally, the solution crystallization process of solid solution were studied using the population balance model.

Clinoptilolite is a low-cost and abundant natural zeolite with a high CO2 selectivity, which is abundant in many regions around the world, but with limited industrial use. By combining experimental and theoretical (molecular simulation) approaches, the present work aims at contributing with fundamental data to assist the design of adsorption processes based on this zeolite. A sample of naturally occurring (raw) clinoptilolite was characterized by TGA, XRD, XRF, N2 adsorption isotherms at -196 °C and CO2 at 0 °C. Single-component (CO2, N2, CH4 and O2) and binary (CO2/CH4 and CO2/N2) equilibrium isotherms were measured gravimetrically. Column dynamics experimental data were obtained in order to validate a fixed-bed model, which was then used to evaluate the performance of separation processes. Molecular simulation (MS) was used to build a predictive zeolite model validated with experimental data for CO2, N2, CH4 and O2 able to investigate adsorption of mixtures and gas diffusion. TGA results demonstrated structural stability up to 600 °C with the X-ray diffraction pattern revealing clinoptilolite as the main phase, together with cristobalite and other silicon phases as contaminants. The textural properties obtained from N2 adsorption isotherms at -196 °C of the studied sample were comparable to those reported in the literature, though inaccurate for the actual clinoptilolite structure, which poses significant diffusional resistance for N2 at such temperature. IAST combined with Sips model was used to predict the behavior of mixtures. The proposed clinoptilolite MS model was used to verify the suitability of IAST in predicting mixture equilibrium on clinoptilolite and to predict the selectivity of CO2 over O2. The MS model was able to assess the equilibrium effects in the adsorption of the CH4-N2 mixture and to confirm the relationship between the diffusivity of gases and the positioning and nature of cations in the structure. The fixed-bed model highlighted the different performances of the raw and exchanged clinoptilolite samples, including the theoretical Na-clinoptilolite crystal predicted by the MS model. Together with experimental data, the MS model can be used to aid the design of separation processes of such gas stream with multiple components using clinoptilolite.

Accurately understanding the thermal conductivities of nanorefrigerants is crucial to the development of heat transfer enhancement technology in applications such as cooling, microelectronics, and refrigeration. In this work, three types of nanorefrigerants, Al2O3−R141b, TiO2−R141b, and SiO2−R141b, were prepared and characterized. The effective thermal conductivities of those nanorefrigerants at different nanoparticle concentrations (φv: 0.05–0.6 vol%), temperature (T: 283–298 K), hydrodynamic size (da: 48.56–115.92 nm) and nanoparticle thermal conductivities (kp: 1.2–36 W/m·K) was experimentally investigated. For the first time, five intelligent models—the radial basis function (RBF), multilayer perceptron (MLP), least-squares support-vector machine (LSSVM), dendrite morphological neural network (DMNN), and spiking neural network (SNN) models—were developed to predict the effective thermal conductivities of nanorefrigerants based on 288 experimental samples. Through statistical and graphical methods, the performances of these intelligent models and seven commonly used theoretical/empirical correlations were compared. In addition, trend and sensitivity analyses were conducted to evaluate the influence degree of each independent input parameter. The results indicated that the effective thermal conductivity increased with increasing nanoparticle concentration, temperature, and nanoparticle thermal conductivity, while it decreased with increasing hydrodynamic size. The RBF model outperformed the other intelligent and theoretical, and empirical models with the highest accuracy (R2 = 0.985) and the lowest error (RMSE = 0.0178, MAPE = 1.26%) in the complete dataset. The RBF model accurately captured the physical trends of the effective thermal conductivity as the input parameters were varied. Meanwhile, the nanoparticle concentration was the most influential factor of the four input parameters.

In this work, a molecular-level kinetic model was built to simulate the vacuum residue (VR) coking process in a semi-batch laboratory-scale reaction kettle. A series of reaction rules for heavy oil coking were summarized and formulated based on the free radical reaction mechanism. Then, a large-scale molecular-level reaction network was automatically generated by applying the reaction rules on the vacuum residue molecules. In order to accurately describe the physical change of each molecule in the reactor, we coupled the molecular-level kinetic model with a vapor-liquid phase separation model. The vapor-liquid phase separation model adopted the Peng-Robinson equation of state to calculate vapor-liquid equilibrium. A separation efficiency coefficient was introduced to represent the mass transfer during the phase separation. We used six sets of experimental data under various reaction conditions to regress the model parameters. The tuned model showed that there was an excellent agreement between the calculated values and experimental data. Moreover, we investigated the effect of reaction temperature and reaction time on the product yields. After a comprehensive evaluation of the reaction temperature and reaction time, the optimal reaction condition for the vacuum residue coking was also obtained.

Currently, the most common method of silane synthesis for electronics and photovoltaics is trichlorosilane (TS) dismutation. TS dismutation proceeds in the form of a reactions cascade, therefore its study is of both practical and scientific interest. The results of calculating the equilibrium composition of the reaction mixture in the vapor phase based on literature data from various sources were not reliable. Therefore, the dependence of the composition of the reaction mixture on the time of contact of the TS vapor with the catalyst under static conditions was experimentally investigated. The stationary composition of the mixture, close to equilibrium, was determined. A good agreement of the obtained results with the literature data in one of the sources was shown. The kinetics of the dismutation reaction of TS and dichlorosilane (DCS) was carried out by the flow method. As a result of regression analysis of experimental data, the rate constants of the direct and reverse dismutation reactions of TS, DCS, and monochlorosilane (MSC) were obtained. The rate constants were used to calculate the equilibrium composition of the reaction mixture. A good agreement between the calculated and experimental data was shown.

Herein, three types of graphene oxides (GOs, GO-M1, GO-M2 and GO-M3) have been successfully prepared from graphite by three different methods and utilized for the removal of Cr(VI) from aqueous solutions. Further, the effects of initial concentration and pH, adsorbent dosage, contact time and temperature on the adsorption performance of GOs were investigated by batch adsorption experiments. Furthermore, the adsorption mechanisms for Cr(VI) adsorption by GOs are mainly the redox reaction and electrostatic attraction, while there are also pore filling, ion exchange and complexation involved in these adsorption processes. The adsorption kinetic and isotherm data indicate that these adsorption processes of GOs on Cr(VI) are dominantly monolayer chemisorption and equilibrium can be reached in 30 min. The saturation adsorption capacities (Qm, 298.15 K) of GO-M1, GO-M2 and GO-M3 for Cr(VI) are estimated to be 3.5412 mg⋅g−1, 2.3631 mg⋅g−1 and 7.0358 mg⋅g−1, respectively. Moreover, the adsorption thermodynamic study showed that these adsorption processes of Cr(VI) by the three types of GOs at 298.15 K to 323.15 K are endothermic, entropy-driven and thermodynamically spontaneous and feasible. Overall, these findings provided vital insights into the mechanism and application of Cr(VI) removal by GOs.

Water is the most important, but at the same time the most anomalous substance on earth. Due to its anomalies it is a challenge even for advanced equations of state like PC-SAFT, CPA and SAFT-VR Mie to predict water’s anomalous properties. It is believed by some researchers that water’s anomalous properties are the result of structural fluctuations. The recently developed DAPT model is consistent with this idea and it is actually able to predict a considerable amount of water’s anomalies, like the density maximum and speed of sound maximum, which is promising, but we show in this work that it produces also some unrealistic results like minima and maxima for isochoric heat capacity. Also DAPT’s accuracy falls at high temperatures/high pressure regions where water behaves like a simple fluid. This reveals further the complexity of the problem since a successful model should treat water as an anomalous and as a simple fluid when necessary. Moreover, all of the association models fail to reproduce some of water’s structural properties and especially the tetrahedral fraction. It may be that a more accurate structural representation of water and the inclusion of steric hindrance and cooperativity effects could provide a more realistic model for water.

Deep eutectic solvents (DESs) have gained attention in recent years as attractive alternatives to traditional solvents. There is a growing number of publications dealing with the thermodynamic modeling of DESs highlighting the importance of modeling the solutions' properties. In this review, we summarize the state-of-the-art in DES modeling as well as its current challenges. We also summarize the various modeling approaches to phase equilibria and properties of DESs with gE-models, EOS and molecular dynamics (MD) simulations. The current gE-model and EOS-based approaches handle DESs as pseudo-components in order to simplify the parameterizations and calculation strategies. However, for the models to become more transferable and predictive, it would be preferable to model the individual DES constituents instead of using the pseudo-components. This implies that validation with more detailed experimental data that includes the distribution of the DES components is also required. MD simulations, in contrast to gE-models and EOS, are capable of providing information about the liquid structure and can predict dynamic properties although, the latter quantities still show some imprecisions. Therefore, insights into the liquid structure of DES systems from MD could also aid in improving present modeling strategies in addition to a better understanding. Finally, the latest developments for DES force fields are discussed as the quality of the applied force fields determine the results of MD simulations.

The new predictive electrolyte model COSMO-RS-ES is evaluated and refined for the calculation of solubilities of salts in mixed solvent systems. It is demonstrated that the model is capable of predicting solid-liquid equilibria at 25 °C for ammonium and alkali metal salts quite accurately in a wide variety of solvent mixtures. Furthermore, through the introduction of Gibbs free energies of transfer of single ions it is shown that the model performance can be improved even further. This new data type also allows for an ion-specific way of evaluating the model for the first time. For some systems when calculating the solubility, larger deviations are observed, but for the vast majority of systems the model delivers good predictions. This shows that COSMO-RS-ES is a valuable tool for calculation of phase equilibria in electrolyte systems especially when the scarcity of data impede the application of models that require a higher number of parameters.

A three-dimensional model has been developed to capture burning properties of ammonium perchlorate(AP)/hydroxyl–terminated polybutadiene(HTPB) composite propellant. Interfacial coupling between condensed domain and gas phase was conducted to obtain the surface temperature and burning rate. The condensed phase was governed by heat transfer equation, and reacting Navier-Stokes equations with pressure dependent 3-step and 12-species global kinetics were adopted. The current model was verified and validated by comparing with analytical and experimental results. Thereafter, the influences of ellipsoidal AP's orientation and aspect ratio on the burning rate were investigated. The flame structure of a typical propellant with ellipsoidal AP particle was examined, and the characteristic heights of multiple flames were determined. Besides, the flame properties of a typical propellant with spherical/ellipsoidal AP particle and different AP topologies were analyzed. Furthermore, the temperature sensitivity coefficients were calculated for composite propellant with different AP contents and sizes, matching reasonably with the experimental results.

This study aimed to experimentally investigate the kinetic compensation effects (KCEs) during Loy Yang brown coal char gasification in an O2 environment at atmospheric pressure in a fluidized-bed g...

The thermodynamic properties of MgCaSi and its mother phase Ca2Si are comparatively investigated from ab initio calculations and quasi-harmonic Debye-Gruneisen model. At 0 K, MgCaSi is more thermodynamically stable. Under high temperature, the advantage of higher thermodynamically stability of MgCaSi is reduced, originating from the less negative entropy contribution because the thermodynamic entropy of MgCaSi increases more slowly with temperature and the entropy values are slightly smaller. With increasing temperature, the anti-softening ability for MgCaSi is slightly smaller due to the slightly faster decrease trend of bulk modulus than that of Ca2Si, although the bulk modulus of MgCaSi is higher in the whole temperature range considered. The thermal expansion behaviors of both MgCaSi and Ca2Si exhibit similar increase trend, although thermal expansion coefficient of MgCaSi is slightly lower and the increases is slightly slower at lower temperature. The isochoric heat capacity CV and isobaric heat capacity CP of MgCaSi and Ca2Si rise nonlinearly with temperature, and both CV are close to the Dulong–Petit limit at high temperature due to the negligibly small electronic contribution. The Debye temperature of both phases decrease with increasing temperature, and the downtrend for MgCaSi is slightly faster. However, MgCaSi possess slightly higher Debye temperature, implying the stronger chemical bonds and higher thermal conductivity than the mother phase Ca2Si. The Gruneisen parameter of MgCaSi and Ca2Si increase slightly with temperature, the values of MgCaSi are slightly larger. The investigation of electronic structures shows that with substitution of partial Ca by Mg in Ca2Si, the stronger Mg Si, Mg Ca and Si Si covalent bonds are formed, and plays a very significant role for the structural stability and mechanical properties.