Combinatorial screening of ionic liquid extractant for removal of methanol from methylal

Gui, Chengmin; Li, Zhiwei; Lei, Zhigang; Dong, Yichun

doi:10.1016/j.ces.2021.117317

Cited by 23 publications

(8 citation statements)

References 65 publications

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“…2 usually indicate a strong interaction and poor interaction, respectively. 47–51 Most IGM isosurfaces in the region between the CPT and cation in ten CPT–IL systems are colored green, confirming that vdWs forces represent the main contribution to the weak interactions in these systems. By comparison, the blue isosurfaces between the H1 atom of CPT and Br, F, Cl, and O atoms of anions, corresponding to a high electron density, reveal the presence of a strongly attractive interaction ( i.e.…”

Section: Resultsmentioning

confidence: 72%

Theoretical study on the solvation mechanism of camptothecin in ionic liquids

Zhu

Huang

2023

Phys. Chem. Chem. Phys.

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Section: Resultsmentioning

confidence: 72%

Theoretical study on the solvation mechanism of camptothecin in ionic liquids

Zhu

Huang

2023

Phys. Chem. Chem. Phys.

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“…29 The V-rescale thermostat 30 and Parrinello−Rahman barostat 31 were used to calculate the generated phase. 32 First, the energy of the system was minimized by the steepest descent method, and the minimum system was balanced by the NVT ensemble for 1 ns with a temperature gradient. Then, the equilibrium simulation was performed at the isothermal−isobaric ensemble (NPT).…”

Section: Methodsmentioning

confidence: 99%

“…The equilibrium phase was calculated using a Berendsen thermostat and Berendsen barostat . The V-rescale thermostat and Parrinello–Rahman barostat were used to calculate the generated phase . First, the energy of the system was minimized by the steepest descent method, and the minimum system was balanced by the NVT ensemble for 1 ns with a temperature gradient.…”

Section: Methodsmentioning

confidence: 99%

Novel Strategy of Machine Learning for Predicting Henry’s Law Constants of CO₂ in Ionic Liquids

Zhang

Wang

Ren

et al. 2023

ACS Sustainable Chem. Eng.

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The Henry’s law constant (HLC) of CO2 is an important parameter to characterize its absorption by solvents. However, the HLC data is incomplete in the NIST ionic liquid (IL) Thermo database. In this work, molecular dynamics (MD) simulations were used to accurately calculate the HLC of CO2 in ILs. Then, machine learning (ML) and artificial neural networks were combined to learn from limited data to rapidly expand the database. Three rapid HLC prediction models were established by cross-validation and grid search based on rigorous data sets of IL’s structure, temperature, and HLC. The determination coefficient, mean absolute error, and mean square error of the optimal multilayer perceptron model were 0.9817, 0.3023, and 0.2104, respectively. Compared with the reported models, the prediction model established in this work has better versatility and higher prediction accuracy. The HLC matrix of CO2 in 306 ILs was completed, which proves the great potential and significance of the MD–ML method in the expansion of green solvent database. Finally, the structure–activity relationship of CO2 absorption by the binary IL mixtures was studied.

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“…44−49 Twenty incipient configurations of Thr-CILs units were then randomly generated via the genmer package, followed by the optimization of those configurations calculated by the MOPAC 2016 program on a PM7 basis and a Molclus program at the B3LYP-D3(BJ)/ma-TZVP level. 42,50 Subsequently, the lowest energy structure of the Thr-CIL unit was picked to do an independent gradient model based on Hirshfeld partition (IGMH) analysis using a Multiwfn package. 51,52 The IGMH isosurface diagrams were obtained with VMD 1.9 software to reveal the chiral recognition sites between the Thr enantiomers and CILs.…”

Section: Quantum Mechanics Calculationmentioning

confidence: 99%

Enhanced Direct Crystallization Resolution of Racemates Mediated in Chiral Ionic Liquid Cosolvent Mixtures: Experimental, Statistical, and QM Simulation Studies

Qin

Lou

et al. 2023

Crystal Growth & Design

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Four chiral ionic liquids (CILs) of 1-hexyl-3-methylimidazolium-l-2-aminobutyrate ([Hmim][l-2-AbA]), 1-hexyl-3-methylimidazolium-d-2-aminobutyrate ([Hmim][d-2-AbA]), di-1-hexyl-3-methylimidazolium-l-malate ([Hmim]2[l-MA]), and di-1-hexyl-3-methylimidazolium-d-malate ([Hmim]2[d-MA]) were first prepared by a neutralization method and adequately characterized. In light of a gravimetric method, the molar fraction solubilities of dl- and d- together with l-threonine (Thr) were then acquired in the binary cosolvent mixtures (CM) of CILs and water, with different mass fractions (ω) of CILs under 101.3 kPa from 283.15 to 333.15 K. In CIL cosolvent mixtures (CCM), a cosolvency phenomenon was observed with the biggest solubilization and discrepant solubilities of Thr enantiomers, with ω of 0.3. Meanwhile, the asymmetry phenomenon was also found from the ternary phase diagram of Thr enantiomers and CCM, in which the enantiomeric excess values at the eutectic point (eeeut) were sensitive to temperature. Subsequently, the nucleation experiments and appearance probability statistics confirmed that CCM with ω of 0.3 (CCM-0.3) can act as excellent nucleation inhibitors which preferred to suppress the nucleation of enantiomeric crystals with the opposite chirality to them, leading to the preferential nucleation of enantiomeric crystals with the same chirality. Furthermore, by coupling seeds and CCM-0.3, the yield and yield ratio of l-Thr products through a direct antisolvent crystallization resolution were improved greatly compared with that in water, where the largest yield ratio was up to 27.47%. At the same time, isosurface diagrams and binding energy calculations by quantum mechanics (QM) revealed that the higher chiral recognition ability of [Hmim]2[l-MA] than [Hmim][l-2-AbA] stemmed from the stronger weak interactions. Particularly, the enhanced direct crystallization resolution can be feasibly recycled owing to the stability and recyclability of the CILs.

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Combinatorial screening of ionic liquid extractant for removal of methanol from methylal

Cited by 23 publications

References 65 publications

Theoretical study on the solvation mechanism of camptothecin in ionic liquids

Theoretical study on the solvation mechanism of camptothecin in ionic liquids

Novel Strategy of Machine Learning for Predicting Henry’s Law Constants of CO₂ in Ionic Liquids

Enhanced Direct Crystallization Resolution of Racemates Mediated in Chiral Ionic Liquid Cosolvent Mixtures: Experimental, Statistical, and QM Simulation Studies

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Combinatorial screening of ionic liquid extractant for removal of methanol from methylal

Cited by 23 publications

References 65 publications

Theoretical study on the solvation mechanism of camptothecin in ionic liquids

Theoretical study on the solvation mechanism of camptothecin in ionic liquids

Novel Strategy of Machine Learning for Predicting Henry’s Law Constants of CO2 in Ionic Liquids

Enhanced Direct Crystallization Resolution of Racemates Mediated in Chiral Ionic Liquid Cosolvent Mixtures: Experimental, Statistical, and QM Simulation Studies

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Novel Strategy of Machine Learning for Predicting Henry’s Law Constants of CO₂ in Ionic Liquids