Core-softened water–alcohol mixtures: the solute-size effects

Marques, Murilo Sodré; Hernandes, Vinícius Fonseca; Bordin, José Rafael

doi:10.1039/d1cp00751c

Cited by 8 publications

(15 citation statements)

References 93 publications

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“…The waterlike solvent is monomeric, while the alcohols are linear rigid polymers: methanol is modeled as a dumbbell-one CSW site, one LJ site, ethanol as a trimer-one CSW site, two LJ sites-and propanol as a tetramer-one CSW site, three LJ sites. Detailed information about the simulation methods, parameters and the models can be found in our previous work [71]. For the pure solvent and all mixtures cases, we have observed five distinct phases.…”

Section: The Computational Detailsmentioning

confidence: 86%

“…The NN approach requires only a system snapshot. The last configuration from each (N, P, T) simulation performed in our last work [71] was chosen as input for the NN. All the systems, for different alcohols and concentrations and pure water, are composed by N = 1000 molecules.…”

Section: The Computational Detailsmentioning

confidence: 99%

“…Many experimental and theoretical works have studied these short-chain alcohol/water mixtures [63][64][65][66][67][68][69]. In our recent works, we have performed molecular dynamics simulations with a core-softened (CS) approach to investigate the behavior of methanol-water [70] and water mixtures with methanol, ethanol and 1-propanol [71]. In this approach, the waterlike solvent is modeled as the CSW fluid proposed by Franzese [72].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the hydroxyl group is modeled as a CSW bead, while the hydrophobicity of the polar sites is given by a modified Lennard Jones (LJ) potential. It was found [71] that the addition of distinct concentrations of alcohols with distinct chain lengths lead to the suppression of the crystal phase, with the favoring of the amorphous phase and the existence of the liquid-liquid phase transition-in addition to the waterlike anomalies in the supercooled regime. With a particular interest for this present work, we observe a variety of phases: a high-density liquid (HDL) and a lowdensity liquid (LDL) phase, two solid phases: a body-centered cubic one (bcc-phase I) and a hexagonal close-packed phase (hcp-phase II), and an amorphous solid phase (phase III).…”

Section: Introductionmentioning

confidence: 99%

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Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures

Hernandes

Marques

Bordin

2021

J. Phys.: Condens. Matter

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Characterization of phases of soft matter systems is a challenge faced in many physical chemical problems. For polymorphic fluids it is an even greater challenge. Specifically, glass forming fluids, as water, can have, besides solid polymorphism, more than one liquid and glassy phases, and even a liquid–liquid critical point. In this sense, we apply a neural network algorithm to analyze the phase behavior of a mixture of core-softened fluids that interact through the continuous-shouldered well (CSW) potential, which have liquid polymorphism and liquid–liquid critical points, similar to water. We also apply the neural network to mixtures of CSW fluids and core-softened alcohols models. We combine and expand methods based on bond-orientational order parameters to study mixtures, applied to mixtures of hardcore fluids and to supercooled water, to include longer range coordination shells. With this, the trained neural network was able to properly predict the crystalline solid phases, the fluid phases and the amorphous phase for the pure CSW and CSW-alcohols mixtures with high efficiency. More than this, information about the phase populations, obtained from the network approach, can help verify if the phase transition is continuous or discontinuous, and also to interpret how the metastable amorphous region spreads along the stable high density fluid phase. These findings help to understand the behavior of supercooled polymorphic fluids and extend the comprehension of how amphiphilic solutes affect the phases behavior.

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Section: The Computational Detailsmentioning

confidence: 86%

Section: The Computational Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures

Hernandes

Marques

Bordin

2021

J. Phys.: Condens. Matter

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“…Zn(NO 3 ) 2 Á6H 2 O + H 2 O -Zn 2+ + 2NO The presence of ethanol in our synthesis is an important parameter to control the size of nanorods obtained as has been reported in the literature. 31 Also, it is essential to keep the reactant mixture in the boiling state to enable the formation of ZnO. Otherwise, decomposition will not take place and the product will be impure with the formation of Zn 5 (OH) 6 (CO 3 ) 2 as has been reported in the literature.…”

Section: Papermentioning

confidence: 96%