Application of QSPR to Binary Polymer/Solvent Mixtures: Prediction of Flory‐Huggins Parameters

Xu, Jie; Liu, Hongtao; Li, Wenbin; Zou, Hantao; Xu, Weilin

doi:10.1002/mats.200800063

Cited by 14 publications

(9 citation statements)

References 41 publications

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“…A number of works deal with the properties of a solution. Miller Chou and Koenig [108] presented a review on the solubility of poly mers. Application of QSPR methods to polymer-solvent binary systems for calculating the Flory-Hug gins interaction parameter χ 12 was considered by Jie Xu et al [109].…”

Section: Discussionmentioning

confidence: 99%

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Methods for calculating the physical properties of polymers

Аскадский

2015

Ref. J. Chem.

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The approaches that derive the best predictions of physical properties of polymers from the chemical structure of their repeating unit are the van Krevelen, Bicerano, and Askadskii-Matveev. The potential of these three approaches is analyzed. All of the approaches are computerized and allow online predictions to be made. The PDTools and SYNTHIA programs are briefly overviewed, and the Cascade software (developed at the Nesmeyanov Institute of Organoelement Compounds) is described in detail. All of the approaches and the corresponding computer programs make it possible to estimate over 120 physical properties of polymers, including their volumetric, thermal, mechanical, thermophysical, optical, dielectric, and barrier properties. Particular attention is focused on the solu bility and miscibility of polymers and on the properties of copolymers, polymer blends, and nanocom posites. The Cascade software provides the means to carry out computer syntheses of polymers with preset properties, the intervals of which are input by the user, and to calculate the dependences of these properties on temperature, plasticizer and nanoparticle concentrations, and nanoparticle size and shape.

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Section: Discussionmentioning

confidence: 99%

“…For the polymer 1c + polymer 2 blend, we have (108) The dependences of the glass transition temperature on the mole fraction of polymer 2 for the blends considered are plotted in Fig. 12.…”

Section: Effect Of the Molecular (Linear Branched Other) Architectumentioning

confidence: 99%

Methods for calculating the physical properties of polymers

Аскадский

2015

Ref. J. Chem.

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“…QSSR OF PHENOLS SOLUBILITY 583 tically significant in a particular model, which means that their influence on the response variable is not merely by chance. 34 A smaller t-probability suggests a more significant descriptor. The t-probability values of the three descriptors are very small, indicating that all of them are highly significant descriptors.…”

Section: Mlr Modelmentioning

confidence: 99%

Modeling of linear and nonlinear quantitative structure property relationships of the aqueous solubility of phenol derivatives

Kherouf

Bouarra

Bouakkadia

et al. 2019

JSCS

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Quantitative structure-solubility relationships (QSSR) are considered as a type of Quantitative structure-property relationship (QSPR) study in which aqueous solubility of chemicals are related to chemical structure. In the present work, multiple linear regression (MLR) and artificial neural network (ANN) techniques were used for QSSR studies of the water solubility of 68 phenols (phenol and its derivatives) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and a genetic algorithm (GA), the descriptors that resulted in the best fitted models were selected. After descriptor selection, multiple linear regression (MLR) was used to construct a linear QSSR model. The R 2 = 91.0 %, LOO 2 Q = 89.33 %, s = 0.340 values of the model developed by MLR showed a good predictive capability for log S values of phenol and its derivatives. The results of MLR model were compared with those of the ANN model. the comparison showed that the R 2 = 94.99 %, s = 0.245 of ANN were higher and lower, respectively, which illustrated an ANN presents an excellent alternative to develop a QSSR model for the log S values of phenols to MLR. age to the environment, plants, animals and human health. The compounds penetrate ecosystems as the result of drainage of municipal or industrial sewage to surface water. 4 Therefore, it is vital to protect the environment and prevent their behavior by studying their physicochemical properties.Aqueous solubility is the concentration of a chemical in the aqueous phase, when the solution is in equilibrium with the pure compound in its usual phase (gas, liquid or solid) under standard conditions of temperature and pressure. 5 Aqueous solubility is one of the major physiochemical properties to be optimized in pharmaceutical and environmental studies; it is related to absorption and distribution in ADME-Tox (absorption, distribution, metabolism, excretion-toxicity).Experimental determination of compound solubility is not easily managed, or even possible, when working with large chemical libraries. 6 Alternately, the quantitative structure-property relationship (QSPR) provides a promising method for the prediction of solubility using descriptors derived solely from the molecular structure to fit experimental data. The QSPR approach attempts to establish simple mathematical relationships to describe the correlation of a given property to molecular structures for a set of compounds. 7 The advantage of this method lies in the fact that it requires only knowledge of the chemical structure and is not dependent on any experimental property. Moreover, it could be used for the prediction of the properties of new compounds.Recently, several works reported QSPR studies on the solubility and other properties. Warne et al. 8 reported the utility of thirty-nine molecular descriptors and physicochemical properties to model the solubility (S) and octanol-water partition coefficient (K ow ) of thirty-one lipophilic or...

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“…[70] Also, gpHSP finds applications in the design of structural materials, such as optoelectronics, sensors, biocatalysis, and thermal and electromagnetic shielding. [71,72,73]…”

Section: Domains Of Applicationmentioning

confidence: 99%

A Bayesian Approach to Predict Solubility Parameters

Sánchez-Lengeling¹,

Roch²,

Perea³

et al. 2018

Preprint

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Solubility is a ubiquitous phenomenon in many aspects of science. While solubility can be determined by considering the cohesive forces in a liquid via Hansen solubility parameters (HSP), the prediction is often done using Quantitative structure-property relationship models due to its low computational cost. Herein, we report an interpretable and versatile probabilistic approach (gpHSP) to determining HSP. Our model is based on Gaussian processes (GP), a Bayesian machine learning approach, which also provides uncertainty bounds to prediction. gpHSP achieves its flexibility by leveraging a variety of input data, such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculations. gpHSP is built on experimentally determined HSP: a general solvents set aggregated from literature, and a polymer set, in-house characterized. In both sets, we obtained a high degree of agreement, surpassing well-established machine learning methods. We demonstrate the general applicability of gpHSP to miscibility of organic semiconductors, drug compounds and in general solvents, which can be further extended to other domains. gpHSP is a fast and accurate toolbox, which could be applied to molecular design for solution processing technologies.<br>

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Application of QSPR to Binary Polymer/Solvent Mixtures: Prediction of Flory‐Huggins Parameters

Cited by 14 publications

References 41 publications

Methods for calculating the physical properties of polymers

Methods for calculating the physical properties of polymers

Modeling of linear and nonlinear quantitative structure property relationships of the aqueous solubility of phenol derivatives

A Bayesian Approach to Predict Solubility Parameters

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