QSPR modeling of nonionic surfactant cloud points: An update

Ren, Yaoyao; Zhao, Baowei; Chang, Qing; Yao, Xiaojun

doi:10.1016/j.jcis.2011.02.042

Cited by 16 publications

(12 citation statements)

References 21 publications

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“…This methodology has been applied to a very wide range of properties, from which we can only name a few. Surfactant cloud points [13], pKa [14], normal boiling points [15] and another one in which the impressive number of 17768 pure chemical compounds were considered and which includes an overview of previous models [16], octanol-water partition coefficients [17] and homogeneous catalysis [18]. The observation though is that accuracy and reliability are often still too limited, something we will further comment on after the next paragraph on the Group Contribution methodology.…”

Section: Current Methodsmentioning

confidence: 99%

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Meier

2019

Applied Sciences

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Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

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Section: Current Methodsmentioning

confidence: 99%

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Meier

2019

Applied Sciences

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“…Eighty two nonionic surfactants were collected from a various research, [3,[21][22][23][24][25][26][29][30] , including linear alkyl, 1402 C. GUO ET AL. branched alkyl, cyclic alkyl, alkyl phenyl ethoxylates, ethoxylates=propoxylates copolymers, carbonhydrate ethoxylates, and gemini alkyl ethoxylates with flexible builder.…”

Section: Data Setmentioning

confidence: 99%

“…Twelve data were selected as the test set similar with Ren Yueying groups work. [26] Another two structures randomly selected as the part test set of 81 structures (see Table 1). Table 3 lists the result of different models.…”

Section: Gp and Other Statistical Modelingmentioning

confidence: 99%

“…Recently, Hu Zhide et al [25] performed an accurate QSPR (quantitative structure-property relationship) model used support vector machine (SVM) for the same set of 62 structures as Huibers with four descriptors selected by the heuristic method and evaluated the model with external test set. More recently, Ren et al [26] update the model with four descriptors selected by genetic algorithm (GA) based on SVM and projection pursuit regression (PPR) machine learning methods. The optimum result of R 2 test is 0.9903 and 0.9904, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Toward a Quantitative Model and Prediction of the Cloud Point of Normal Nonionic Surfactants and Novel Gemini Surfactants with Heuristic Method and Gaussian Process

Chen

Ren

Zhou

et al. 2012

Journal of Dispersion Science and Technology

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A systematic study of integrating statistical modeling and experimental analysis to investigate the cloud point (CP) and environmental risk of 82 structurally diverse nonionic surfactants is performed. During this procedure, the structural profiles of the studied compounds are characterized using hydrophilic domain and the whole molecular. Hundreds of descriptors, including constitutional, topological, geometrical, and electrostatic were calculated by the CODESSA program, and the resulting variables of the characterization selected by heuristic method are then modeled by the Gaussian process (GP). A variety of regression techniques, including MLR, PLS, SVM, and LSSVM are performed to a comprehensive comparison with GP on the basis of statistical analysis and experimental properties, in conjunction with the sophisticated variable selection methods, that is, empirical heuristic strategy. Among all the built models, the most predictable one is constructed based on the GP modeling combination of heuristic variable selection related to hydrophilic domain, with its predictive coefficient of determination (r 2 pred ) and root-mean-square error of prediction (RMSP) on external independent test set of 0.962 and 5.200, respectively. The statistic model shows that the CP phenomenon is a comprehensive interaction of relative molecular weight, moments of inertia A, and topological structure account for hydrophilic part.

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“…The clustering and further CP phenomenon is mainly induced by a temperature rise for nonionic surfactants [13][14][15], while a counterion effect is of particular impact in both the micellization and the clustering of micelles for ionic ones [16][17][18]. A plenty of works are aimed at revealing the correlation of the phase transitions in solutions of nonionic and ionic surfactants with both nature and size of their hydrophilic moieties [15,18,19]. It is worth noting that phase transitions in aqueous solutions of surfactants and their binding with metal ions are of great significance from both fundamental and practical points of view.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and aggregation properties of new biodegradable amphiphilic derivatives of p-tert-butylphenol for green separation of Gd(III) ions

Stepanov

Vasilieva

Валеева

et al. 2015

Colloids and Surfaces A: Physicochemical and Engineering Aspect

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QSPR modeling of nonionic surfactant cloud points: An update

Cited by 16 publications

References 21 publications

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Toward a Quantitative Model and Prediction of the Cloud Point of Normal Nonionic Surfactants and Novel Gemini Surfactants with Heuristic Method and Gaussian Process

Synthesis and aggregation properties of new biodegradable amphiphilic derivatives of p-tert-butylphenol for green separation of Gd(III) ions

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