Predicting Surfactant Cloud Point from Molecular Structure

Summary -Quantitative structure-property relationship (QSPR) analysis has been directed to a series of pure nonionic surfactants containing linear alkyl, cyclic alkyl, and alkey phenyl ethoxylates. Modeling of cloud point of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. In this study, a genetic algorithm (GA) was applied as a variable selection method in QSPR analysis. The results indicate that the GA is a very effective variable selection approach for QSPR analysis. The comparison of the two regression methods used showed that PLS has better prediction ability than MLR.

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“…Huibers and coworkers 42 . They used the topological descriptors to allow one to account for branching and ring structure in alkanes.…”

Section: Genetic Algorithmmentioning

confidence: 98%

Combination of Genetic Algorithm and Partial Least Squares for Cloud Point Prediction of Nonionic Surfactants from Molecular Structures

Ghasemi

Ahmadi

2006

Annali di Chimica

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“…It is basically affected by its molecular structure [13], its own concentration [14] and the presence of additives [15]. For nonionic surfactant which hydrogen bond can be formed with water, the intensity and number of hydrogen bonds have profound impact on cloud point [16] as well.…”

Section: Cloud Pointmentioning

confidence: 99%

Synthesis and Properties of Novel Non-Ionic Polyurethane Dispersion Based on Hydroxylated Tung Oil and Alicyclic Isocyanates

Yang¹,

Ren²,

Ren³

et al. 2015

MSCE

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Hydroxylated Tung oil (HTO) based nonionic polyurethane dispersion (HTO-NPUD) were synthesized using dicyclohexyl methane diisocyanate (HMDI) and HTO as main hydrophobic materials whereas polyethylene glycol-800 (PEG-800) as hydrophilic chain extender. To effectively study the effects of HTO on properties of NPUD, polypropylene glycol-400(PPG-400) based NPUD was prepared by HMDI reacting with PPG-400 and PEG-800. The structures of those novel nonionic polyurethane dispersions were characterized by FTIR and 1 H NMR. Moreover, particle size and size distribution, cloud point and surface tension had been investigated. Results showed that, by comparing with PPG based NPUD (PPG-NPUD), the introduction of HTO into NPUD result in larger particle size and more uniformed particle size distribution, higher cloud point and lower surface tension.

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“…The QSPR approach has been applied in many different areas, including (a) properties of single molecules such as boiling point [40,41], critical temperature [42], vapor pressure [12,43], flash point [44], auto ignition temperature [45], density [46][47][48], refractive index [49] and melting point [11,50,51]; (b) interactions among different molecular species such as octanol/water partition coefficient [13,52], aqueous solubility of solids, liquids and vapors [47,48,53], solvent polarity scales [50,51], and GC retention time and response factor [129]; (c) surfactant properties such as critical micelle concentration [54], cloud point [55]; and (d) polymer properties such as the polymer glass transition temperature [56], polymer refractive index, and rubber vulcanization acceleration [57].…”

Section: Structure-property Relationship Modelingmentioning

confidence: 99%

Improved structure–property relationship models for prediction of critical properties

Godavarthy

Robinson

Gasem

2008

Fluid Phase Equilibria

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Predicting Surfactant Cloud Point from Molecular Structure

Cited by 113 publications

References 15 publications

Combination of Genetic Algorithm and Partial Least Squares for Cloud Point Prediction of Nonionic Surfactants from Molecular Structures

Combination of Genetic Algorithm and Partial Least Squares for Cloud Point Prediction of Nonionic Surfactants from Molecular Structures

Synthesis and Properties of Novel Non-Ionic Polyurethane Dispersion Based on Hydroxylated Tung Oil and Alicyclic Isocyanates

Improved structure–property relationship models for prediction of critical properties

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