scholarly journals Generic QSPR study for predicting critical micelle concentration of gemini cationic surfactants using the online chemical modeling environment (OCHEM)

2021 ◽  
Author(s):  
Ely Setiawan ◽  
Karna Wijaya ◽  
Mudasir Mudasir
Keyword(s):  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Chemical Modeling ◽  
Modeling Environment ◽  
Qspr Study

scholarly journals QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

2020 ◽  
Author(s):  
Ely Setiawan ◽  
Mudasir Mudasir ◽  
Karna Wijaya
Keyword(s):  
Machine Learning ◽  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Molecular Descriptors ◽  
Consensus Model ◽  
Structure Property ◽  
Learning Methods ◽  
Data Set ◽  
Chemical Modeling ◽  
Machine Learning Methods

<p> A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R<sup>2</sup> = 0.95, q<sup>2 </sup>= 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R<sup>2</sup> = 0.87, q<sup>2</sup> = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.</p>

scholarly journals QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

2020 ◽  
Author(s):  
Ely Setiawan ◽  
Mudasir Mudasir ◽  
Karna Wijaya
Keyword(s):  
Machine Learning ◽  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Molecular Descriptors ◽  
Consensus Model ◽  
Structure Property ◽  
Learning Methods ◽  
Data Set ◽  
Chemical Modeling ◽  
Machine Learning Methods

<p> A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R<sup>2</sup> = 0.95, q<sup>2 </sup>= 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R<sup>2</sup> = 0.87, q<sup>2</sup> = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.</p>

scholarly journals Poly(Oxyethylene)-Amidoamine Based Gemini Cationic Surfactants for Oilfield Applications: Effect of Hydrophilicity of Spacer Group

Materials ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1046 ◽  
Author(s):  
S. M. Shakil Hussain ◽  
Ahmad Mahboob ◽  
Muhammad Shahzad Kamal
Keyword(s):  
Surface Tension ◽  
High Temperature ◽  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Structural Integrity ◽  
High Salinity ◽  
Heat Stability ◽  
High Heat ◽  
Gravimetric Analysis ◽  
Nacl Solution

Thermal stability, salt tolerance, and solubility in normal and high salinity brine are the major requirements for any surfactant designed for oilfield applications because the surfactant stays in a non-ambient environment inside the reservoir for a long period of time. Herein, a series of new gemini cationic surfactants (GSs) with varying spacer hydrophilicity were synthesized and elucidated using MALDI-ToF-MS, NMR (1H, 13C), as well as FTIR spectroscopy. GSs found to be soluble in normal as well as high salinity brine and aqueous stability tests revealed that GSs possess the ability to retain their structural integrity at high salinity and high temperature conditions because no suspension formation or precipitation was detected in the oven aged sample of GSs at 90 °C for 30 days. Thermal gravimetric analysis displayed a higher decomposition temperature than the real reservoir temperature and the GS with a secondary amine spacer exhibited high heat stability. The significant reduction in surface tension and critical micelle concentration was observed using 1 M NaCl solution in place of deionized water. The difference in surface tension and critical micelle concentration was insignificant when the 1 M NaCl solution was replaced with seawater. The synthesized surfactants can be utilized for oilfield applications in a challenging high temperature high salinity environment.

Determination of the Critical Micelle Concentration of Cationic Surfactants: An Undergraduate Experiment

1999 ◽  
Vol 76 (1) ◽  
pp. 93 ◽  
Author(s):  
Xirong Huang ◽  
Jinghe Yang ◽  
Wenjuan Zhang ◽  
Zhenyu Zhang ◽  
Zesheng An
Keyword(s):  
Critical Micelle Concentration ◽  
Cationic Surfactants

Surface‐enhanced Raman scattering as a tool to study cationic surfactants exhibiting low critical micelle concentration

2019 ◽  
Vol 51 (3) ◽  
pp. 452-460 ◽  
Author(s):  
George N. Mathioudakis ◽  
Amaia Soto Beobide ◽  
Georgios Bokias ◽  
Petros G. Koutsoukos ◽  
George A. Voyiatzis
Keyword(s):  
Critical Micelle Concentration ◽  
Raman Scattering ◽  
Cationic Surfactants ◽  
Surface Enhanced Raman Scattering ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Enhanced Raman Scattering
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