Synthesis and Properties of Heterocyclic Cationic Gemini Surfactants

Bhadani, Avinash; Singh, Sukhprit; Sakai, Hideki; Abe, Masahiko

doi:10.1002/9781119075691.ch43

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…The mass spectra of gemini surfactants were recorded on a JEOL JMS-T100CS instrument (JEOL Japan) using ESI as an ion source. 1 H NMR and 13 C NMR spectra were recorded on a JEOL-ECP500 instrument (JEOL Japan) using CDCl 3 as a solvent and tetramethylsilane as an internal standard.…”

Section: Discussionmentioning

confidence: 99%

“…Gemini surfactants are being increasingly investigated for various applications ranging from materials science to biomedical sciences . These surfactants demonstrate superior physicochemical properties compared to monomeric surfactants and are able to self-aggregate into micelles at relatively lower concentration compared to their monomeric analogues . Cationic geminis are an important category of surfactants containing two positively charged headgroups and two hydrophobic tails often connected by a spacer .…”

Section: Introductionmentioning

confidence: 99%

“…1 These surfactants demonstrate superior physicochemical properties compared to monomeric surfactants and are able to self-aggregate into micelles at relatively lower concentration compared to their monomeric analogues. 1 Cationic geminis are an important category of surfactants containing two positively charged headgroups and two hydrophobic tails often connected by a spacer. 2 These surfactants are able to interact with a wide variety of molecules and materials in nature and possess unique characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Physicochemical Evaluation of Micellar Solution and Lyotropic Phases Formed by Self-Assembled Aggregates of Morpholinium Geminis

et al. 2017

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The micellar solution and the lyotropic liquid crystalline phases formed by gemini surfactants containing morpholinium headgroups are investigated for their self-aggregation and physicochemical properties in water. These gemini surfactants demonstrated good surface activity because they are able to undergo micellization at lower concentration and form nanosized micellar aggregates in dilute aqueous solution. The binary mixture of the morpholinium gemini surfactant–water system is investigated over a wide range of concentrations. The micellar solution of the morpholinium gemini surfactants demonstrated Newtonian fluidlike behavior between 10 and 50 wt % as the observed viscosities were independent of the applied shear rate. At higher concentration, morpholinium geminis formed self-assembled lyotropic phases in water. These liquid crystalline phases were characterized by small-angle X-ray scattering and polarized optical microscopy techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physicochemical Evaluation of Micellar Solution and Lyotropic Phases Formed by Self-Assembled Aggregates of Morpholinium Geminis

et al. 2017

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“…The cmc dataset of gemini cationic surfactant were collected from multiple publications 20,21,22,23,24,25 . This dataset containing 231 compounds of gemini cationic surfactants including with their cmc value.…”

Section: Experimental Materials and Methods Data Preparationmentioning

confidence: 99%

“…Gemini cationic surfactants, which containing two positively charged headgroups, are generally used for most of the gemini surfactants research studies. The positively charged head groups are quaternary ammonium, immidazolium, pyridinium, esterified quaternaries, etc 4 . These surfactants have been increasingly used in advances technology domains such as capping agents for the synthesis of nanoparticles and nanorods, enhanced oil recovery process, the fabrication of high-porosity materials, corrosion inhibitors, genetic science and pharmaceutical applications including gene delivery, drug delivery, and antimicrobial activity 5,6,7 .…”

Section: Introductionmentioning

confidence: 99%

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

Setiawan¹,

Mudasir²,

Wijaya³

2020

Preprint

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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 2 = 0.95, q 2 = 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R 2 = 0.87, q 2 = 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.

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

Setiawan

Wijaya

Mudasir

2021

4th International Seminar on Chemistry

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Synthesis and Properties of Heterocyclic Cationic Gemini Surfactants

Cited by 5 publications

References 35 publications

Physicochemical Evaluation of Micellar Solution and Lyotropic Phases Formed by Self-Assembled Aggregates of Morpholinium Geminis

Physicochemical Evaluation of Micellar Solution and Lyotropic Phases Formed by Self-Assembled Aggregates of Morpholinium Geminis

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

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

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