QSPR Study of Critical Micelle Concentrations of Nonionic Surfactants

Katritzky, Alan R.; Pacureanu, Liliana; Slavov, Svetoslav; Dobchev, Dimitar A.; Karelson, Mati

doi:10.1021/ie800954k

Cited by 50 publications

(36 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both of these QSPR models were obtained using MLR approach over a set of descriptors based on molecular topology and constitution. Apart from the work of Anoune et al [91], QSPR models intended to the CMC prediction were trained over specific chemical families that are: nonionic [89,[92][93][94][95][96][97][98], cationic [99][100][101], and anionic [90,94,[102][103][104][105][106][107] surfactants. Table 1 presents statistical coefficients of QSPR models listed in the literature.…”

Section: Cmc Predictionmentioning

confidence: 99%

“…This table shows that most of these models have been developed using MLR and we can remark that the size of the database varied from few tens to less than two hundred compounds. Katritzky [96] and Katritzky et al [100,106] have published the QSPR models for the CMC learned using the most comprehensive databases. In these two last works, authors present comparisons between MLR and ANN models.…”

Section: Cmc Predictionmentioning

confidence: 99%

“…In these two last works, authors present comparisons between MLR and ANN models. For nonionic surfactants [96], the ANN model is reported to perform better than the four-descriptor based MLR. In the case of cationic surfactants [100], the fivedescriptor multilinear model developed by Katritzky et al shows comparable performance with respect to that of the ANN model.…”

Section: Cmc Predictionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

Creton

Nieto‐Draghi

Pannacci

2012

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

Oil & Gas Science and Technology -Rev. IFP Energies nouvelles, Vol. 67 (2012), No. 6, pp. 969-982 Copyright c 2013, IFP Energies nouvelles DOI: 10.2516 Prediction of Surfactants' Properties using Multiscale Molecular Modeling Tools: A Review B. Creton, C. Nieto-Draghi and N. Pannacci Bois-Préau, 92852 Rueil-Malmaison -France e-mail: benoit.creton@ifpen.fr -carlos.nieto@ifpen.fr -nicolas.pannacci@ifpen.fr Résumé -Prédiction de propriétés des tensioactifs à l'aide d'outils de modélisation moléculaire : une revue -Une des voies possibles de récupération assistée du pétrole, l'EOR (Enhanced Oil Recovery), consiste en l'injection d'un fluide ASP (Alkaline/Surfactant/Polymer) dans le réservoir dans le but de déplacer le pétrole piégé vers le puits de production. La conception et/ou l'optimisation de mélanges ASP, de tensioactifs ou de mélanges de tensioactifs est donc d'un intérêt premier pour améliorer l'efficacité d'un tel procédé. Les codes de simulation moléculaire développés et largement validés durant ces dernières décennies apparaissent comme des outils incontournables pour la compréhension des effets microscopiques, la prédiction de propriétés de tensioactifs complexes ou encore l'optimisation des structures voire de la composition de mélanges de tensioactifs. Dans cet article, nous présentons une revue des travaux de la littérature sur le potentiel de diverses techniques de simulation moléculaire pour la prédiction de propriétés structurales ou thermophysiques des tensioactifs. Les techniques de simulation auxquelles nous nous sommes intéressés sont la dynamique moléculaire (MD), les simulations Monte Carlo (MC), la dissipative particle dynamics (DPD) ainsi que des approches statistiques faisant un lien direct entre structure et propriété (QSPR, pour Quantitative Structure-Property Relationship). IFP Energies nouvelles, 1-4 Avenue de Abstract -Prediction of Surfactants' Properties using Multiscale Molecular Modeling Tools: A Review -During one of the existing Enhanced Oil Recovery (EOR) procedures, a mixture of Alkaline/Surfactant/Polymer (ASP) is injected into wells in order to INTRODUCTIONThe actual capacity of oil extraction still remains limited and can be roughly estimated to 30-60%, or more, of the reservoir's original oil within the considered field [1]. The production process is typically split into tree distinct phases: -the primary recovery which is the consequence of natural effects such as the pressure of the reservoir; -the secondary recovery which consists in injecting water or gas to move the oil to the wellbore; -the tertiary recovery or Enhanced Oil Recovery (EOR), which gathers techniques such as thermal recovery, chemical or microbial injection [2][3][4]. The chemical injection technique can involve combinations of Alkaline/Surfactant/Polymer (ASP) in which the alkali reacts with some of the crude oil components decreasing the water/oil InterFacial/surface Tension (IFT) [5]. Surfactants are used to reduce the water/oil IFT and the role of Multiscale molecular modeling tools from qua...

show abstract

Section: Cmc Predictionmentioning

confidence: 99%

Section: Cmc Predictionmentioning

confidence: 99%

Section: Cmc Predictionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

Creton

Nieto‐Draghi

Pannacci

2012

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

show abstract

“…Previously, Katritzky and coworkers had successfully employed the CODESSA method to predict the critical micelle concentration value and other physical properties of diverse surfactants. [31][32][33] …”

Section: Descriptor Generationmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

Quantitative Structure–Activity/Property Relationships: The Ubiquitous Links between Cause and Effect

et al. 2012

View full text Add to dashboard Cite

Current literature demonstrates that almost every area of chemical and life sciences, as well as technology utilizes quantitative structure‐activity/property relationships (QSAR/QSPR) to accelerate development and increase efficiency. The designs of pharmaceuticals, agrichemicals, and consumer products as well as the assessment of their toxicity and environmental impact have become major areas of application QSAR/QSPR; the methods of which also penetrate into relatively new applications such as materials science and nanotechnology. In terms of methodology development the new trend is the integration of QSAR/QSPR with adjacent computational methods such as virtual screening and molecular dynamics. Such synergy offers unique opportunities and heralds a new era of computer‐aided molecular design.

show abstract

QSPR Study of Critical Micelle Concentrations of Nonionic Surfactants

Cited by 50 publications

References 53 publications

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

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

Quantitative Structure–Activity/Property Relationships: The Ubiquitous Links between Cause and Effect

Contact Info

Product

Resources

About