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تعیین عدد تجمع سورفکتانتهای آنیونی بر اساس تکنیک هدایت سنجی: بهکارگیری تکنیک مدلسازیQSAR-ANN برای پیشبینی عدد تجمع سورفکتانتها | ||
| شیمى کاربردى روز | ||
| دوره 17، شماره 63، تیر 1401، صفحه 87-108 اصل مقاله (990.27 K) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/chem.2022.25907.2042 | ||
| نویسندگان | ||
| بهناز عبدوس؛ سیده مریم سجادی* ؛ احمد باقری | ||
| گروه شیمی، دانشکده شیمی، دانشگاه سمنان، سمنان، ایران | ||
| تاریخ دریافت: 04 بهمن 1400، تاریخ بازنگری: 20 اردیبهشت 1401، تاریخ پذیرش: 08 خرداد 1401 | ||
| چکیده | ||
| در این مطالعه، از یک روش مدلسازی جدید با استفاده از مدل QSAR و شبکه عصبی مصنوعی برای پیشبینی عدد تجمع برخی از سورفکتانتهای آنیونی در محلول آبی در دمای 25 درجه سانتیگراد استفاده شده است. عدد تجمع مایسل با استفاده از اندازهگیریهای هدایت الکتریکی و روش اوانس برای سورفکتانتهای آنیونی در محلولهای آبی تعیین شد. اما نتایج به دست آمده با استفاده از این روش، نتایج حاصل از از روش فلورسانس مطابقت خوبی نداشت از آنجائیکه روش فلورسانس روش دقیقتری برای محاسبه عدد تجمع مایسلها میباشد به همین دلیل از نتایج روش فلورسانس در این مطالعه استفاده شد. به منظور ارتباط ساختار مولکولی این سورفکتانتها با عدد تجمع آنها، مطالعه ارتباط کمی ساختار-خاصیت (QSPR) انجام شد. یک مدل شبکه عصبی مصنوعی (ANN) برای پیشبینی عدد تجمع سورفکتانتهای آنیونی با استفاده از چهار مورد از بیش از 3200 توصیفگر مولکولی، محاسبه شده توسط نرمافزار Dragon، به عنوان متغیرهای ورودی، توسعه داده شد. اهمیت توصیفگرهای انتخابی بر اساس روش ANN محاسبه شدند که ترتیب اهمیت آنها بدین صورت می باشد: nC> X5V> MWC05> MWC04. مجموعه کامل 24 سورفکتانت آنیونی به صورت تصادفی به یک مجموعه آموزشی 16 تایی، یک مجموعه آزمایشی 4 تایی و یک مجموعه اعتبارسنجی 4 تایی تقسیم شدند. همچنین از تحلیل رگرسیون خطی چندگانه (MLR) برای ساخت یک مدل خطی با استفاده از توصیفگرهای مشابه استفاده شد. ضریب همبستگی (R2) و ریشه میانگین مربعات خطا (RMSE) مدلهای ANN و MLR (برای کل مجموعه دادهها) به ترتیب 94/0، 99/4 و 82/0، 38/8 بود. R2بالاتر روش ANN نشان داد که رابطه بین توصیفگرها و عدد تجمع ترکیبات، غیرخطی است. | ||
| کلیدواژهها | ||
| توصیفگرهای مولکولی؛ QSAR؛ شبکه عصبی مصنوعی؛ سورفکتانت آنیونی؛ عدد تجمع | ||
| عنوان مقاله [English] | ||
| Determining the Aggregation Number of Anionic Surfactants based on Conductivity Method: Employing QSAR-ANN Modelling Techniques for Predicting the aggregation number of surfactants | ||
| نویسندگان [English] | ||
| behnaz abdous؛ S. Maryam Sajjadi؛ Ahmad Bagheri | ||
| Department of Chemistry, Faculty of Chemistry, Semnan University, Semnan, Iran | ||
| چکیده [English] | ||
| In this study, a new modeling method using QSAR model and artificial neural network is used to predict the aggregation number of some anionic surfactants in aqueous solution at 25 °C. The micelle aggregation number was determined using electrical conductivity measurements and the Evans method for anionic surfactants in aqueous solutions. However, the obtained results based on conductibvity strategy were not in good agreement with those of fluorescence method. Since the fluorescence method is a more accurate method for calculating the aggregation number of micelles, the results of the fluorescence method have been used in this study. In order to correlate the molecular structure of these surfactants with their aggregation number, a quantitative structure-property relationship (QSPR) study was performed. An artificial neural network (ANN) model was developed to predict the aggregation number of anionic surfactants by using four out of more than 3200 molecular descriptors, calculated by Dragon software, as input variables. The importance of selected descriptors were computed based on ANN method and listed as follows in descending order: nC> X5V> MWC05> MWC04. The complete set of 24 anionic surfactants was randomly divided into a training set of 16, a test set of 4, and a validation set of 4 compounds. Also, multiple linear regression (MLR) analysis was utilized to build a linear model by using the same descriptors. Correlation coefficient (R2) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.94, 4.99 and 0.82, 8.38, respectively. The higher R2 of the ANN method showed that the relationship between the descriptors and the aggregation number of the compounds is nonlinear. | ||
| کلیدواژهها [English] | ||
| Molecular Descriptors, QSAR, Artificial Neural network, Anionic Surfactant, Aggregation Number | ||
| مراجع | ||
|
[1] A. Bagheri, Comparison of the interaction between propranolol hydrochloride (PPL) with anionic surfactant and cationic surface active ionic liquid in micellar phase, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 615 (2021) 126183.
[2] T. Gaudin, P. Rotureau, I. Pezron, G. Fayet, New QSPR Models to Predict the Critical Micelle Concentration of Sugar-Based Surfactants, Industrial & Engineering Chemistry Research, 55 (2016) 11716.
[3] Ž. Medoš, S. Friesen, R. Buchner, M. Bešter-Rogač, Interplay between aggregation number, micelle charge and hydration of catanionic surfactants, Physical Chemistry Chemical Physics, 22 (2020) 9998.
[4] Lindman, B. and H. Wennerström, Micelles. 1980, Springer Berlin / Heidelberg, 1.
[5] J. van Stam, S. Depaemelaere, F.C. De Schryver, Micellar Aggregation Numbers - A Fluorescence Study, Journal of Chemical Education, 75 (1998) 93.
[6] Massart, D. L.; Vandeginste, B. G.; Buydens, L.; Lewi, P.; SmeyersVerbeke, J. Handbook of chemometrics and qualimetrics: Part A; Elsevier Science Inc., 1997.
[7] Wold, S. Chemometrics and Intelligent Laboratory Systems, 30 (1995) 109.
[8] Michielan, L.; Moro, S. Journal of chemical information and modeling, 50 (2010) 961.
[9] Bertolaccini L, Solli P, Pardolesi A, Pasini A (2017) An overview of the use of artificial neural networks in lung cancer research. Journal of thoracic disease 9 (4):924
[10] Willis MJ, Montague GA, Di Massimo C, Tham MT, Morris AJ (1992) Artificial neural networks in process estimation and control. Automatica 28 (6):1181.
[11] W. LI, H. XIE, Y. HUANG, L. SONG, Y. SHAO, K. QIU, Application of Gaussian 09/GaussView 5.0 in Analytical Chemistry Teaching. Journal of Kunming Medical University, 12 (2016) 134.
[12] K.W. Ely Setiawan, Mudasir Mudasir, QSAR modeling for predicting the antifungal activities of gemini imidazolium surfactants against Candida albicans using GA-MLR methods, 2021.
[13] J. Ghasemi, S. Ahmadi, Combination of Genetic Algorithm and Partial Least Squares for Cloud Point Prediction of Nonionic Surfactants from Molecular Structures, Annali di Chimica, 97 (2007) 69.
[14] Ramalingam V, Palaniappan B, Panchanatham N, Palanivel S (2006) Measuring advertisement effectiveness—a neural network approach. Expert systems with applications 31 (1):159.
[15] Zupan J, Gasteiger J, Neural networks in chemistry and drug design. John Wiley & Sons, Inc, (1999).
[16] Fletcher R, A new approach to variable metric algorithms. The computer journal 13 (3) (1970) 317.
[17] Goldfarb D, A family of variable-metric methods derived by variational means. Mathematics of computation 24 (109) (1970) 23.
[18] Goh AT Back-propagation neural networks for modeling complex systems. Artificial intelligence in engineering, 9 (3) (1995) 143.
[19] Iqbal A, Aftab S, A Feed-Forward and Pattern Recognition ANN Model for Network Intrusion Detection. International Journal of Computer Network & Information Security 11 (4) (2019).
[20] Nidhyananthan SS, Shenbagalakshmi V, Assessment of dysarthric speech using Elman back propagation network (recurrent network) for speech recognition. International Journal of Speech Technology 19 (3), (2016) 577.
[21] Ahmed S, IP Traffic forecasting using focused time delay feed forward neural network. Bahria University Journal of Information & Communication Technologies (BUJICT), 2 (1) (2009).
[22] Fermo IR, Cavali TS, Bonfim-Rocha L, Srutkoske CL, Flores FC, Andrade CM, Development of a low-cost digital image processing system for oranges selection using hopfield networks. Food and Bioproducts Processing 125 (2021) 181.
[23] M. Arezoo, B. Ahmad, J. Of Applied Chemistry, 61 (1400) 175, in Persian.
[24] B.W. Barry, R. Wilson, Micellar molecular weights and hydration of ethoxylated anionic and cationic surfactants, Colloid and Polymer Science, 256 (1978) 44.
[25] C. La Mesa, Micelle aggregation numbers from colligative properties, Colloid and Polymer Science, 268 (1990) 959.
[26] Anachkov SE, Danov KD, Basheva ES, Kralchevsky PA, Ananthapadmanabhan KP,.Determination of the aggregation number and charge of ionic surfactant micelles from the stepwise thinning of foam films. Advances in Colloid and Interface Science 183-184, (2012)55.
[27] M. Aoudia, B. Al-Haddabi, Z. Al-Harthi, A. Al-Rubkhi, Sodium Lauryl Ether Sulfate Micellization and Water Solubility Enhancement Towards Naphthalene and Pyrene: Effect of the Degree of Ethoxylation, Journal of Surfactants and Detergents, 13 (2010) 103.
[28] N.V. Lebedeva, A. Shahine, B.L. Bales, Aggregation Number-Based Degrees of Counterion Dissociation in Sodium n-Alkyl Sulfate Micelles, The Journal of Physical Chemistry B, 109 (2005) 19806.
[29] D.J. Jobe, V.C. Reinsborough, Micellar properties of sodium alkyl sulfoacetates and sodium dialkyl sulfosuccinates in water, Canadian Journal of Chemistry, 62 (1984) 280.
[30] H.V. Tartar, A.L.M. Lelong, Micellar Molecular Weights of Some Paraffin Chain Salts by Light Scattering, The Journal of Physical Chemistry, 59 (1955) 1185.
[31] M. Benrraou, B.L. Bales, R. Zana, Effect of the Nature of the Counterion on the Properties of Anionic Surfactants. 1. Cmc, Ionization Degree at the Cmc and Aggregation Number of Micelles of Sodium, Cesium, Tetramethylammonium, Tetraethylammonium, Tetrapropylammonium, and Tetrabutylammonium Dodecyl Sulfates, The Journal of Physical Chemistry B, 107 (2003) 13432.
[32] P. De, K. Roy, Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors, SAR and QSAR in Environmental Research, 29 (2018) 319.
[33] G. Rücker, C. Rücker, On Topological Indices, Boiling Points, and Cycloalkanes, Journal of Chemical Information and Computer Sciences, 39 (1999) 788.
[34] T. Hancock, R. Put, D. Coomans, Y. Heyden, Y. Everingham, A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies, Chemometrics and Intelligent Laboratory Systems, 76 (2005) 185.
[35] Fathollahi M, Sajady H, QSPR modeling of decomposition temperature of energetic cocrystals using artificial neural network. Journal of Thermal Analysis and Calorimetry, 133 (3) (2018) 1663.
[36] N. Fakhari, Z. Kalantar, N. godarzi, Linear and non-linear QSPR study of n-alkanes viscosity over a wide range of temperature and pressure, 2012 | ||
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