تعیین عدد تجمع سورفکتانت‌های آنیونی بر اساس تکنیک هدایت سنجی: به‌کارگیری تکنیک مدل‌سازیQSAR-ANN برای پیش‌بینی عدد تجمع سورفکتانت‌ها

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

گروه شیمی، دانشکده شیمی، دانشگاه سمنان، سمنان، ایران

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

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

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شیمى کاربردى روز
دوره 17، شماره 63
تیر 1401
صفحه 87-108

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