مهندسی شیمی ایران

مهندسی شیمی ایران

سنتز سبز نانوذرات نقره بااستفاده‌از عصارۀ برگ درخت انجیر و حرارت‌دهی با گرمکن همزن‌دار: مقایسۀ مدل‌سازی روش‌های شبکۀ عصبی و طراحی فاکتور‌یل

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار مهندسی شیمی، دانشگاه کردستان
2 دانشیار مهندسی شیمی، دانشگاه کردستان
چکیده
در تحقیق حاضر بهمقایسۀ روشهای شبکۀ عصبی و فاکتوریل در سنتز سبز نانوذرات نقره بابهره‌گیریاز عصارۀ برگ درخت انجیر پرداختهشد. متغیرهای مستقلِ درنظرگرفتهشده شامل: دما، زمان، سرعت همزن و pH بود که هرکدام در 3 سطح مختلف، بررسی و با روش فاکتوریل به طراحی آزمایش درآمد. پاسخهای درنظرگرفته‌شده، شامل: میانگین اندازۀ ذرات، پتانسیل زتا و شاخص پراکندگی بود. نتایج نشان‌داد که کمترین میانگین اندازۀ نانوذرات نقره (25 نانومتر) با کمترین شاخص پراکندگی (0/189) و بالاترین پتانسیل زتا یا بیشترین پایداری (20/1 میلیولت) در دمای 40 درجۀ سلسیوس و مدت زمان سنتز 30 دقیقه، با سرعت به‌همزدن 400 دوردردقیقه و در pH خنثی (7) حاصلشد. هم‌چنین، از شبکۀ عصبی برای پیش‌بینی سه متغیر وابستۀ درنظرگرفته‌شده به‌عنوان تابعیاز متغیرهای مستقل استفاده‌شد. نتایج مدل‌سازی شبکۀ عصبی دقت بالایی را بهمنظور پیش‌بینی متغیر هدف نشان‌داد؛ به طوریکه، مقادیر متوسط خطای نسبی (MRE) برای سه متغیر وابستۀ میانگین اندازۀ ذرات، شاخص پراکندگی و پتانسیل زتا به‌ترتیب برابر با 1/99، 10/5 و 2/74 درصد بود که درمقایسه‌با روش فاکتوریل که مقادیر آن برابربا 2/43، 0/47 و 8/05 درصد بود، باعث بهبود چشم‌گیر پیش‌بینی پتاسیل زتا و بهبود نسبی میانگین اندازۀ ذرات شد. از میان سه خروجی درنظرگرفتهشده، دقت شبکه‌ها برای تخمین میانگین اندازۀ ذرات و به‌خصوص، پتانسیل زتا از مدل فاکتوریل بهتر بودهاست. این نتایج مدل‌سازی شبکۀ عصبی در بهینه‌سازی فرایندهای سنتز نانوذرات و تسهیل طراحی تجربیات مکرر مهم شناخته‌می‌شود و می‌تواند به توسعۀ روش‌های پایدارتر و کارامدتر در نانو فناوری منجرشود.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Green Synthesis of Silver Nanoparticles Using Fig Leaf Extract and Heating with a Stirred Heater: Comparative Evaluation of Neural Network Modeling and Factorial Design Techniques

نویسندگان English

O. Ahmadi 1
R. Beigzadeh 2
1 Assistant Professor of Chemical Engineering, University of Kurdistan
2 Associate Professorof Chemical Engineering, University of Kurdistan
چکیده English

In this research, neural network and factorial methods were compared for the green synthesis of silver nanoparticles using fig leaf extract.The examined independent variables included temperature, time, stirrer speed, and pH, each assessed at three distinct levels. The experimental design employed the factorial method. The responses analyzed encompassed average particle size, dispersity index, and zeta potential. The results indicated that the smallest average size of silver nanoparticles (25 nm), alongside the lowest dispersion index (0.189) and the highest zeta potential (20.1 mV), was attained at a temperature of 40°C,a synthesis time of 30 minutes, a stirring speed of 400 rpm, and neutral pH (7). Furthermore, a neural network was utilized to predict the three dependent variables based on the independent variables. The results of the neural network modeling demonstrated high accuracy in predicting the target variables, with average relative error (MRE) values for mean particle size, dispersion index, and zeta potential being 1.99, 0.51, and 74.2, respectively. In contrast, the factorial method yielded MRE values of 2.43, 0.47, and 8.05, highlighting a significant improvement in the prediction of zeta potential and a relative enhancement in the prediction of average particle size. Among the three considered outputs, the accuracy of the ANNs for estimating the average particle size and especially the zeta potential was better than the factorial model. These results of neural network modeling provide great importance in optimizing nanoparticle synthesis processes and facilitating the design of repeated experiments, which can lead to the development of more stable and efficient methods in nanotechnology.

کلیدواژه‌ها English

Fig Leaf Extract
Green Synthesis
Neural Network
Silver Nanoparticles
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