Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space
Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sen...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2013/305713 |
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| author | Parvaneh Shabanzadeh Norazak Senu Kamyar Shameli Maryam Mohaghegh Tabar |
| author_facet | Parvaneh Shabanzadeh Norazak Senu Kamyar Shameli Maryam Mohaghegh Tabar |
| author_sort | Parvaneh Shabanzadeh |
| collection | DOAJ |
| description | Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm. |
| format | Article |
| id | doaj-art-9f5949caa537430bb0992b2dcaa2cd2d |
| institution | OA Journals |
| issn | 2090-9063 2090-9071 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Chemistry |
| spelling | doaj-art-9f5949caa537430bb0992b2dcaa2cd2d2025-08-20T02:20:30ZengWileyJournal of Chemistry2090-90632090-90712013-01-01201310.1155/2013/305713305713Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer SpaceParvaneh Shabanzadeh0Norazak Senu1Kamyar Shameli2Maryam Mohaghegh Tabar3Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Selangor, MalaysiaDepartment of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Selangor, MalaysiaDepartment of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Selangor, MalaysiaDepartment of Mathematics, Faculty of Science, University of Gilan, Rasht 1914, IranArtificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.http://dx.doi.org/10.1155/2013/305713 |
| spellingShingle | Parvaneh Shabanzadeh Norazak Senu Kamyar Shameli Maryam Mohaghegh Tabar Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space Journal of Chemistry |
| title | Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space |
| title_full | Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space |
| title_fullStr | Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space |
| title_full_unstemmed | Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space |
| title_short | Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space |
| title_sort | artificial intelligence in numerical modeling of silver nanoparticles prepared in montmorillonite interlayer space |
| url | http://dx.doi.org/10.1155/2013/305713 |
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