Modeling of biosynthesized silver nanoparticles in Vitex negundo L. extract by artificial neural network

RSC Advances ◽  
2015 ◽  
Vol 5 (106) ◽  
pp. 87277-87285 ◽  
Author(s):  
Parvaneh Shabanzadeh ◽  
Rubiyah Yusof ◽  
Kamyar Shameli
Keyword(s):  
Neural Network ◽  
Aqueous Solution ◽  
Artificial Neural Network ◽  
Silver Nanoparticles ◽  
Silver Nitrate ◽  
Water Extract ◽  
Vitex Negundo ◽  
Ag Nps ◽  
Artificial Neural

In this study silver nanoparticles (Ag-NPs) are biosynthesized from silver nitrate aqueous solution through a simple and eco-friendly route using water extract ofVitex negundoL. (V. negundo) which acted as a reductant and stabilizer simultaneously.

scholarly journals Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Parvaneh Shabanzadeh ◽  
Norazak Senu ◽  
Kamyar Shameli ◽  
Maryam Mohaghegh Tabar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silver Nanoparticles ◽  
Reaction Temperature ◽  
Chemical Reduction ◽  
Size Control ◽  
Design Parameters ◽  
Ag Nps ◽  
Factors Affecting ◽  
Artificial Neural

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 AgNO3concentration, 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 AgNO3concentration 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 AgNO3of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

Artificial Neural Network Modelling of Green Synthesised Silver Nanoparticles in Bentonite/Starch Bio-Nanocomposite

2018 ◽  
Vol 14 (3) ◽  
pp. 239-251 ◽  
Author(s):  
Anupama Thapliyal ◽  
Roop Krishen Khar ◽  
Amrish Chandra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silver Nanoparticles ◽  
Mean Square Error ◽  
Reduction Method ◽  
Mean Square ◽  
Ann Model ◽  
Ag Nps ◽  
Feed Forward ◽  
Artificial Neural

Background: In this study, computational Artificial Neural Network (ANN) model is applied for optimisation and evaluation of silver nanoparticles (AgNPs) size in the bionanocomposite matrix. The primary purpose of this study is used a feed-forward ANN model to create a connection between the output as the size of Ag–NPs, with four inputs variables, including AgNO3 concentration, the weight percentage of starch, Bentonite amount and Gallic acid concentration. Method: Silver nanoparticles were synthesised via biogenic green reduction method. The fast Levenberg– Marquardt (LM) backpropagation algorithm applied for the training of ANN model in this research. The optimised ANN is a multilayer perceptron (MLP) which is a kind of feed forward (4- 10-1) network has an input layer with 4 nodes, hidden layers with 10 neurones, and an output layer with 1 node found a fitness function. Results: The output results of developed computational ANN model were compared to its predictive values of the size of silver nanoparticles regarding two statistical parameters, the coefficient of determination (R2) and mean square error (MSE) of data set. It observed that ANN predicted values are close to the actual values and well fitted to the data. The mean square error(MSE) is 0.03, and a regression is about 1. Conclusion: AgNO3 concentration has the most likely factor affecting the size of silver nanoparticles (Ag–NPs) and this makes possible to develop a green reduction method for the preparation of silver nanoparticles. This study confirms that employing ANN method with LM feed forward (4-10-1) network is a useful tool with cost-effective for predicting the results of analysis and modelling of the chemical reactions.

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