Prediction of nanostructure of SnO2 properties using artificial neural networks / Khadijah Mohd Suhami ... [et al.]

Mohd Suhami, Khadijah and Inderan, Vicinisvarri and Senin, Syahrul Fithry and Lee, Hooi Ling (2021) Prediction of nanostructure of SnO2 properties using artificial neural networks / Khadijah Mohd Suhami ... [et al.]. In: International Exhibition & Symposium on Productivity, Innovation, Knowledge, Education & Design (i-SPiKe 2021). (Submitted)

Abstract

Tin(IV) oxide, SnO2 nanostructures such as nanorods, nanoflowers, nanosheets, nanocubes have been receiving significant interest in various fields due to their inherent properties. The types of shape and size of nanorods vary based on the applications. A fine tuning of the parameters ( e.g concentration, pH, temperature, template, type of solvent etc.) during the synthesis process can alter the morphology of the SnO2. However, producing nanostructures with the desired size and shape is extremely complex and still remains a challenge. Hence, in this study a mathematics modelling called Artificial Neural Network (ANN) for the prediction of the SnO2 morphology was developed. This study was carried out using the real time data collected via experimental work and training the data using a neural network toolbox in MATLAB Version (R2016a) software. An ANN modelling was constructed with the input parameters of reaction time and concentration of precursors and three different output parameters namely, crystalline size, band gap energy and size of particles. This modelling was developed based on trial and error at different network architecture, activation function and training algorithm. The data set was trained using hyperbolic tangent sigmoid (tansig) activation function and Levenberg-Marquardt training algorithm. The performance of modelling was evaluated based on the mean square error (MSE)
and coefficient of determination (R2). The finding shows, there is no overfitting while constructing the neural network and it is able to track the data. The result shows that the MSE performance plot and R2 are in the range of 0.1-1.0. Therefore, it is suggested that the ANN modellings constructed in this study are able to produce a decent prediction. These values indicate that prediction of nanostructure SnO2 properties using artificial neural network (ANN) is a great success.

Metadata

Item Type: Conference or Workshop Item (Paper)
Creators:
Creators
Email / ID Num.
Mohd Suhami, Khadijah
khadijahmsuhami@gmail.com
Inderan, Vicinisvarri
vicinisvarri@uitm.edu.my
Senin, Syahrul Fithry
syahrul573@uitm.edu.my
Lee, Hooi Ling
hllee@usm.my
Subjects: Q Science > QD Chemistry > Organic chemistry
Q Science > QD Chemistry > Physical and theoretical chemistry > Conditions and laws of chemical reactions
Divisions: Universiti Teknologi MARA, Kedah > Sg Petani Campus
Event Title: International Exhibition & Symposium on Productivity, Innovation, Knowledge, Education & Design (i-SPiKe 2021)
Page Range: pp. 565-569
Keywords: SnO2, nanostructures, ANN, process modelling, hyperbolic tangent sigmoid, Levenberg-Marquardt training
Date: 2021
URI: https://ir.uitm.edu.my/id/eprint/56910
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