An intelligent optical fibre Al(lll) sensor based on advanced materials-sol-gel & polyaniline-porous nanocomposite / Faiz Bukhari Mohd Suah, Abdul Mutalib Md Jani and Mohd Nasir Taib
An optical fibre sensor for determination of Al(lll) based on the use of eriochrome cyanine R (ECR) immobilized on sol-gel and polyaniline-porous nanocomposite and reflectance spectroscopy has been developed. A kinetic approach was used to quantify sensor response to Al(lll) concentration in which t...
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| Main Authors: | , , |
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| Format: | Research Reports |
| Language: | English |
| Published: |
2006
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| Subjects: | |
| Online Access: | http://ir.uitm.edu.my/id/eprint/48800/1/48800.pdf http://ir.uitm.edu.my/id/eprint/48800/ |
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| Summary: | An optical fibre sensor for determination of Al(lll) based on the use of eriochrome cyanine R (ECR) immobilized on sol-gel and polyaniline-porous nanocomposite and reflectance spectroscopy has been developed. A kinetic approach was used to quantify sensor response to Al(lll) concentration in which the reflectance signal is measured at a fixed time interval of 3 minutes. Reproducible measurement of Al(lll) was possible using the same probe (RSD = 1.8%). Linear response was obtained for Al(lll) concentration 1.3 x 10"5 - 4.0 x 10"4 mg/L with limit of detection of 1.0 x 10"5 mg/L of the metal ion. The sensor was also used for the determination of Al(lll) in aqueous samples and the results obtained were comparable to those obtained by graphite furnace atomic absorption spectrophotometry. Subsequently, a methodology based on the coupling of experimental design and artificial neural networks (ANN) is proposed in the optimization of a new flow injection system for the spectrophotometric determination of Al(lll). An orthogonal design is utilized to design the experimental protocol, in which three variables are varied simultaneously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the experimental conditions is carried out in the neural network with 3:7:1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. The method has been applied to the determination of Al(lll) in water samples and provided satisfactory results. |
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