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- 저자 : Feizbakhsh, Alireza (검색결과 2 건)
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High capacity polymer for nickel determination in environmental samples
Panahi, Homayon Ahmad,Feizbakhsh, Alireza,Dadjoo, Fatemeh,Moniri, Elham Techno-Press 2013 Advances in environmental research Vol.2 No.4
High AA new high capacity sorbent for preconcentration and determination of nickel in environmental samples was synthesized. The sorbent was synthesized by copolymerization of allyl glaycidyl ether / imminodiacetic acid with N,N-dimethylacrylamide as functional monomers in the presence of N,N-bismethylenacryl amid as cross linker and characterized by Fourier transform infra red spectroscopy, elemental analysis, thermogravimetric analysis and scanning electron microscopy. A recovery of 93.6% was obtained for the metal ion with 0.1 M, sulfuric acid as the eluting agent. The sorption capacity of the functionalized sorbent was 55.9 $mgg^{-1}$. The equilibrium sorption data of Ni(II) on polymeric sorbent were analyzed using Langmuir, Freundlich, Temkin and Redlich.Peterson models. Based on equilibrium adsorption data the Langmuir, Freundlich and Temkin constants were determined 0.87 (L mg-1), 25.87 ($mgg^{-1}$) $(Lmg^{-1})^{1/n}$ and 171.4 ($Jmol^{-1}$) respectively at pH 4.5 and $20^{\circ}C$.
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Mohsen Khatibinia,Abdosattar Feizbakhsh,Ehsan Mohseni,Malek Mohammad Ranjbar 사단법인 한국계산역학회 2016 Computers and Concrete, An International Journal Vol.18 No.6
The main aim of this study is to predict the compressive and flexural strengths of self–compacting mortar (SCM) containing nano–SiO2, nano–Fe2O3 and nano–CuO using wavelet–based weighted least squares–support vector machines (WLS–SVM) approach which is called WWLS–SVM. The WWLS–SVM regression model is a relatively new metamodel has been successfully introduced as an excellent machine learning algorithm to engineering problems and has yielded encouraging results. In order to achieve the aim of this study, first, the WLS–SVM and WWLS–SVM models are developed based on a database. In the database, nine variables which consist of cement, sand, NS, NF, NC, superplasticizer dosage, slump flow diameter and V–funnel flow time are considered as the input parameters of the models. The compressive and flexural strengths of SCM are also chosen as the output parameters of the models. Finally, a statistical analysis is performed to demonstrate the generality performance of the models for predicting the compressive and flexural strengths. The numerical results show that both of these metamodels have good performance in the desirable accuracy and applicability. Furthermore, by adopting these predicting metamodels, the considerable cost and time–consuming laboratory tests can be eliminated.
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