Modeling adsorption of sodium dodecyl benzene sulfonate (SDBS) onto polyaniline (PANI) by using multi linear regression and artificial neural networks

Özdemir, Utkan; Özbay, Bilge; Veli, Sevil; Zor, Sibel

doi:10.1016/j.cej.2011.10.046

Cited by 116 publications

(37 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-linear regression analysis is commonly used in environmental studies in order to model the impact of numerous independent factors on the dependent variable being examined (Özdemir et al, 2011;Chen et al, 2013).…”

Section: Multi-linear Regressionmentioning

confidence: 99%

Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression – A quantitative structure property relationship approach

et al. 2015

View full text Add to dashboard Cite

Section: Multi-linear Regressionmentioning

confidence: 99%

Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression – A quantitative structure property relationship approach

et al. 2015

View full text Add to dashboard Cite

“…Particularly, published studies on sodium dodecylbenzenesulphonate (SDBS) are still limited despite the significant importance of this surfactant in several industrial applications. Most of the limited published studies on SDBS have focused on the adsorption of this surfactant at the air-water [2] and polymer-water [3,4] interfaces in the absence of co-and counter-ions. Since industrial surfactant systems usually contain electrolytes at controlled pH [5], the interfacial behaviour of SDBS in the presence of co-and counter-ions is of great industrial relevance.…”

Section: Introductionmentioning

confidence: 99%

Adsorption of an anionic surfactant at air-liquid and different solid-liquid interfaces from solutions containing high counter-ion concentration

2015

View full text Add to dashboard Cite

The self-assembly (adsorption) of the anionic surfactant, sodium dodecylbenzenesulphonate (SDBS), at airliquid and different (octadecanethiol, β-mercaptoethanol, α-lipoic acid) solid-liquid interfaces from aqueous solutions containing high concentrations of counter-ion has been investigated. SDBS adsorption at the solid-liquid interfaces was obtained using surface plasmon resonance (SPR) while its adsorption at the air-liquid interface was extracted from surface tension measurements. The results have demonstrated that SDBS packing at the air-liquid interface is similar to its packing at the hydrophobic octadecanethiol-liquid interface. Additionally, SDBS packing at the three solid-liquid interfaces increases with increasing surface hydrophobicity, irrespective whether the surface is neutral or negatively charged. Nonetheless, SDBS adsorption is always within monolayer coverage (no evidence of bilayer or admicelle formation). The results have also revealed that SDBS affinity for the solid-liquid interfaces increases with increasing surface hydrophobicity. Furthermore, SDBS affinity for the air-liquid interface is more than 10-fold its affinity for the solid-liquid interfaces.

show abstract

“…27 The structure of an ANN model is determined by the number of its layers and the number of nodes in each layer and the nature of the transfer function. 28,29 Selecting the optimum architecture of the network is one of the challenging steps in ANN modeling. The term "architecture" refers to the number of layers in the network and the number of neurons in each layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Maleki¹,

Zabihollah²

2016

Mater. Tehnol.

View full text Add to dashboard Cite

Titanium matrix composites (TMCs) have wide application prospects in the field of aerospace, automobile and other industries because of their good properties, such as high specific strength, good ductility, and excellent fatigue properties. However, in order to improve their fatigue strength and life, crack initiation and growth at the surface layers must be suppressed using surface treatments. Shot peening (SP) is an effective surface mechanical treatment method widely used in industry which can improve the mechanical properties of a surface. However, artificial neural networks (ANNs) have been used as an efficient approach to predict and optimize the science and engineering problems. In the present study the effects of SP on TMC were modeled by means of ANN and the capability of the ANN in predicting the output parameters is investigated. A back-propagation (BP) error algorithm is developed for the network training. Data of experimental tests on the (TiB + TiC)/Ti-6Al-4V composite are employed in order to train the network. The volume fractions of the reinforcements (TiB + TiC) were 5 % and 8 %. ANN testing is accomplished using different experimental data thaat were not used during the network training. The distance from the surface (depth) and SP intensity are regarded as input parameters and residual stress and hardness of the Ti-6Al-4V before and after the SP and adding reinforcements are gathered as the output parameters of the network. A comparison was made between experimental and predicted data. The predicted results were in good agreement with experimental ones, which indicates that developed neural network can be used for modeling the SP process on TMCs. Keywords: titanium matrix composites, surface treatment, shot peening, artificial neural networks, residual stress, hardness Kompoziti na osnovi titana (TMCs) imajo {iroko mo`nost uporabe na podro~ju letalstva, avtomobilske in druge industrije zaradi njihovih dobrih lastnosti, kot so: velika specifi~na trdnost, dobra duktilnost in odli~na odpornost na utrujanje. Vseeno pa je za pove~anje odpornosti na utrujanje in`ivljenjsko dobo, potrebna povr{inska obdelava, da se zavre nastanek razpok in njihova rast na povr{ini. Hladno povr{insko kovanje (SP) je u~inkovita mehanska metoda, ki se v industriji pogosto uporablja za izbolj{anje mehanskih lastnosti povr{ine. Umetne nevronske mre`e (ANNs) se uporabljajo kot u~inkovit pribli`ek za napovedovanje in optimiranje znanstvenih osnov in in`eniringa tega problema. V {tudiji so bili modelirani vplivi SP na TMC s pomo~jo ANN in preiskovana je bila zmo`nost napovedovanja izhodnih parametrov z ANN. Za usposabljanje mre`e je bil razvit algoritem vzvratnega {irjenja napak (BP). Podatki iz eksperimentalnih preizkusov na (TiB + TiC)/Ti-6Al-4V kompozitu so uporabljeni za usposabljanje mre`e. Volumska dele`a delcev (TiB + TiC) za oja~anje sta bila 5 % in 8 %. ANN preizku{anje je bilo izvedeno z uporabo razli~nih eksperimentalnih podatkov, ki niso bili uporabljeni pri usposabljanju mre`e. Razdalja od povr{ine (globina) in...

show abstract

Modeling adsorption of sodium dodecyl benzene sulfonate (SDBS) onto polyaniline (PANI) by using multi linear regression and artificial neural networks

Cited by 116 publications

References 42 publications

Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression – A quantitative structure property relationship approach

Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression – A quantitative structure property relationship approach

Adsorption of an anionic surfactant at air-liquid and different solid-liquid interfaces from solutions containing high counter-ion concentration

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Contact Info

Product

Resources

About