TR-Dizin

Permanent URI for this collectionhttps://ada.atilim.edu.tr/handle/123456789/21

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    ANN-assisted forecasting of adsorption efficiency to remove heavy metals
    (Tubitak Scientific & Technological Research Council Turkey, 2019) Buaısha, Magdi; Balku, Şaziye; Yaman, Şeniz Özalp; Energy Systems Engineering; Chemical Engineering
    In wastewater treatment, scientific and practical models utilizing numerical computational techniques suchas artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems.In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which is determined by mean square error (MSE) and coefficient of determination (R2). According to the results, the model can forecast adsorption efficiency percent (AE%) with a tangent sigmoid transfer function (tansig) in the hidden layer with 10 neurons and a linear transferfunction (purelin) in the output layer. Furthermore, the Levenberg–Marquardt algorithm is seen to be most ideal for training the algorithm for the case studies, with the lowest MSE and high R2 . In addition, the experimental results and the results predicted by the model with the ANN were found to be highly compatible with each other.
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    Gold-assembled silica-coated cobalt nanoparticles as efficient magnetic separation units and surface-enhanced Raman scattering substrate Lütfiye Sezen YILDIRIM1,, Murat KAYA2,∗,, Mürvet VOLKAN
    (Tubitak Scientific & Technological Research Council Turkey, 2019) Yıldırım, Lütfiye Sezen; Kaya, Murat; Volkan, Mürvet; Chemical Engineering
    Magnetic and optical bifunctional nanoparticles that combine easy separation, preconcentration, and efficientSERS capabilities have been fabricated with high sensitivity and reproducibility through a low-cost method. Thesegold nanoparticles attached on magnetic silica-coated cobalt nanospheres (Co@SiO2 /AuNPs) display the advantageof strong resonance absorption due to gaps at nanoscale between neighboring metal nanoparticles bringing large fieldenhancements, known as “hot spots”. The prepared particles can be controlled by using an external magnetic field,which makes them very promising candidates in biological applications and Raman spectroscopic analysis of dissolvedorganic species. The magnetic property of the prepared particles lowers the detection limits through preconcentrationwith solid-phase extraction in SERS analysis. The performance of the prepared nanostructures was evaluated as a SERSsubstrate using brilliant cresyl blue (BCB) and rhodamine 6G (R6G) as model compounds. The solid-phase affinityextraction of 4-mercapto benzoic acid (4-MBA) using bifunctional Co@SiO2 /AuNPs nanoparticles followed by magneticseparation and the measurement of the SERS signal on the same magnetic particles without elution were investigated.Approximately 50-fold increase in SERS intensity was achieved through solid-phase extraction of 8.3 × 10 −6 M 4-MBAin 10 min.