Sürdürülebilir Yeşil Kampüs Koleksiyonu / Sustainable Green Campus Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7755
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Article Citation - WoS: 5Citation - Scopus: 6Development of an Optical Tyrosinase Biosensor (tca) for Detection of “parathion-Methyl”(Emerald Group Publishing Ltd., 2019) Polatoğlu, İlker; Çakıcıoğlu Özkan, FehimePurpose: This paper aims to present a novel and cost-effective optical biosensor design by simple preparation method for detection of “parathion-methyl,” which is a model pesticide pose to public health and the environment. Design/methodology/approach: The optical enzyme biosensor (TCA) for detection of pesticide “parathion-methyl” was developed on the basis of immobilization of tyrosinase enzyme on chitosan film by adsorption technique. The analytic performance of TCA was investigated by measuring its activity with Ultraviolet (UV) visible spectrophotometer. Findings: Uniform porous network structure and protonated groups of chitosan film provided a microenvironment for tyrosinase immobilization evident from Fourier transform infrared (FTIR) spectroscopy and Atomic Force Microscopy analysis. TCA has a wide linear detection range (0-1.03 µM) with high correlation coefficient and it can detect the parathion-methyl concentration as low as 159 nM by noncompetitive inhibition kinetics. Using the TCA sensor both for ten times and at least 45 days without a significant loss in its activity are the indicators of its good operational and storage stability. Moreover, TCA can be applicable to tap water, providing a promising tool for pesticides detection. Originality/value: This is the first time to use the in situ analytical technique that can improve the performance of optical enzyme sensor provided to control the pesticide residue better with respect to traditional techniques. The effect of organic solvents on the performance of optical enzyme biosensor was investigated. Inhibition kinetic of the solvents rarely encountered in literature was also studied besides the pH and temperature tolerance of the optical biosensor.Article Citation - WoS: 19Citation - Scopus: 29Modeling of an Activated Sludge Process for Effluent Prediction—a Comparative Study Using Anfis and Glm Regression(Springer Verlag, 2018) Araromi, Dauda Olurotimi; Majekodunmi, Olukayode Titus; Adeniran, Jamiu Adetayo; Salawudeen, Taofeeq OlalekanIn this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive models of the effluent chemical and 5-day biochemical oxygen demands were developed from measured past inputs and outputs. From a set of candidates, least absolute shrinkage and selection operator (LASSO), and a fuzzy brute-force search were utilized in selecting the best combination of regressors for the GLMs and ANFIS models respectively. Root mean square error (RMSE) and Pearson’s correlation coefficient (R-value) served as metrics in assessing the predicting performance of the models. Contrasted with the GLM predictions, the obtained modeling results show that the ANFIS models provide better predictions of the studied effluent variables. The results of the empirical search for the dominant regressors indicate the models have an enormous potential in the estimation of the time lag before a desired effluent quality can be realized, and preempting process disturbances. Hence, the models can be used in developing a software tool that will facilitate the effective management of the treatment operation.Article Effects of Reactor Pressure and Inlet Temperature on N-butane/Dimethyl Ether Oxidation and the Formation Pathways of the Aromatic Species(John Wiley and Sons Inc., 2016) Bekat, Tuğçe; İnal, FikretOxidation of n-butane/dimethyl ether (DME)/O2/Ar system was studied by chemical kinetic modeling in a tubular reactor operated adiabatically and at constant pressure. Effects of the reactor pressure on the formation of various major, minor, and trace oxidation products were investigated for two different pressures (1 and 5 atm) and at six different inlet temperature values (700, 800, 900, 1100, 1300, and 1500 K). The analysis was carried out for two different concentrations of dimethyl ether in the inlet fuel mixture (20 and 50 mol %). Higher pressure (5 atm) resulted in higher mole fractions of methane, vinylacetylene, and cyclopentadiene; and lower mole fractions of formaldehyde, acetylene, acetaldehyde, ethane, propargyl, and propane. The mole fractions of CO and CO2 were not affected considerably by the pressure change. The main formation routes of benzene were developed at two different inlet temperature values (1100 and 1300 K), and the main precursors participating in these routes were found to be propargyl, propene, and diacetylene. A skeletal mechanism was developed for the oxidation of n-butane/DME mixture from the detailed mechanism by reduction of the elementary reactions by 79%, and it was tested for accuracy by comparison with the data from the literature.Article Citation - WoS: 24Citation - Scopus: 25Atmospheric Concentrations and Phase Partitioning of Polycyclic Aromatic Hydrocarbons in Izmir, Turkey(John Wiley and Sons Inc., 2011) Demircioğlu, Eylem; Sofuoğlu, Aysun; Odabaşı, MustafaAmbient air polycyclic aromatic hydrocarbon (PAH) samples were collected at a suburban (n=63) and at an urban site (n=14) in Izmir, Turkey. Average gas-phase total PAH (∑ 14PAH) concentrations were 23.5ngm -3 for suburban and 109.7ngm -3 for urban sites while average particle-phase total PAH concentrations were 12.3 and 34.5ngm -3 for suburban and urban sites, respectively. Higher ambient PAH concentrations were measured in the gas-phase and ∑ 14PAH concentrations were dominated by lower molecular weight PAHs. Multiple linear regression analysis indicated that the meteorological parameters were effective on ambient PAH concentrations. Emission sources of particle-phase PAHs were investigated using a diagnostic plot of fluorene (FLN)/(fluorine+pyrene; PY) versus indeno[1,2,3-cd]PY/(indeno[1,2,3-cd]PY+benzo[g,h,i]perylene) and several diagnostic ratios. These approaches have indicated that traffic emissions (petroleum combustion) were the dominant PAH sources at both sites for summer and winter seasons. Experimental gas-particle partition coefficients (K P) were compared to the predictions of octanol-air (K OA) and soot-air (K SA) partition coefficient models. The correlations between experimental and modeled K P values were significant (r 2=0.79 and 0.94 for suburban and urban sites, respectively, p<0.01). Octanol-based absorptive partitioning model predicted lower partition coefficients especially for relatively volatile PAHs. However, overall there was a relatively good agreement between the measured K P and soot-based model predictions. Ambient air polycyclic aromatic hydrocarbon (PAH) samples were collected at a suburban and at an urban site in Izmir, Turkey. The multiple linear regression analysis indicated that the meteorological parameters were effective on the measured ambient PAH concentrations. The results indicated that traffic emissions were the dominant PAH sources at both sites for summer and winter seasons.Article Citation - WoS: 9Citation - Scopus: 11Oxidation Behavior of C-And Au-Ion Biodegradable Polymers(Institute of Electrical and Electronics Engineers Inc., 2012) Sokullu Urkaç, Emel; Öztarhan, Ahmet; Tıhmınlıoğlu, Funda; Nikolaev, Alexey; Brown, IanBiodegradable polymers are widely used in biomedical and tissue engineering applications due to their biocompatibility and hydrolysis properties in the body. However, their low surface energy and lack of functional groups to interact with the cellular environment have limited their applications for in vivo studies. Ion beam modification is a convenient method for improving the surface properties of polymeric materials for functional biomedical applications. In the work described here, vacuum arc metal ion implantation was used to modify the composition of the near-surface region of three kinds of polymerspoly(L-lactide), poly(D, L-lactide-co-glycolide), and poly(L-lactide/caprolactone)chosen as representative of biodegradable polymers. X-ray photoelectron spectroscopy analysis was used to characterize the chemical effects of these polymers after implantation with C and with Au, and the results were compared with untreated control samples. We find that oxidation behavior is brought about for certain implantation fluences, resulting in improved surface hydrophilicity. © 2011 IEEE.Article Citation - WoS: 28Citation - Scopus: 18Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey(John Wiley and Sons Inc., 2010) İnal, FikretTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross-validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 μg/m3, 11.15μg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. Tropospheric ozone has adverse effects on human health and environment. Here, the next-day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron type artificial neural networks (MLP-ANNs). The MLP-ANNs were compared to multiple linear and multiple non-linear regression models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.Article Citation - WoS: 39Citation - Scopus: 48The Indoor Environmental Index and Its Relationship With Symptoms of Office Building Occupants(Taylor and Francis Ltd., 2004) Moschandreas, Demetrios J.; Sofuoğlu, Sait CemilAn index for indoor environmental quality, the Indoor Environmental Index (IEI), was developed. This study aggregates the Indoor Air Pollution Index, an index found in the literature, and a new index: the Indoor Discomfort Index. The average of these two indices is the IEI, which is calculated using concentrations of eight pollutants and two comfort variables measured in 100 office buildings in the United States. The database used was developed for the U.S. Environmental Protection Agency Building Assessment Survey Evaluation study. A symptom index also is developed to denote persistent occupant symptoms. The IEI and the symptom index are used to investigate the relationship between indoor environmental quality and symptoms. Two simple linear regression models were formulated; these models explain 67 and 79% of the variation in the average symptom index, with the variation of the average IEI depending on the method of averaging used in the construction of the models. In addition, a conceptual explanation is provided for the empirical or regression models formulated. The IEI and the associated models relating indoor environmental quality with the office occupant symptom index may be used as management tools, as illustrated with an example.