Yalcin Urfa | Vahit Corumlu | Ahmet Altindal

Makale | 2021 | ELSEVIER SCIENCE SA

This study reports the influence of gamma ray irradiation of various doses in the range of 1?150 kGy on methanol sensing performance and adsorption kinetics of ZnO nanoparticles based sensors. ZnO nanoparticles were synthesized via sol-gel method and characterized with X-ray diffraction (XRD), transmission electron microscope (SEM), and Fourier transform infrared spectroscopy (FTIR) techniques. The results revealed that the methanol sensing performance of ZnO nanoparticles based sensor including sensitivity, response and recovery times improved by the gamma ray irradiation. Additionally, Elovich equation, Ritchie?s equation and pseu . . .do first order model were selected to follow the methanol adsorption process. The preliminary result of the methanol adsorption kinetic studies revealed that the adsorption kinetics strongly depends on the gamma irradiation dose. Among other kinetic models investigated, the pseudo first-order equation was the best to describe the adsorption kinetics of methanol on ZnO nanoparticles up to 50 kG dose of gamma ray, as evidenced by the highest correlation coefficients. On the other hand, for higher doses than of 50 kGy of gamma irradiation, our analysis showed that Elovich equation generates a straight line that best fit to methanol adsorption data on ZnO nanoparticles Daha fazlası Daha az

Metin Zontul | Esra Kavalci Yilmaz | Oguz Kaynar | Ramazan Katirci

Makale | 2021 | ELSEVIER SCIENCE SA

In this study, chrome coatings were carried out using a Cr-III electroplating bath. The coated parts were classified depending on their appearance. A new approach was developed to classify the coated parts automatically using artificial intelligence methods. Mask RCNN and machine learning (ML) methods such as Multilayer Perceptron (MLP), Support Vector Classifier (SVC), Gaussian Process (GP), K-nearest Neighbors (KNN), XGBoost, and Random Forest Classifier (RFC) were used together. Mask RCNN was used to clean the coated parts from the redundant data. The extracted data were flattened and converted to the row vectors for use as input . . . in ML methods. ML algorithms were used to classify the coated parts as Pass and Fail. The classification accuracy was checked with the leave one out (loo) cross-validation method. RFC method gave the highest accuracy, 0.83, and F1 score, 0.88. The accuracy of Mask RCNN was checked using a dataset of separated validation images. It was observed that extracting the unnecessary data from the images increased the accuracy exceedingly. Moreover, the method exhibits a high potential to keep the parameters of the electroplating process under control Daha fazlası Daha az

Erem Ahmetali | Hande Pekbelgin Karaoglu | Yalcin Urfa | Ahmet Altindal | Makbule Burkut Kocak

Makale | 2020 | ELSEVIER SCIENCE SA

In this study, the three novel fluoro substituted asymmetric zinc phthalocyanines substituted with 3-hydroxy-3-methyl-1-butynyl (3), ethynyl (4), and 4-nitrophenylethynyl (5) groups were synthesized and characterized by Fourier-transform infrared spectrophotometer, ultraviolet-visible, proton nuclear magnetic resonance, and matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy. The thickness dependence of relative humidity sensing performance of these compounds using quartz crystal microbalance transducers in a broad operating range of humidity was investigated. Results from this preliminary analysis indicated . . . that responses and the response and recovery times of the 3-5 compound-based relative humidity sensors strongly depended on the thickness of the sensory layer. Experimental studies indicated that the optimized thickness as 450 nm produces a good sensitivity, and the best response and recovery times. The relative humidity sensing results demonstrated that the quartz crystal microbalance sensor coated with peripherally substituted unsymmetrical zinc(II) phthalocyanines bearing nitrophenyl group was the most useful coating for the relative humidity sensing Daha fazlası Daha az