PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/5

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Citation - WoS: 2
Citation - Scopus: 2
Adrenal Lesion Classification With Abdomen Caps and the Effect of Roi Size
(Springer, 2023) Solak, Ahmet; Ceylan, Rahime; Bozkurt, Mustafa Alper; Cebeci, Hakan; Koplay, Mustafa
Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection and classification of lesions in medical imaging heavily rely on several key factors, including the specialist's level of experience, work intensity, and fatigue of the clinician. These factors are critical determinants of the accuracy and effectiveness of the diagnostic process, which in turn has a direct impact on patient health outcomes. With the spread of artificial intelligence, the use of computer-aided diagnosis (CAD) systems in disease diagnosis has also increased. In this study, adrenal lesion classification was performed using deep learning on MR images. The data set used was obtained from the Department of Radiology, Faculty of Medicine, Selcuk University, and all adrenal lesions were identified and reviewed in consensus by two radiologists experienced with abdominal MR. Studies were carried out on two different data sets created by T1- and T2-weighted MR images. The data set consisted of 112 benign and 10 malignant lesions for each mode. Experiments were performed with regions of interest (ROIs) of different sizes to increase the working performance. Thus, the effect of the selected ROI size on the classification performance was assessed. In addition, instead of the convolutional neural network (CNN) models used in deep learning, a unique classification model structure called Abdomen Caps was proposed. When the data sets used in classification studies are manually separated for training, validation, and testing, different results are obtained with different data sets for each stage. To eliminate this imbalance, tenfold cross-validation was used in this study. The best results obtained were 0.982, 0.999, 0.969, 0.983, 0.998, and 0.964 for accuracy, precision, recall, F1-score, area under the curve (AUC) score, and kappa score, respectively.
Citation - WoS: 1
Citation - Scopus: 1
Analysis of Fluid Forces Impacting on the Impeller of a Mixed Flow Blood Pump With Computational Fluid Dynamics
(Sage Publications Ltd, 2024) Diallo, Abdoulaye Billo; Cinar, Hasan; Yapici, Rafet
This study presents four different impeller designs to compare hydrodynamic forces. Numerical simulation studies are performed via computational fluid dynamics to specify and investigate the hydraulic forces impacting the impeller of the mixed-flow blood pump with a volute. The design point of this pump is that the flow rate is 5 L/min, the rotational speed is 8000 rpm, and the manometric head is 100 mmHg. The designed impellers are placed in the same volute and simulation studies are performed with the same mesh size (17.3 million cells) of the pumps. The simulation studies have been conducted in setting 1050 kg/m3 blood density, 35 cP fluid viscosity, and SST-k omega turbulence model. Additionally, this study examines the changes in hydraulic forces and hydraulic efficiency with fluid viscosity. As a result of experimental simulation studies, the highest hydraulic efficiencies of 40.87% and 39.5% are achieved in the case of the shaftless-grooveless and shafted-grooveless impeller, respectively. The maximum axial forces are obtained from the pump with the shaftless-grooveless impeller. Whereas radial forces, maximum values are calculated in the pump with the shaftless-outer groove impeller for all flow rates. Finally, the wall shear stresses, which are important for blood pump designs, are evaluated and the maximum value of 227 Pa is observed in the pump impeller with a shaftless-grooved.
Citation - WoS: 26
Citation - Scopus: 26
Analysis of Land Use/Land Cover Changes and Prediction of Future Changes With Land Change Modeler: Case of Belek, Turkey
(Springer, 2023) Akdeniz, Halil Burak; Serdaroğlu Sağ, Neslihan; İnam, Şaban
In the areas declared to be a tourism center by state planning, a rapid tourism-related development occurs depending on the investments in tourism, which causes a dramatic land use/land cover (LULC) change. Determining, monitoring, and modeling of LULC changes are required in order to ensure the conservation-use balance and sustainability within such vulnerable areas that are under development pressure. This study consists of four steps. In the first step, the Landsat images dated 1985, 2000, 2010, and 2021 were classified using the maximum likelihood method and the LULC of Belek Tourism Center located in Turkey were determined. The second step included the identification of areal and spatial changes between the LULC classes for the four periods. In the third step, the LULC changes in Belek Tourism Center for 2040 were modeled using the land change modeler. Last step evaluated the relationship between the modeled spatial development pattern and the current planning decisions. According to the results obtained during 36 years, the rates of built-up, forest, and water body areas have increased by 11.91%, 13.67%, and 0.82%, respectively, whereas the rates of barren land and agricultural areas have reduced by 22.25% and 4.15%, respectively. The LULC map modeled for 2040 predicts the built-up areas to expand by 8.25% and the agricultural areas to shrink by 5.42% by comparison with 2021. This study will contribute as a key measure for planners, policy-, and decision-makers to make decisions related to sustainable land use in the areas declared to be a tourism center.
Assessment of Accumulation, Spatial Distribution and Sources of Potentially Toxic Elements (PTEs) in Sediments of a Saline Lake
(Taylor & Francis Inc, 2025) Huseyinca, Mehmet Yavuz; Kupeli, Suayip
Potentially Toxic Elements (PTEs) are hazardous for human and ecosystem health due to their non-biodegradable nature. In this study we investigated the concentrations of PTEs, including As, Co, Cr, Cu, Mn, Mo, Ni, Pb and V in sediments of Lake Tuz around the salt pans for possible contamination. Lake Tuz is a shallow saline lake where halite (table salt) production is carried out in the salt pans and has significant geo and eco-tourism potential due to its unique ecosystem and natural beauty. The extent of pollution level and ecological risk were evaluated by geochemical indices and guideline values. According to the Geoaccumulation Index (Igeo), Enrichment Factor (EF) and Contamination Factor (Cf) indices Cr, Mo, As and occasionally Ni accumulated in moderate to strong levels. Intensity maps of Pollution Load Index (PLI) and Modified Degree of Contamination (mCdeg) indicated pollution hotspots in the neck region and in the eastern shore of the lake respectively. The Potential Ecological Risk Index (PERI) values indicated low and moderate levels of ecological risk. Statistical analyses including Pearson Correlation Coefficient (PCC), Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) suggested that Co, Cr, Cu, Mn, Mo, Ni and V are of geogenic origin and As and Pb are of anthropogenic origin. Provenance analysis suggested that host rocks for geogenic PTEs were granodiorites and ophiolites situated in the catchment area of the lake. Anthropogenic PTEs were most likely related to agrochemicals used in surrounding farmlands.
Citation - WoS: 5
Citation - Scopus: 5
Combined Use of Bwm-Topsis Methods in the Selection of Thermal Power Plant Installation Site in the Karapinar/Turkiye Region, at Risk of Sinkhole Formation
(Springer Science and Business Media Deutschland GmbH, 2024) Gumussoy, A.D.; Onen, V.; Yalpir, S.
With the rapidly increasing world population and the need for industrialization, energy supply has become an important global problem. A significant part of the world's energy needs is provided by fossil fuels. About half of all global coal deposits are low-quality coals, including lignite. Karapınar/Konya, also the study area, has Turkiye's second richest lignite reserve. The region's lignite reserve can be used in thermal power plants for electricity generation in terms of its nature and the amount is an important opportunity to meet the energy demand for both region and country. The region contains many sinkholes, and the potential for the formation of new sinkholes makes the site selection for thermal power plants in the region an even more strategic decision. This study aims to propose the most suitable thermal power plant site for the region by using Multi-Criteria Decision Making methods and Geographic Information Systems in an integrated way. Within the scope of the study, a total of twelve sub-criteria were taken into consideration under the main criteria of Geological, Economic and Environmental. The Best–Worst Method was applied to determine the criteria weights, and by using the weights, a suitability map for the thermal power plant installation site was produced and candidate regions were determined. TOPSIS was applied to determine the most suitable location among the candidate regions. The Candidate Region in the easternmost part of Karapinar district was chosen as the most suitable site for the thermal power plant installation. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Citation - WoS: 1
Citation - Scopus: 2
Computational Fluid Dynamics Simulating of the Fda Benchmark Blood Pump With Different Coefficient Sets and Scaler Shear Stress Models Used in the Power-Law Hemolysis Model
(Springer Japan Kk, 2025) Önder, Ahmet; İncebay, Ömer; Yapıcı, Rafet
Hemolysis is the most important issue to consider in the design and optimization of blood-contacting devices. Although the use of Computational Fluid Dynamics (CFD) in hemolysis prediction studies provides convenience and has promising potential, it is an extremely challenging process. Hemolysis predictions with CFD depend on the mesh, implementation method, coefficient set, and scalar-shear-stress model. To this end, an attempt was made to find the combination that would provide the most accurate result in hemolysis prediction with the commonly cited power-law based hemolysis model. In the hemolysis predictions conducted using CFD on the Food and Drug Administration (FDA) benchmark blood pump, 3 different scalar-shear-stress models, and 5 different coefficient sets with the power-law based hemolysis model were used. Also, a mesh independence test based on hemolysis and pressure head was performed. The pressure head results of CFD simulations were compared with published pressure head of the FDA benchmark blood pump and a good agreement was observed. In addition, results of CFD-hemolysis predictions which are conducted with scalar-shear-stress model and coefficient set combinations were compared with experimental hemolysis data at three operating conditions such as 6-7 L/min flow rates at 3500 rpm rotational speeds and 6 L/min at 2500 rpm. One of the combinations of the scalar-shear-stress model and the coefficient set was found to be within the error limits of the experimental measurements, while all other combinations overestimated hemolysis.
Citation - WoS: 3
Citation - Scopus: 3
Diagnosis of Covid-19 From Blood Parameters Using Convolutional Neural Network
(Springer, 2023) Doğan, Gizemnur Erol; Uzbaş, Betül
Asymptomatically presenting COVID-19 complicates the detection of infected individuals. Additionally, the virus changes too many genomic variants, which increases the virus's ability to spread. Because there isn't a specific treatment for COVID-19 in a short time, the essential goal is to reduce the virulence of the disease. Blood parameters, which contain essential clinical information about infectious diseases and are easy to access, have an important place in COVID-19 detection. The convolutional neural network (CNN) architecture, which is popular in image processing, produces highly successful results for COVID-19 detection models. When the literature is examined, it is seen that COVID-19 studies with CNN are generally done using lung images. In this study, one-dimensional (1D) blood parameters data were converted into two-dimensional (2D) image data after preprocessing, and COVID-19 detection was made with CNN. The t-distributed stochastic neighbor embedding method was applied to transfer the feature vectors to the 2D plane. All data were framed with convex hull and minimum bounding rectangle algorithms to obtain image data. The image data obtained by pixel mapping was presented to the developed 3-line CNN architecture. This study proposes an effective and successful model by providing a combination of low-cost and rapidly-accessible blood parameters and CNN architecture making image data processing highly successful for COVID-19 detection. Ultimately, COVID-19 detection was made with a success rate of 94.85%. This study has brought a new perspective to COVID-19 detection studies by obtaining 2D image data from 1D COVID-19 blood parameters and using CNN.
Citation - WoS: 13
Citation - Scopus: 16
The Effects of Urban Growth on Natural Areas: the Three Metropolitan Areas in Turkiye
(Springer, 2023) Öncel, Hale; Levend, Sinan
Today, more than half of the world's population of 7.6 billion lives in cities, and by 2030, it is estimated that the population of urban residents will exceed 5 billion worldwide. Since growth in cities destroys agriculture, forests, and wetlands, an increasing carbon footprint brings many environmental problems, such as global climate change. Among the developing countries, Turkiye's largest cities have been experiencing a rapid urbanization process. The study aims to analyze the adverse effects of urban growth in Turkiye's largest metropolises on natural areas such as agriculture, forests, and wetlands. In this context, the Istanbul, Ankara, and Izmir metropolitan areas have been determined as case areas. The correlation between the changes in the land cover and the urban expansion processes of the three big cities from 1990 to 2018 has been systematically analyzed in the GIS environment using Corine land cover program data. The study indicates the devastating effect of urban growth on agricultural areas in all three case areas. In addition, the urbanization pressure in Istanbul continues to destroy northern forests.
Citation - WoS: 5
Citation - Scopus: 7
Estimation of the Risk of Work-Related Accidents for Underground Hard Coal Mine Workers by Logistic Regression
(TAYLOR & FRANCIS LTD, 2022) Bilim, Niyazi; Bilim, Atiye
Coal mining has the most risk in all of the mining sectors. Hence, in this sector, most work accidents encountered are intensive. The demographic characteristics of workers affect the occurrence of occupational accidents. This study aims to develop an equation that predicts workday loss by analyzing the relationship between workers' demographic characteristics and having an accident with workday loss. In this study, work-related accidents between 2014 and 2019 in underground hard coal mines in Turkey were analyzed using logistic regression analysis. An equation is derived that estimates the workday loss with the characteristics of workers in hard coal mines. With the equation derived in this study, employers can determine the potential for work accidents according to the demographic characteristics of the workers and serious work accidents will be prevented. Therefore, proactive solutions can be produced by applying the methods used in this study to different industries.
Citation - WoS: 10
Citation - Scopus: 14
Heuristic Optimization of Impeller Sidewall Gaps-Based on the Bees Algorithm for a Centrifugal Blood Pump by Cfd
(SAGE PUBLICATIONS LTD, 2021) Önder, Ahmet; İncebay, Ömer; Şen, Muhammed Arif; Yapıcı, Rafet; Kalyoncu, Mete
Optimization studies on blood pumps that require complex designs are gradually increasing in number. The essential design criteria of centrifugal blood pump are minimum shear stress with maximal efficiency. The geometry design of impeller sidewall gaps (blade tip clearance, axial gap, radial gap) is highly effective with regard to these two criteria. Therefore, unlike methods such as trial and error, the optimal dimensions of these gaps should be adjusted via a heuristic method, giving more effective results. In this study, the optimal gaps that can ensure these two design criteria with The Bees Algorithm (BA), which is a population-based heuristic method, are investigated. Firstly, a Computational Fluid Dynamics (CFD) analysis of sample pump models, which are selected according to the orthogonal array and pre-designed with different gaps, are performed. The dimensions of the gaps are optimized through this mathematical model. The simulation results for the improved pump model are nearly identical to those predicted by the BA. The improved pump model, as designed with the optimal gap dimensions so obtained, is able to meet the design criteria better than all existing sample pumps. Thanks to the optimal gap dimensions, it has been observed that compared to average values, it has provided a 42% reduction in aWSS and a 20% increase in efficiency. Moreover, original an approach to the design of impeller sidewall gaps was developed. The results show that computational costs have been significantly reduced by using the BA in blood pump geometry design.
Citation - WoS: 1
Citation - Scopus: 1
Histological Tissue Classification With a Novel Statistical Filter-Based Convolutional Neural Network
(Wiley, 2024) Ünlükal, Nejat; Ülker, Erkan; Solmaz, Merve; Uyar, Kübra; Tasdemir, Sakir
Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.
Citation - WoS: 3
Citation - Scopus: 3
Hyperspectral Imaging-Based Cutaneous Wound Classification Using Neighbourhood Extraction 3d Convolutional Neural Network
(Walter De Gruyter Gmbh, 2023) Cihan, Mucahit; Ceylan, Murat
Objectives: Hyperspectral imaging is an emerging imaging modality that beginning to gain attention for medical research and has an important potential in clinical applications. Nowadays, spectral imaging modalities such as multispectral and hyperspectral have proven their ability to provide important information that can help to better characterize the wound. Oxygenation changes in the wounded tissue differ from normal tissue. This causes the spectral characteristics to be different. In this study, it is classified cutaneous wounds with neighbourhood extraction 3-dimensional convolutional neural network method.Methods: The methodology of hyperspectral imaging performed to obtain the most useful information about the wounded and normal tissue is explained in detail. When the hyperspectral signatures of wounded and normal tissues are compared on the hyperspectral image, it is revealed that there is a relative difference between them. By taking advantage of these differences, cuboids that also consider neighbouring pixels are generated, and a uniquely designed 3-dimensional convolutional neural network model is trained with the cuboids to extract both spatial and spectral information.Results: The effectiveness of the proposed method was evaluated for different cuboid spatial dimensions and training/testing rates. The best result with 99.69% was achieved when the training/testing rate was 0.9/0.1 and the cuboid spatial dimension was 17. It is observed that the proposed method outperforms the 2-dimensional convolutional neural network method and achieves high accuracy even with much less training data. The obtained results using the neighbourhood extraction 3-dimensional convolutional neural network method show that the proposed method highly classifies the wounded area. In addition, the classification performance and the2computation time of the neighbourhood extraction 3-dimensional convolutional neural network methodology were analyzed and compared with existing 2-dimensional convolutional neural network.Conclusions: As a clinical diagnostic tool, hyperspectral imaging, with neighbourhood extraction 3-dimensional convolutional neural network, has yielded remarkable results for the classification of wounded and normal tissues. Skin color does not play any role in the success of the proposed method. Since only the reflectance values of the spectral signatures are different for various skin colors. For different ethnic groups, The spectral signatures of wounded tissue and the spectral signatures of normal tissue show similar spectral characteristics among themselves.
Citation - WoS: 8
Citation - Scopus: 9
An Insight on the Impact of Covid-19 on the Global and Turkish Mining Industry
(Ios Press, 2022) Kekeç, Bilgehan; Bilim, Niyazi; Ghiloufi, Dhikra
BACKGROUND: COVID-19 affected numerous industries and the mining industry has not been immune to the adverse impacts caused by the pandemic. OBJECTIVE: This study examines the importance of the mining industry and its benefits to the economy of the producing countries. The paper also gives an insight into the pre-COVID global and Turkish mining industries and investigates the impact of the pandemic on the global and Turkish mining sectors. Furthermore, the study suggests numerous measures that should be adopted in mines to limit the spread of COVID-19 and conduct mining operations safely and efficiently. METHODS: An extensive literature review was conducted and relevant papers on the importance and benefits of the mining industry, the Turkish and global mining industry, and the impact of COVID-19 on the Turkish and global mining industry were studied. RESULTS: The COVID-19 crisis has deeply affected metal and mineral production and the economic sectors that depend on the mining industry for supplies. The most significant impacts caused by the COVID-19 pandemic on the global mining industry consist of the drastic decline in demand and production and the decrease in the prices of several commodities. As with any complex global situation, the mining industries of some countries were affected more than others by the COVID-19 crisis. The Turkish mining industry was to some extent affected by the COVID-19 crisis, but it quickly recovered. CONCLUSIONS: An efficient planning of operations and adopting effective measures and precautions enable limiting the spread of COVID-19 in quarries and mines.
Citation - WoS: 6
Citation - Scopus: 7
Investigating the Impact of Urban Growth on Land Use Using Spatial Autocorrelation Methods in Konya/Türkiye
(Springer, 2024) Uyan, Mevlut; Ertunç, Ela
Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales.
Citation - WoS: 10
Citation - Scopus: 13
Investigation of Landslide Detection Using Radial Basis Functions: a Case Study of the Takent Landslide, Turkey
(SPRINGER, 2020) Zeybek, Mustafa; Şanlıoğlu, İsmail
This paper investigates landslide detection over flat and steep-slope areas with large forest cover using different radial basis function interpolation methods, which can affect the quality of a digital elevation model. Unmanned aerial vehicles have been widely used in landslide detection studies. The generation of image-based point clouds is achievable with various matching algorithms from computer vision systems. Point cloud-based analysis was performed by generating multi-temporal digital elevation models to detect landslide displacement. Interpolation methodology has a crucial task to fill the gaps in insufficient areas that result from filtered areas or sensors that do not generate spatial information. Radial basis function interpolations are the most commonly used technique for estimating the unknown values in survey areas. However, the quality of the radial basis function interpolation methods for landslide studies has not been thoroughly investigated in previous studies. In this study, radial basis function interpolation methods are investigated and compared with the global navigational satellite systems, which provide high accuracy for geodetic measurement systems. The main purpose of this study was to investigate the various radial basis function models to detect landslides using a point cloud-based digital elevation model and determine the quality of detection with global navigational satellite systems. As a result of this study, each of the radial basis function-generated digital elevation models was found to be statistically compatible with global navigational satellite systems, resulting in displacements from the ground truth data.
Isolation and Characterization of Chemical Constituents From Chaerophyllum Bulbosum Roots and Their Enzyme Inhibitory and Antioxidant Effects
(Walter De Gruyter Gmbh, 2022) Tel-Çayan, Gülşen; Deveci, Ebru; Molo, Zeynep; Duru, Mehmet Emin; Öztürk, Mehmet
Isolation and bioactive effects of the roots of Chaerophyllum bulbosum L. were firstly investigated herein. Enzyme (acetylcholinesterase, butyrylcholinesterase, urease, alpha-amylase, alpha-glucosidase, and tyrosinase) inhibitory effects of C. bulbosum root extracts were tested. Three known compounds, n-heptadecanyl eicosanoate (1), stigmasterol (2), and beta-sitosterol-3-O-beta-d-glucopyranoside (3) were isolated from C. bulbosum. Antioxidant and enzyme inhibitory effects of isolated compounds were investigated. The hexane extract (IC50: 349.58 +/- 0.06 mu g/mL) displayed a higher alpha-glucosidase inhibitory effect than the standard (IC50: 378.66 +/- 0.14 mu g/mL). The best inhibitory effect was found in compound 2 on AChE (46.40 +/- 0.31%), BChE (56.41 +/- 0.54%), and urease (92.47 +/- 0.11%); compound 1 on alpha-amylase (22.27 +/- 0.61%); and compound 3 on alpha-glucosidase (12.43 +/- 0.25%) and tyrosinase (19.00 +/- 0.16%). All isolated compounds showed moderate antioxidant effects in all assays. This study contributes to the therapeutic uses of Chaerophyllum roots and emphasizes the value of C. bulbosum species for the development of novel therapeutic agents.
Citation - WoS: 6
Citation - Scopus: 7
Measurement and Evaluation of Particulate Matter and Atmospheric Heavy Metal Pollution in Konya Province, Turkey
(SPRINGER, 2021) Kunt, Fatma; Ayturan, Zeynep Cansu; Yümün, Feray; Karagönen, İlknur; Semerci, Mümin; Akgün, Mehmet
Air pollution has negative effects on human health, visibility, materials, plants, and animal health. Particulate matters are one of the most important air pollutants that may create a risk for human health. Especially particulate matters, which are composed of heavy metals and cancer-causing chemicals such as PAH, dioxin, furan, can cause serious reactions in the respiratory tract. Heavy metals are so important because of their capability of accumulation in human tissues. Almost 0.01-3% of heavy metal content may be found in particulate matter. Coarse particulate matters (PM10) which have smaller diameters than 10 microns may enter from the respiratory system and reach the lungs. In this study, PM10 concentrations and heavy metal content (Lead, Nickel, Arsenic, Cadmium) of the samples were measured and evaluated concerning present regulations and limit values for different points in Konya Province, Turkey. The samples were taken at different seasons such as winter, summer, and spring for 16 days. According to the results of this study, in the winter season, PM10 concentration of the measurement point (Directorate Building) located at the settlement area was found the highest. In the summer season, PM10 concentration of the measurement point (Sille Junction) located at crossroads was found the highest. In spring season, maximum PM10 concentration was detected on the measurement point (KOS base station) located in the industrial area. Moreover, daily average nickel (Ni) concentration measured at KOS base station was found the highest and some other station located close to the industrial area and settlement areas were detected higher than average annual limit values in the winter period. Daily average lead (Pb) value was found at least 67% and maximum 98% higher at Sille and Besyol Junctions, but below the annual average limits. Daily average cadmium (Cd) value was mostly calculated in the Directorate Building winter measurement, but it did not exceed the limit value during the measurement periods. Daily average arsenic (As) values at Directorate Building and Karkent measurement points in the winter period were found higher than the annual average limit values. Only daily average nickel concentrations were detected higher than the annual average limits for the summer and spring period at the KOS base station measurement point.
Citation - WoS: 3
Citation - Scopus: 3
Methylene Blue Removal Using Modified Poly(glycidyl Methacrylate) as a Low-Cost Sorbent in Batch Mode: Kinetic and Equilibrium Studies
(Springer Science and Business Media Deutschland GmbH, 2024) Kara, G.; Temel, F.; Özaytekin, İ.
Industrial textile wastewater contains large amounts of cationic dye material. Therefore, a new adsorbent was synthesized as modified poly(glycidyl methacrylate) (mPGMA) with a fluorine group-containing compound 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP). mPGMA was characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectrometer (FTIR). The proposed adsorbent has been used to remove methylene blue (MB) from aqueous solutions by the adsorption process. In further experiments, the removal efficiency of adsorbent in both powder (˂600 μm) and granular form was compared from aqueous solutions by adsorption process. Furthermore, the effects of changing parameters such as adsorbent dosage, contact time, pH, temperature, and initial dye concentration on methylene blue adsorption were investigated. Also, Langmuir, Freundlich, and Temkin isotherms have been used to describe the equilibrium characteristics of adsorption. Finally, the experimental data fitted well by Langmuir isotherm with a maximum adsorption capacity of 17.5 mg g−1. The experimental data were applied to pseudo-first- and second-order models. The experimental results were better fitted for the pseudo-second-order model than the other model. Consequently, the experimental results showed that mPGMA is a suitable low-cost adsorbent with great potential benefit in removing methylene blue from aqueous solutions. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Citation - WoS: 6
Citation - Scopus: 8
Pan-Based Activated Carbon Nanofiber/Metal Oxide Composites for Co2 and Ch4 Adsorption: Influence of Metal Oxide
(SCIENTIFIC TECHNICAL RESEARCH COUNCIL TURKEY-TUBITAK, 2021) Kırbıyık Kurukavak, Çisem; Büyükbekar, Burak Zafer; Ersöz, Mustafa
In the present study, we successfully prepared two different electrospun polyacrylonitrile (PAN) based-activated carbon nanofiber (ACNF) composites by incorporation of well-distributed Fe2O3 and Co3O4, nanoparticles (NPs). The influence of metal oxide on the structural, morphological, and textural properties of final composites was thoroughly investigated. The results showed that the morphological and textural properties could be easily tuned by changing the metal oxide NPs. Even though, the ACNE composites were not chemically activated by any activation agent, they presented relatively high surface areas (S-BET) calculated by Brunauer-Emmett-Thller (BET) equation as 212.21 and 185.12 m(2)/g for ACNE/Fe2O3 and ACNF/Co3O4 composites, respectively. Furthermore, the ACNE composites were utilized as candidate adsorbents for CO2 and CH4 adsorption. The ACNF/Fe2O3 and ACNF/Co3O4 composites resulted the highest CO2 adsorption capacities of 1.502 and 2.166 mmol/g at 0 degrees C, respectively, whereas the highest CH4 adsorption capacities were obtained to be 0.516 and 0.661 mmol/g at 0 degrees C by ACNF/Fe2O3 and ACNE/Co3O4 composites, respectively. The isosteric heats calculated lower than 80 kJ/mol showed that the adsorption processes of CO2 and CH4 were mainly dominated by physical adsorption for both ACNE composites. Our findings indicated that ACNF-metal oxide composites are useful materials for designing of CO2 and CH4 adsorption systems.
Citation - WoS: 25
Citation - Scopus: 22
A Phenyl Glycinol Appended Calix[4]arene Film for Chiral Detection of Ascorbic Acid on Gold Surface
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2019) Akpınar, Merve; Temel, Farabi; Tabakcı, Begüm; Özçelik, Egemen; Tabakcı, Mustafa
This paper describes the synthesis of new chiral calix [4]arene derivative having (R)-2-phenylglycinol moiety (compound 6), and its chiral recognition studies for ascorbic acid (AA) enantiomers by using Quartz Crystal Microbalance (QCM). Initial experiments indicated that the outstanding selective chiral recognition (alpha) was observed as 2.61 for L-enantiomer of AA. The sensitivity (S) and the limit of detection (LOD) values for L-AA were calculated as 0.0226 Hz/mu M and 0.63 mu M, respectively. Furthermore, the sorption behavior and mechanism of AA onto compound 6 film were evaluated and the sorption data exhibited a good correlation with the Freundlich isotherm models. The maximum uptake of L-AA by the sensor was found as 5895.76 mg/g. In conclusion, chiral recognition of AA enantiomers as real-time, sensitive, selective and effective was performed by a calixarene derivative coated QCM sensor.

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