Enstitüler

Permanent URI for this communityhttps://hdl.handle.net/20.500.12573/391

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Optik Saçılma Temelli Rastgele Orman Destekli Parçacık Tespiti ve Sınıflandırılması
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Genç, Sinan; Genç, Sinan; İçöz, Kutay; Erdem, Talha
Microplastics, tiny plastic particles with sizes smaller than 5 mm., are often found in oceans, rivers, lakes, and atmosphere due to plastic pollution. Microplastics releasing toxic chemicals threaten the environment and harm the aquatic life and humans. Especially, the accumulation of microplastics can have detrimental effects on the food chain as a result of larger organisms consuming smaller organisms. Detecting the microplastics is crucial but also challenging. Over the years, researchers have developed different detection methods. One of the standard methods is using spectroscopy tools such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. These techniques can identify the chemical composition of microplastics, which can help determine their sources and potential impacts. Another method is the use of microscopy, which allows for the visualization and counting of microplastics in samples. However, these techniques require costly infrastructure, and these instruments being large in size significantly limits the mobility. As a remedy to the cost and mobility problems, in this thesis, we propose and demonstrate a low-cost, portable system to detect size, concentration, and refractive index of microplastics. Our system comprises of low-cost and low-weight components which are utilized for recording the scattering patterns of microplastics in aqueous media. We demonstrate successful predictions of the size and refractive index of microparticles at a given wavelength using a Random Forest Algorithm which relates the measured scattering pattern with the Mie theory. We further employ the refractive index information at various wavelengths for determining the material type of microplastics. We believe that our proposed system enabling an easy, fast, low-cost, and on-site detection of microplastics will be a beneficial tool for the fight against microplastics in the environment.
Yüksek Parlaklık Kuantum Nokta Led Aygıtların Geliştirilmesi
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Biçer, Ayşenur; Mutlugün, Evren
Optoelectronic devices are essential components of optical communication systems, internet and displays. Among these devices, in the category of light emitting diodes (LED), there are quantum dot LEDs (QLED) that emit light by employing quantum dots (QDs) and have rich optoelectronic properties such as varying emission wavelength associated with the its size and excellent brightness [1], [2]. In this thesis, we worked on transparent and solution processible QLEDs in three groups: Indium Phosphide (InP) QLEDs, Carbon Quantum Dot (CQD) LEDs and Cadmium Selenide (CdSe) QLEDs. In the InP study, a QLED was fabricated using InP-based QDs as the emitting layer to demonstrate the feasibility of these QDs. Results found a maximum external quantum efficiency (EQE) of 1.16% and brightness of 1039 cd/m2. For the CQD LEDs, yellow emissive QDs were mixed systematically in Poly(9-vinylcarbazole) (PVK) as the host. A blue-to-white shift was observed in the CIE coordinate with varying ratios. From these, white luminescent devices were obtained with a maximum brightness of 774.3 cd/m2 and an EQE of 0.76%. High-brightness irradiation was obtained compared to other white-luminescent studies in the literature. In CdSe QLEDs, as a proof of concept, devices with a maximum brightness of 111,450 cd/m2 and an EQE of 15.08% were obtained. In these three works, devices with high brightness in their own categories were produced using both heavy metal and non-heavy metal QDs. Keywords: Optoelectronics, LED, QD, CQD LED, InP QLED, CdSe QLED
BBSome, Caenorhabditis Elegans'ta ARL13B'ye Bağımlı İki Farklı Silyanın Birlikte Uzamasını Düzenler
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Turan, Merve Gül; Kaplan, Sebiha Çevik
Cilia or flagella are interchangeably used to refer to the hair-like organelles extending from the cell surface to communicate with environmental signals or triggers. Cilium, the singular form of cilia, and its components are well-conserved structures throughout evolution and are divided into motile and primary cilium. The primary cilia of different cells are seen to form joint cilia by extending in parallel. For instance, PHA and PHB primary cilia in C. elegans protrude from the ends of the dendrite but extend parallel to one another and intersect in the middle portion of the cilia, reaching the same length. Nevertheless, the molecular mechanisms underlying how parallel cilia get similar lengths remain mysterious. In this thesis, we used C. elagans as a model organism to examine the molecular mechanism associated with the cilia direction. We generated various single, double, and triple mutants to examine PHA and PHB cilia for phenotype and length. We found that a Joubert syndrome protein, ARL13B, is required for determining cilia direction in PHA & PHB cilia and ASE & ASI cilia.
3D Biyobaskı Parametrelerinin PCL İskelesinin Basılabilirliği ve Mekanik Davranışı Üzerindeki Etkisi
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Ceylan, Saniye Aylin; İşoğlu, İsmail Alper
Polycaprolactone (PCL) is a synthetic polymer that exhibits desirable properties such as biodegradability, tolerable mechanical properties, and biocompatibility for a diverse range of tissue engineering applications. In this study, we analyzed the effects of polymer concentration (10%, 25%, 50% and 75% w/v), solvent effect (dichloromethane, chloroform and acetic acid), and device parameters (pressure, speed, nozzle-surface distance, nozzle gauge, infill density) on printed scaffolds fabricated through 3D Bioprinting. Scanning electron microscopy (SEM) and optical microscopy were used to assess printability, and uniaxial tensile testing was performed to evaluate mechanical behavior. The aim of this study was to investigate the effects of different printing speeds (5 mm/s, 10 mm/s, and 15 mm/s) on the mechanical properties of PCL_DCM and PCL_CF scaffolds. The scaffolds printed at the lowest speed exhibited the highest ultimate tensile strength (UTS) values. Scaffolds printed at 5 mm/s with the highest printing pressure (480 kPa) demonstrated a remarkably high Young's modulus of 39.69 MPa and a UTS value of 6.4 for PCL_DCM, as well as Young's modulus of 26.80 MPa and a UTS value of 6.3 MPa for PCL_CF. Additionally, we investigated the influence of polymer concentrations (50% and 75%) and infill densities (50%, 70%, and 90%). The results showed that increasing the infill density and using a lower concentration (50%) led to improvements in Young's modulus and UTS values for both PCL_DCM and PCL_CF scaffolds. These results highlight the importance of carefully controlling printing parameters to optimize the mechanical properties of the printed scaffolds.
Işık ile Çapraz Bağlanabilen Aljinat Bazlı Tannik Asit ile Güçlendirilmiş Biyomürekkep Hidrojellerin Hazırlanması ve Karakterizasyonu
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Demirci, Enes Hamdi; İşoğlu, Sevil Dinçer; Demirtaş, Tuğrul Tolga
Alginate is a commonly used biopolymer in bioprinting applications. However, alginate-based bioinks have some mechanical limitations for printing purposes. Also, existing methacrylation methods are time consuming and have low methacrylation efficiencies. Based on these facts, we focused on enhancing mechanical strength of alginate within the scope of this thesis. To do this, we applied microwave irradiation during methacrylation process of alginate and compared the efficiencies between conventional and microwave irradiation. Here, we report a significantly faster and more effective method for the controlled synthesis of methacrylated alginate (Alg-MA) by microwave energy (250 W) with approximately 80% degree of methacrylation (DM) even with a very low amount of metyhacrylation agent (aminoethyl methacrylate (AEMA)). Rheological and mechanical analyses showed that Alg-MAs synthesized by microwave irradiation allowed the formation of more elastic and stronger hydrogels with very high stability than the ones synthesized by the conventional method. Additionally, we combined these hydrogels with tannic acid by a second cross-linking in order to improve their mechanical strength and tissue integration ability. Addition of TA provided hydrogels very good mechanical strength and also antibacterial characteristics towards gram-positive and gram-negative bacteria. As a conclusion, hydrogels with mechanically superior properties and antibacterial characteristics were obtained by MW-assisted methacrylation and physical cross-linking by TA.
Derin Öğrenme Yöntemleri Kullanarak Dermatoskopik Görüntülerden Otomatik Cilt Kanseri Tespiti ve Sınıflandırılması
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Kalaycı, Serdar; Yılmaz, Bülent
Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. The most prevalent form of cancer is skin cancer and if left undetected, it can spread and become more difficult to treat. A dangerous and frequently fatal type of skin cancer is melanoma. Regular skin examinations and self-examinations can help identify suspicious moles or lesions, which can then be evaluated by a dermatologist. In addition, advances in technology and artificial intelligence have enabled the development of tools for automated skin cancer screening, providing a convenient and efficient means of early detection. This can lead to more efficient diagnosis, reduced healthcare costs and improved patient care. By evaluating skin lesions from images, deep learning techniques have shown considerable potential in increasing the precision of melanoma detection. By using large datasets and complex neural networks, deep learning algorithms can effectively distinguish between benign and malignant skin lesions with high accuracy. Ensemble of CNN models helps improve the performance and reliability of the classification task. By combining the predictions of multiple CNN models lead to more accurate and robust predictions. In this thesis, for melanoma classification problem, many different data augmentations techniques applied and different convolutional neural networks architectures evaluated, applied vignetting effect filter and hair noise in accordance with the dataset and results of ensemble of the best CNN models are promising. This thesis attempts to produce a reliable model for the classification of melanoma by conducting experiments on two combined publically accessible data sets, ISIC 2019 and ISIC 2020. On the testing sets in our studies, the proposed solution attained 95.75% AUC.

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