Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Publisher: search.filters.publisher.01. Izmir Institute of TechnologySubject: search.filters.subject.Biosensors

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  • Master Thesis
    Deep Learning-Based Analysis of Electrochemical, Biomedical, and Optical Signals
    (01. Izmir Institute of Technology, 2024) Gümüş, Abdurrahman; Gümüş, Abdurrahman; Gümüş, Abdurrahman; Odacı, Dilek; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Bu tez, derin öğrenme (DÖ) tekniklerinin çeşitli alanlardaki uygulamalarını inceleyerek, karmaşık verilerin tespiti, sınıflandırılması ve analizi konularında önemli iyileştirmeler sağlamaktadır. Çalışma, DÖ modellerini farklı analitik yöntemlerle entegre ederek performansı artırmayı amaçlamaktadır. Elektrokimyasal analiz alanında, CD36'nın tespiti ve sınıflandırılması için bir immünobiyosensör kullanılarak DÖ tabanlı bir yaklaşım geliştirilmiştir. Geleneksel teknikler, özellikle düşük analit konsantrasyonlarında duyarlılık ve hızlı analizde yetersiz kalmaktadır. Tek boyutlu evrişimli sinir ağı (1B-ESA) ve hibrit 1B-ESA – uzun kısa süreli bellek (UKSB) ağları gibi DÖ modellerinin entegrasyonu, biyosensörün duyarlılığını ve özgüllüğünü önemli ölçüde artırmıştır. Biyomedikal uygulamalarda, yüzey elektromiyografi (yEMG) sinyalleri kullanılarak el hareketlerinin sınıflandırılması için Vision Transformer (ViT) teknikleri kullanılmıştır. sEMG verileri, ileri zaman-frekans analizi (TFA) yöntemleri ve çeşitli ViT modelleri ile analiz edilerek yüksek doğruluk elde edilmiştir. Optik algılama alanında, Faza Duyarlı - Zaman Bölgesinde Optik Geriyansımalı Ölçüm Tekniği (Faz-OTDR) verilerinin analizi için DÖ teknikleri kullanılmıştır. DÖ yöntemlerinin Faz-OTDR tabanlı akım algılama sistemlerinin verimliliğini artırdığı gösterilmiştir. 1B-ESA, 1B-ESA – UKSB ve 1B-ESA – Çift yönlü UKSB modelleri kullanılarak, akım değerlerinin doğru bir şekilde sınıflandırılması sağlanmıştır. Ayrıca, optik sinyalleri görüntüye çevirme metodu uygulanarak, aktarımlı öğrenme modelleri ile yüksek sınıflandırma doğruluğu elde edilmiş ve veri depolama daha verimli hale getirilmiştir. Bu tez, DÖ tekniklerinin çeşitli analitik yöntemlerle entegrasyonunun önemli ilerlemeler sağlama potansiyelini göstermektedir. Çalışmalar, DÖ'nün veri analizi performansını artırmadaki çok yönlülüğünü, daha doğru, hassas ve verimli çözümler sunarak ortaya koymaktadır. Geliştirilen metodolojiler, diğer biyomarkerlar, sinyal türleri ve analitik zorluklara genişletilebilir.
  • Master Thesis
    Effect of Gold Nanorod Properties on Lspr Response
    (01. Izmir Institute of Technology, 2023) Bulmuş Zareie, Volga; Tekin, Hüseyin Cumhur; Bulmuş Zareie, Esma Volga; Tekin, Hüseyin Cumhur; 03.01. Department of Bioengineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Optical qualities make gold nanorods (GNRs) excellent for plasmonic biosensors. Localized surface plasmon resonance (LSPR) phenomenon which occurs on GNR surfaces enables the creation of highly sensitive biosensors. The physical properties such as aspect ratio and size are directly related to the LSPR response of GNRs. The aim of this study is to investigate the impact of the aspect ratio (AR) and the interparticle distance on the localized surface plasmon resonance (LSPR) response of GNRs decorated glass sensor chips. For this aim, GNRs were first synthesized using a seed-mediated growth method. The effect of AgNO3 concentration on the AR of GNRs was investigated. It was observed that increasing AgNO3 concentration resulted in GNRs with higher AR and a red shift in the longitudinal plasmon peak wavelength. GNRs with an AR of 4, 6 and 8 were successfully synthesized. Next, the effect of the stabilizer molecule type and molecular weight on the distribution of GNRs on the silanized glass surface was investigated. It was found that the APTES modified glass surfaces cannot be coated with CTAB stabilized GNRs. Using GNRs stabilized with PEG5K resulted in a more homogeneous distribution of GNRs on the glass surface with respect to GNRs stabilized with PEG2K. The interparticle distance between GNRs on the glass surface was successfully controlled by simply concentrating or diluting the GNR solution used for coating the glass surfaces. It was observed that the LSPR peak shifts decreased upon binding of analytes as the interparticle distance between GNRs decreased in the studied range. On the other hand, as the AR decreased, the LSPR response of the GNRs shifted blue. The results presented in this thesis may contribute to future research to improve the potential of LSPR-based biosensors for diverse biomedical and diagnostic applications.
  • Master Thesis
    Development of an Advanced Lspr-Based Biosensor Chip for Rapid Detection of Border Disease Virus
    (01. Izmir Institute of Technology, 2023) Bulmuş Zareie, Volga; Tekin, Hüseyin Cumhur; Bulmuş Zareie, Esma Volga; Tekin, Hüseyin Cumhur; 03.01. Department of Bioengineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    The Border Disease Virus (BDV) is responsible for causing fetal deathly infection, leading to annual occurrences of affected farms. BDV, along with other pestiviruses such as classical swine fever virus (CSFV) and bovine viral diarrhea virus (BVDV), are known to cause major losses in stock farming. These losses can result in reproductive failure, expensive inspections, and other impacts on livestock health. The current detection methods of BDV include various techniques such as RT-PCR, ELISA, VNT, and immunofluorescence assays. These methods, although reliable, may require specialized equipment, time-consuming procedures, and laboratory facilities, making them less suitable for rapid on-site detection. Hence, it is imperative to employ diverse methodologies for detection of BDV. LSPR-based biosensors are a subset of plasmonic biosensors that exhibit numerous advantages for diverse applications. LSPR-based biosensors are particularly well-suited for the production of compact, practical devices for rapid, on-site detection of analytes. The aim of this study is to design and fabricate a biosensor chip utilizing LSPR technology for potential BDV detection. For this aim, glass surfaces were functionalized with gold nanorods modified with a BDV-specific primer sequence, complementary single-strand DNA sequence of 19 bases, and fabricated with PMMA microchannels. Different concentrations of target BDV-DNAsequence ranging from 0.01 pM to 100 nM were exposed to the channels, and the LSPR response was quantified using a Vis-NIR spectrometer. The limit of quantification of the biosensor chips was determined to be 10 pM, while the limit of detection was found to be less than or equal to 1 pM. The sensitivity of the biosensor chips was calculated to be 0.0567 nm/RIU. The dynamic range of the biochips lies between 10 pM to 100 pM.