Researcher:
Onbaşlı, Mehmet Cengiz

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Mehmet Cengiz

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Onbaşlı

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Onbaşlı, Mehmet Cengiz

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2016 - 2019162020 - 202322

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    Thickness-dependent double-epitaxial growth in strained SrTi0.7Co0.3O3-delta films
    (American Chemical Society (ACS), 2018) Tang, Astera S.; Sun, Xueyin; Ross, Caroline A.; Department of Electrical and Electronics Engineering; Onbaşlı, Mehmet Cengiz; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 258783
    Perovskite-structured SrTi0.7Co0.3O3-delta (STCo) films of varying thicknesses were grown on SrTiO3(001) substrates using pulsed laser deposition. Thin films grow with a cube-on-cube epitaxy, but for films exceeding a critical thickness of about 120 nm, a double-epitaxial microstructure was observed, in which (110)-oriented crystals nucleated within the (001)-oriented STCo matrix, both orientations being epitaxial with the substrate. The crystal structure, strain state, and magnetic properties are described as a function of film thickness. Both the magnetic moment and the coercivity show maxima at the critical thickness. The formation of a double-epitaxial microstructure provides a mechanism for strain relief in epitaxially mismatched films.
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    Materials anddevices for integrated room temperature quantum spintronics
    (Scientific and Technological Research Council of Turkey, 2021) Department of Electrical and Electronics Engineering; Onbaşlı, Mehmet Cengiz; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 258783
    Recent advances in precise stoichiometry control and high-resolution characterization of advanced spintronic materials allowed for the development of integrated spintronic devices, which might enable ultralow power magnonic devices with multi-THz spin wave bandwidth and topologically protected spin wavefunctions that are robust for fabrication imperfections. In addition, advances in microwave and optical excitation and control of quantum states in diamond nitrogen-vacancy systems (diamond-NV) allowed for ultrasensitive magnetometry and integrated quantum logic applications. Here, an integrated spintronic garnet/diamond-NV quantum system has been reviewed and discussed for logic and memory applications. After an overview of the recent advances in the growth and characterization of insulating magnetic iron garnets, previous computational demonstrations of ultrawide bandwidth topologically protected few-nanometer size chiral spin structures called skyrmions are discussed for carrying information on chip between diamond-NV systems. Next, earlier diamond-NV characterization studies using microwave ferromagnetic resonance and photoluminescence measurements were reviewed. Finally, a brief discussion is presented on the steps needed for integrated quantum spintronic devices to operate at room temperature.
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    Symmetric meandering distributed feedback structures for silicon photonic circuits
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Physics; Chaudhry, Muhammad Rehan; Zakwan, Muhammad; Onbaşlı, Mehmet Cengiz; Serpengüzel, Ali; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Physics; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Sciences; N/A; N/A; 258783; 27855
    Fano lineshapes and electro-magnetically induced transparency-like peaks in the transmittance of a transverse electric polarization silicon-on-insulator symmetric meandering distributed feedback photonic structure are demonstrated. The coupling constants at the five identical directional couplers are varied to obtain the desired spectral responses. The numerically simulated and experimentally measured transmittance spectra are in good agreement with each other. The numerically calculated and experimentally measured insertion loss for the symmetric meandering distributed feedback structure with directional coupler coupling length L-c = 10 mu m are respectively -5 dB and -17 dB, including the grating couplers. Fano lineshapes with mode splitting is observed at directional coupler coupling constant value C of 0.24. For coupling constant value of C similar to 0.78, electro-magnetically induced transparency-like peaks are observed, and spectrally adjusted by varying the directional coupler coupling length. Fano lineshapes show an extinction ratio of more than 26 dB and slope ratio of 368 dB/nm. Electro-magnetically induced transparency-like peaks show a quality-factor on the order of 5 x 10(4). The symmetric meandering distributed feedback structure shows promise for possible applications as an optical switch, and an optical filter in wavelength division multiplexing and data networks, as well as optical sensors in optical diagnostics, using silicon photonics.
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    Snowflakes: a prototyping tool for computational jewelry
    (2021) Buruk, Turan Oğuz; Genç, Çağlar; N/A; Department of Electrical and Electronics Engineering; Yıldırım, İhsan Ozan; Onbaşlı, Mehmet Cengiz; Özcan, Oğuzhan; Researcher; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; N/A; College of Engineering; N/A; 258783
    Smart-jewelry design has many layers such as comfort, ergonomics, fashionability, interactivity and functionality that create a complex design process, making the form exploration challenging. Various wearable prototyping tools were developed to overcome this challenge; however, they are usually textile-based and do not target smart jewelry design. To bridge this gap, we developed Snowflakes that differentiates from existing tools by 1) allowing designers to explore different jewelry forms, 2) incorporating external materials such as leather, 3) creating form factors that fit body parts with flexible connectors. In this paper, we explain the design process of Snowflakes which is inspired by 7 design parameters (limbs, materials, grip, fastener, decoration, placement, form) extracted through the examination of non-smart jewelry. We also demonstrate three reimplementations and design concepts implemented with Snowflakes. Our exploration with Snowflakes contributes to the wearable community in terms of smart-jewelry visual expressions, interaction modalities, and merger of traditional and computational materials.
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    Machine learning-based approach to identify formalin-fixed paraffin-embedded glioblastoma and healthy brain tissues
    (Spie-Int Soc Optical Engineering, 2022) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Torun, Hülya; Batur, Numan; Bilgin, Buse; Esengür, Ömer Tarık; Baysal, Kemal; Kulaç, İbrahim; Solaroğlu, İhsan; Onbaşlı, Mehmet Cengiz; PhD Student; Undergraduate Student; PhD Student; Undergraduate Student; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; N/A; N/A; N/A; N/A; 119184; 170305; 102059; 258783
    Glioblastoma is the most malignant and common high-grade brain tumor with a 14-month overall survival length. According to recent World Health Organization Central Nervous System tumor classification (2021), the diagnosis of glioblastoma requires extensive molecular genetic tests in addition to the traditional histopathological analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Time-consuming and expensive molecular tests as well as the need for clinical neuropathology expertise are the challenges in the diagnosis of glioblastoma. Hence, an automated and rapid analytical detection technique for identifying brain tumors from healthy tissues is needed to aid pathologists in achieving an error-free diagnosis of glioblastoma in clinics. Here, we report on our clinical test results of Raman spectroscopy and machine learning-based glioblastoma identification methodology for a cohort of 20 glioblastoma and 18 white matter tissue samples. We used Raman spectroscopy to distinguish FFPE glioblastoma and white matter tissues applying our previously reported protocols about optimized FFPE sample preparation and Raman measurement parameters. One may analyze the composition and identify the subtype of brain tumors using Raman spectroscopy since this technique yields detailed molecule-specific information from tissues. We measured and classified the Raman spectra of neoplastic and non-neoplastic tissue sections using machine learning classifiers including support vector machine and random forest with 86.6% and 83.3% accuracies, respectively. These proof-of-concept results demonstrate that this technique might be eventually used in the clinics to assist pathologists once validated with a larger and more diverse glioblastoma cohort and improved detection accuracies.
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    Anisotropic gold nanostructures: optimization via in silico modeling for hyperthermia
    (Amer Chemical Soc, 2018) Singh, Ajay Vikram; Jahnke, Timotheus; Wang, Shuo; Xiao, Yang; Alapan, Yunus; Kozielski, Kristen; David, Hilda; Richter, Gunther; Bill, Joachim; Laux, Peter; Luch, Andreas; Sitti, Metin; Department of Electrical and Electronics Engineering; N/A; Onbaşlı, Mehmet Cengiz; Kharratian, Soheila; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 258783; N/A
    Protein- and peptide-based manufacturing of self-assembled supramolecular functional materials has been a formidable challenge for biomedical applications, being complex in structure and immunogenic in nature. In this context, self assembly of short amino acid sequences as simplified building blocks to design metal-biomolecule frameworks (MBioFs) is an emerging field of research. Here, we report a facile, bioinspired route of anisotropic nanostructure synthesis using gold binding peptides (10-15mers) secreted by cancer cells. The bioinformatics tool i-TASSER predicts the effect of amino acid sequences on metal binding sites and the secondary structures of the respective peptide sequence. Electron microscopy, X-ray, infrared, and Raman spectroscopy validated the versatile anisotropic gold nanostructures and the metal-bioorganic nature of this biomineralization. We studied the influence of precursor salt, pH, and peptide concentration on the evolution of nanoleaf, nanoflower, nanofiber, and dendrimer-like anisotropic MBioFs. Characterization of photothermal properties using infrared laser (785 nm) revealed excellent conversion of light into heat. Exposure of bacterial cells in culture exhibits high rate of photothermal death using lower laser power (1.9 W/cm(2)) compared with recent reports. The MBioF's self-assembly process shown here can readily be extended and adapted to superior plasmonic material synthesis with a promising photothermal effect for in vivo biofilm destruction and cancer hyperthermia applications.
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    Effect of laser pulse fluence, waveform and film thickness on ultrafast magnetization dynamics in nickel
    (Optica Publishing Group (formerly OSA), 2020) Department of Electrical and Electronics Engineering; N/A; Onbaşlı, Mehmet Cengiz; Zanjani, Saeedeh Mokarian; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 258783; N/A
    The effect of femtosecond laser pulse parameters on ultrafast magnetization dynamics in Nickel films is modeled. For Gaussian laser pulse (unlike sinc), Ni recovers its magnetization in one picosecond within an optimal laser fluence range.
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    An efficient and low-latency deep inertial odometer for smartphone positioning
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) Abdel-Qader, A.; N/A; Department of Electrical and Electronics Engineering; Soyer, Muhammet Serhat; Onbaşlı, Mehmet Cengiz; Master Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 258783
    The Global Positioning System (GPS) is not an effective solution for pedestrian indoor navigation using embedded devices because of poor signal penetration and high power requirements. Indoor positioning based purely on commercial-grade inertial measurement units (IMUs) may provide a good alternative but their significant noise and random bias must be eliminated. Although Kalman filters and pedestrian dead reckoning helped reduce the errors using IMUs, these methods cannot prevent the divergence of estimated position error. Deep learning was proposed for accurate position estimation with IMUs. Despite the progress, robust position estimation in mobile embedded devices using deep learning has not been established yet because of memory requirements, latency and inaccurate position estimation especially for stationary pedestrians. In this study, we present an efficient embedded deep learning approach for robust, real-time and accurate pedestrian position estimation using commercial Android smartphone IMUs. We first extended a publicly available deep inertial navigation dataset (OxIOD) with stationary data to enhance the positioning accuracy for both steady state and motion. Next, we trained and tested a deep learning architecture that yields a higher positioning accuracy with 50% lower network latency and 31% lower network size compared with earlier deep positioning networks such as IONet. Our real-time tests of the model in an Android smartphone indicated that the extension for the dataset reduces the position shift when the smartphone is stationary. Since our embedded deep learning solution simultaneously decreases the positioning error, latency and memory requirements, the solution paves the way for numerous practical indoor navigation applications.
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    Clinical validation of SERS metasurface SARS-CoV-2 biosensor
    (Spie-Int Soc Optical Engineering, 2022) İlgu, Müslüm; Yanık, Cenk; Çelik, Süleyman; Öztürk, Meriç; N/A; N/A; Department of Electrical and Electronics Engineering; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Bilgin, Buse; Torun, Hülya; Doğan, Özlem; Ergönül, Önder; Solaroğlu, İhsan; Can, Füsun; Onbaşlı, Mehmet Cengiz; PhD Student; PhD Student; Undergraduated Student; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; Koç University Hospital; N/A; N/A; N/A; 170418; 110398; 102059; 103165; 258783
    The real-time polymerase chain reaction (RT-PCR) analysis using nasal swab samples is the gold standard approach for COVID-19 diagnosis. However, due to the high false-negative rate at lower viral loads and complex test procedure, PCR is not suitable for fast mass screening. Therefore, the need for a highly sensitive and rapid detection system based on easily collected fluids such as saliva during the pandemic has emerged. In this study, we present a surface-enhanced Raman spectroscopy (SERS) metasurface optimized with genetic algorithm (GA) to detect SARS-CoV-2 directly using unprocessed saliva samples. During the GA optimization, the electromagnetic field profiles were used to calculate the field enhancement of each structure and the fitness values to determine the performance of the generated substrates. The obtained design was fabricated using electron beam lithography, and the simulation results were compared with the test results using methylene blue fluorescence dye. After the performance of the system was validated, the SERS substrate was tested with inactivated SARS-CoV-2 virus for virus detection, viral load analysis, cross-reactivity, and variant detection using machine learning models. After the inactivated virus tests are completed, with 36 PCR positive and 33 negative clinical samples, we were able to detect the SARS-CoV-2 positive samples from Raman spectra with 95.2% sensitivity and specificity.
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    Genetic algorithm-driven design of SERS-active surfaces for early detection of diseases
    (Spie-Int Soc Optical Engineering, 2020) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; Department of Electrical and Electronics Engineering; Bilgin, Buse; Türkmen, Berkay; Baysal, Kemal; Solaroğlu, İhsan; Onbaşlı, Mehmet Cengiz; PhD Student; Undergraduate Student; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; School of Medicine; College of Engineering; N/A; N/A; 119184; 102059; 258783
    Surface-enhanced Raman spectroscopy (SERS) enables the surface plasmon-based amplification and detection of Raman signals from biomarkers, which emerge at ultralow concentrations in the early phases of diseases. Thus, SERS chips could be used for early detection of diseases from their biomarkers obtained from liquid or tissue biopsies. While this surface enhancement capability of nanoscale gold or silver layers on different substrates were demonstrated in previous experiments and electromagnetic models, the position of the biomarker molecules on the SERS chips cannot be known or estimated a priori. As a result, SERS chips must be designed over millimeter-scale areas such that the signal amplification must be large (10(6) times or higher with respect to no SERS) and must span the entire slide. Simultaneous surface-enhancement of Raman signals and distributing this enhancement factor (EF) over the sample surface requires an iterative and \learning" design procedure for the geometries of nanoscale metallic features that could maximize both EF and its area simultaneously. In this study, we develop genetic algorithms and use finite-difference time-domain (FDTD) modeling to optimize the geometry of gold nanostructures (NS) on glass microscope slides to functionalize these slides as SERS-active surfaces for SERS-based enhancement of Raman spectra. By using FDTD models, we calculated the enhancement factors in 3D on glass surface for 785 nm laser for Raman spectrum measurements and used genetic algorithms (GA) to iterate on the metal NS geometry to maximize the average and the hot spot EF over the periodic patterns on the slide. Field enhancement factors as high as 10(17) and 10(15) were calculated for hot-spots and for whole-slide averages, respectively. The optimized structures indicate that GA could help maximize label-free and whole-slide Raman signal enhancement factors for single-cell SERS detection.