https://hdl.handle.net/20.500.12573/202 Fri, 08 May 2026 05:17:41 GMT 2026-05-08T05:17:41Z https://hdl.handle.net/20.500.12573/2537 Electrochemical and Optical Multi-Detection of Escherichia coli Through Magneto-Optic Nanoparticles: A Pencil-on-Paper Biosensor Soysaldi, Furkan; Ekici, Derya Dincyurek; Soylu, Mehmet cagri; Mutlugun, Evren Escherichia coli (E. coli) detection suffers from slow analysis time and high costs, along with the need for specificity. While state-of-the-art electrochemical biosensors are cost-efficient and easy to implement, their sensitivity and analysis time still require improvement. In this work, we present a paper-based electrochemical biosensor utilizing magnetic core-shell Fe2O3@CdSe/ZnS quantum dots (MQDs) to achieve fast detection, low cost, and high sensitivity. Using electrochemical impedance spectroscopy (EIS) as the detection technique, the biosensor achieved a limit of detection of 2.7 x 10(2) CFU/mL for E. coli bacteria across a concentration range of 10(2)-10(8) CFU/mL, with a relative standard deviation (RSD) of 3.5781%. From an optical perspective, as E. coli concentration increased steadily from 10(4) to 10(7) CFU/mL, quantum dot fluorescence showed over 60% lifetime quenching. This hybrid biosensor thus provides rapid, highly sensitive E. coli detection with a fast analysis time of 30 min. This study, which combines the detection advantages of electrochemical and optical biosensor systems in a graphite-based paper sensor for the first time, has the potential to meet the needs of point-of-care applications. It is thought that future studies that will aim to examine the performance of the production-optimized, portable, graphite-based sensor system on real food samples, environmental samples, and especially medical clinical samples will be promising. Mon, 01 Jan 2024 00:00:00 GMT https://hdl.handle.net/20.500.12573/2537 2024-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2528 Molecular Separation by Using Active and Passive Microfluidic chip Designs: A Comprehensive Review Ebrahimi, Aliakbar; Icoz, Kutay; Didarian, Reza; Shih, Chih-Hsin; Tarim, E. Alperay; Nasseri, Behzad; Akpek, Ali; Cecen, Berivan; Bal-Ozturk, Ayca; Gulec, Kadri; Li, Yi-Chen Ethan; Shih, Steven; Tarim, Burcu Sirma; Tekin, H. Cumhur; Alarcin, Emine; Tayybi-Azar, Mehdi; Ghorbanpoor, Hamed; Ozel, Ceren; Sariboyaci, Ayla Eker; Guzel, Fatma Dogan; Bassous, Nicole; Shin, Su Ryon; Avci, Huseyin Separation and identification of molecules and biomolecules such as nucleic acids, proteins, and polysaccharides from complex fluids are known to be important due to unmet needs in various applications. Generally, many different separation techniques, including chromatography, electrophoresis, and magnetophoresis, have been developed to identify the target molecules precisely. However, these techniques are expensive and time consuming. “Lab-on-a-chip” systems with low cost per device, quick analysis capabilities, and minimal sample consumption seem to be ideal candidates for separating particles, cells, blood samples, and molecules. From this perspective, different microfluidic-based techniques have been extensively developed in the past two decades to separate samples with different origins. In this review, “lab-on-a-chip” methods by passive, active, and hybrid approaches for the separation of biomolecules developed in the past decade are comprehensively discussed. Due to the wide variety in the field, it will be impossible to cover every facet of the subject. Therefore, this review paper covers passive and active methods generally used for biomolecule separation. Then, an investigation of the combined sophisticated methods is highlighted. The spotlight also will be shined on the elegance of separation successes in recent years, and the remainder of the article explores how these permit the development of novel techniques. Mon, 01 Jan 2024 00:00:00 GMT https://hdl.handle.net/20.500.12573/2528 2024-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2522 Exploring CsPbX3 (X = Cl, Br, I) Perovskite Nanocrystals in Amorphous Oxide Glasses: Innovations in Fabrication and Applications Samiei, Sadaf; Soheyli, Ehsan; Vighnesh, Kunnathodi; Nabiyouni, Gholamreza; Rogach, Andrey L. Metal halide perovskites with excellent optical and electronic properties have become a trending material in the current research. However, their limited stability under ambient conditions degrades quality and threatens their potential commercialization as optoelectronic devices. Various approaches are adopted to improve the stability of perovskite nanocrystals (PeNC) while maintaining their advantageous optical properties, particularly strong luminescence. Among different possible improvement strategies, encapsulation of PeNCs within the amorphous glass matrices of inorganic oxides has drawn widespread attention because it ensures high resistance against chemical corrosion and high temperature, thus enhancing their chemical, thermal, and mechanical stability with improved light-emission characteristics. In this article, two types of materials, namely all-inorganic metal halide PeNCs and amorphous oxide glasses are briefly introduced, and then the methods are reviewed to fabricate and improve the quality of PeNC@glass composites. These methods are classified into three universal categories: compositional modification, structural modification, and dual encapsulation. In the final part of this review paper, examples of applications of PeNCs@glass composites in light-emitting devices and displays, data storage and anti-counterfeiting, lasing, photodetectors and X-ray detectors, photocatalysis, optical filters, solar concentrators, and batteries are provided. Mon, 01 Jan 2024 00:00:00 GMT https://hdl.handle.net/20.500.12573/2522 2024-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2518 ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments Golec, Muhammed; Gill, Sukhpal Singh; Cuadrado, Felix; Parlikad, Ajith Kumar; Xu, Minxian; Wu, Huaming; Uhlig, Steve Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and CO2 emission amount of these models are evaluated and compared for the training and prediction phases. Mon, 01 Jan 2024 00:00:00 GMT https://hdl.handle.net/20.500.12573/2518 2024-01-01T00:00:00Z