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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6

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Quartile: search.filters.quartile.Q2Subject: search.filters.subject.Biochemical research methods

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Nanoscale communication with molecular arrays in nanonetworks
    (Institute of Electrical and Electronics Engineers (IEEE), 2012) Galmes, Sebastia; Atakan, Barış; Akan, Özgür Barış; PhD Student; Faculty Member; College of Engineering
    Molecular communication is a promising nanoscale communication paradigm that enables nanomachines to exchange information by using molecules as communication carrier. Up to now, the molecular communication channel between a transmitter nanomachine (TN) and a receiver nanomachine (RN) has been modeled as either concentration channel or timing channel. However, these channel models necessitate exact time synchronization of the nanomachines and provide a relatively low communication bandwidth. In this paper, the Molecular ARray-based COmmunication (MARCO) scheme is proposed, in which the transmission order of different molecules is used to convey molecular information without any need for time synchronization. The MARCO channel model is first theoretically derived, and the intersymbol interference and error probabilities are obtained. Based on the error probability, achievable communication rates are analytically obtained. Numerical results and performance comparisons reveal that MARCO provides significantly higher communication rate, i.e., on the scale of 100 Kbps, than the previously proposed molecular communication models without any need for synchronization. More specifically, MARCO can provide more than 250 Kbps of molecular communication rate if intersymbol time and internode distance are set to 2 mu s and 2 nm, respectively.
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    PublicationOpen Access
    Efficient photocapacitors via ternary hybrid photovoltaic optimization for photostimulation of neurons
    (Optical Society of America (OSA), 2020) Department of Electrical and Electronics Engineering; Srivastava, Shashi Bhushan; Melikov, Rustamzhon; Yıldız, Erdost; Han, Mertcan; Şahin, Afsun; Nizamoğlu, Sedat; Researcher; PhD Student; PhD Student; Master Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Health Sciences; School of Medicine; College of Engineering; N/A; N/A; N/A; N/A; 171267; 130295
    Optoelectronic photoelectrodes based on capacitive charge-transfer offer an attractive route to develop safe and effective neuromodulators. Here, we demonstrate efficient optoelectronic photoelectrodes that are based on the incorporation of quantum dots (QDs) into poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-Phenyl-C61-butyric acid methyl ester (PCBM) bulk heterojunction. We control the performance of the photoelectrode by the blend ratio, thickness, and nanomorphology of the ternary bulk heterojunction. The optimization led to a photocapacitor that has a photovoltage of 450 mV under a light intensity level of 20 mW.cm(-2) and a responsivity of 99 mA/W corresponding to the most light-sensitive organic photoelectrode reported to date. The photocapacitor can facilitate action potential generation by hippocampal neurons via burst waveforms at an intensity level of 20 mW.cm(-2). Therefore, the results point to an alternative direction in the engineering of safe and ultra-light-sensitive neural interfaces.
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    PublicationOpen Access
    Machine learning-enabled multiplexed microfluidic sensors
    (American Institute of Physics (AIP) Publishing, 2020) Yetişen, Ali Kemal; N/A; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Dabbagh, Sajjad Rahmani; Rabbi, Fazle; Doğan, Zafer; Taşoğlu, Savaş; Faculty Member; Faculty Member; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 280658; 291971
    High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
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    PublicationOpen Access
    A communication theoretical analysis of FRET-based mobile ad hoc molecular nanonetworks
    (Institute of Electrical and Electronics Engineers (IEEE), 2014) Kuşcu, Murat; Akan, Özgür Barış; Faculty Member; College of Engineering
    Nanonetworks refer to a group of nano-sized machines with very basic operational capabilities communicating to each other in order to accomplish more complex tasks such as in-body drug delivery, or chemical defense. Realizing reliable and high-rate communication between these nanomachines is a fundamental problem for the practicality of these nanonetworks. Recently, we have proposed a molecular communication method based on Forster Resonance Energy Transfer (FRET) which is a nonradiative excited state energy transfer phenomenon observed among fluorescent molecules, i.e., fluorophores. We have modeled the FRET-based communication channel considering the fluorophores as single-molecular immobile nanomachines, and shown its reliability at high rates, and practicality at the current stage of nanotechnology. In this study, for the first time in the literature, we investigate the network of mobile nanomachines communicating through FRET. We introduce two novel mobile molecular nanonetworks: FRET-based mobile molecular sensor/actor nanonetwork (FRET-MSAN) which is a distributed system of mobile fluorophores acting as sensor or actor node; and FRET-based mobile ad hoc molecular nanonetwork (FRETMAMNET) which consists of fluorophore-based nanotransmitter, nanoreceivers and nanorelays. We model the single message propagation based on birth death processes with continuous time Markov chains. We evaluate the performance of FRETMSAN and FRET-MAMNET in terms of successful transmission probability and mean extinction time of the messages, system throughput, channel capacity and achievable communication rates.