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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Deep learning-augmented T-junction droplet generation(Elsevier Inc., 2024) N/A; Department of Mechanical Engineering; Ahmadpour, Abdollah; Shojaeian, Mostafa; Taşoğlu, Savaş; Department of Mechanical Engineering; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of EngineeringDroplet generation technology has become increasingly important in a wide range of applications, including biotechnology and chemical synthesis. T-junction channels are commonly used for droplet generation due to their integration capability of a larger number of droplet generators in a compact space. In this study, a finite element analysis (FEA) approach is employed to simulate droplet production and its dynamic regimes in a T-junction configuration and collect data for post-processing analysis. Next, image analysis was performed to calculate the droplet length and determine the droplet generation regime. Furthermore, machine learning (ML) and deep learning (DL) algorithms were applied to estimate outputs through examination of input parameters within the simulation range. At the end, a graphical user interface (GUI) was developed for estimation of the droplet characteristics based on inputs, enabling the users to preselect their designs with comparable microfluidic configurations within the studied range.Publication Metadata only Selection of ionic liquid electrolytes for high-performing lithium-sulfur batteries: an experiment-guided high-throughput machine learning analysis(Elsevier B.V., 2024) Kılıç, Ayşegül; Abdelaty, Omar; Yıldırım, Ramazan; Eroğlu, Damla; Department of Chemical and Biological Engineering; Zeeshan, Muhammad; Uzun, Alper; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; College of EngineeringThe polysulfide (PS) shuttle mechanism (PSM) is one of the most significant challenges of lithium-sulfur (Li-S) batteries in achieving high capacity and cyclability. One way to minimize the shuttle effect is to limit the PS solubilities in the battery electrolyte. Ionic liquids (IL) are particularly suited as electrolyte solvents because of their tunable physical and chemical properties. In this work, thousands of ILs are screened to narrow down potentially viable candidates to be used as electrolytes in Li-S batteries. To that end, the COnductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations are performed over more than 36,000 ILs. An extensive database containing PS solubilities and other relevant properties is constructed at 25 °C. First, the effectiveness of the COSMO-RS calculations is experimentally tested with six different ILs having a wide range of solubility and viscosity values; a strong correlation between the PS solubility and battery performance is obtained. After specifying the target limits for promising ILs using the experimental battery performance data, machine learning (ML) tools are used to predict and identify the relationship between IL properties and PS solubilities and structural and molecular descriptors of ILs. The extreme gradient boosting (XGBoost) method successfully predicts the solubility and property values. Association rule mining (ARM) and the feature importance analysis show that anion descriptors are more dominant, whereas cations have less impact on the solubilities and properties of ILs. Finally, the imidazolium and pyridinium ILs with bis_imide and borate anion groups are identified as the most promising ones.Publication Metadata only Spatial and thermal aware methods for efficient workload management in distributed data centers(Elsevier B.V., 2024) N/A; Department of Computer Engineering; Ali, Ahsan; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringGeographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.Publication Metadata only Hydrothermal liquefaction of chlamydomonas nivalis and nannochloropsis gaditana microalgae under different operating conditions over copper-exchanged zeolites(Elsevier B.V., 2024) Borhan, E.; Haznedaroglu, Berat Z.; Department of Chemical and Biological Engineering; Yousefzadeh, Hamed; Uzun, Alper; Erkey, Can; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of EngineeringIn this study, two different green microalgae, Chlamydomonas nivalis (C. nivalis) and Nannochloropsis gaditana (N. gaditana), were cultivated in open ponds and the harvested wet biomass was converted to bio-crude by hydrothermal liquefaction (HTL) with/without catalyst. Catalytic HTL experiments were performed by using copper-exchanged zeolites including Cu-MOR, Cu-ZSM-5, and Cu-SSZ13, synthesized by recently developed supercritical ion exchange method using scCO2. The composition of all bio-crudes was analyzed by elemental analysis and GC/MS. First, the effects of different operating conditions on the yields of the products and the bio-crude composition were determined for non-catalytic process. Temperature, duration, and water/algae biomass ratio in the feed were the process parameters investigated in the ranges of 250–350 ºC, 10–60 min, and 5–20 wt%, respectively. For C. nivalis, 300 ºC, 60 min, and water/algae ratio of 4 were the optimum conditions which led to maximum bio-crude yield of 18.8 wt%, while 300 ºC, 30 min, and water/algae ratio of 9 were the optimum ones for N. gaditana at which the maximum bio-crude yield of 34.0 wt% was observed. Bio-crude yield of N. gaditana was improved using Cu-MOR, while using catalysts for the case of C. nivalis resulted in more gasification with no positive effect on bio-crude yield. Moreover, elemental analysis showed that the fraction of nitrogen and oxygen in biocrude decreased in catalytic HTL runs, in line with the GC/MS results showing that the concentration of hydrocarbons and cyclic compounds increased in the presence of catalysts accompanied by a decrease in concentration of nitrogenous compounds.Publication Metadata only Discrete memories of a continuous world: a working memory perspective on event segmentation(Elsevier B.V., 2024) Güler, Berna; Uysal, Bilge; Günseli, Eren; Adıgüzel, Zeynep; Graduate School of Social Sciences and HumanitiesWe perceive the world in a continuum but remember our past as discrete episodic events. Dominant models of event segmentation suggest that prediction errors or contextual changes are the driving factors that parse continuous experiences into segmented events. These models propose working memory to hold a critical role in event segmentation, yet the particular functioning of working memory that underlies segmented episodic memories remains unclear. Here, we first review the literature regarding the factors that result in the segmentation of episodic memories. Next, we discuss the role of working memory under two possible models regarding how it represents information within each event and suggest experimental predictions. Clarifying the contributions of working memory to event segmentation is important to improve our understanding of the structure of episodic memories.Publication Metadata only Metric-bourbaki algebroids: cartan calculus for m-theory(Elsevier, 2024) Çatal-Özer, Aybike; Doğan, Keremcan; Department of Physics; Dereli, Tekin; Department of Physics; College of SciencesString and M theories seem to require generalizations of usual notions of differential geometry on smooth manifolds. Such generalizations usually involve extending the tangent bundle to larger vector bundles equipped with various algebroid structures such as Courant algebroids, higher Courant algebroids, metric algebroids, or G-algebroids. The most general geometric scheme is not well understood yet, and a unifying framework for such algebroid structures is needed. Our aim in this paper is to propose such a general framework. Our strategy is to follow the hierarchy of defining axioms for a Courant algebroid: almostCourant - metric - pre -Courant - Courant. In particular, we focus on the symmetric part of the bracket and the metric invariance property, and try to make sense of them in a manner as general as possible. These ideas lead us to define new algebroid structures which we dub Bourbaki and metric-Bourbaki algebroids, together with their almostand pre -versions. For a special case of metric-Bourbaki algebroids that we call exact, we construct a collection of maps which generalize the Cartan calculus of exterior derivative, Lie derivative and interior product. This is done by a kind of reverse -mathematical analysis of the Severa classification of exact Courant algebroids. By abstracting crucial properties of this collection of maps, we define the notion of Bourbaki calculus. Conversely, given an arbitrary Bourbaki calculus, we construct a metric-Bourbaki algebroid by building up a standard bracket that is analogous to the Dorfman bracket. Moreover, we prove that any exact metric-Bourbaki algebroid satisfying some further conditions has to have a bracket that is the twisted version of the standard bracket; a partly analogous result to Severa classification. We prove that many physically and mathematically motivated algebroids from the literature are examples of these new algebroids, and when possible we construct a Bourbaki calculus on them. In particular, we show that the Cartan calculus can be seen as the Bourbaki calculus corresponding to an exact higher Courant algebroid. We also point out examples of Bourbaki calculi including the generalization of the Cartan calculus on vector bundle valued forms. One straightforward generalization of our constructions might be done by replacing the tangent bundle with an arbitrary Lie algebroid A. This step allows us to define an extension of our results, A -version, and extend our main results for them while proving many other algebroids from the literature fit into this framework.Publication Metadata only Mechanical properties of silicon nanowires with native oxide surface state(Elsevier, 2024) Department of Mechanical Engineering; Zarepakzad, Sina; Esfahani, Mohammad Nasr; Alaca, Burhanettin Erdem; Department of Mechanical Engineering; n2STAR-Koç University Nanofabrication and Nanocharacterization Center for Scientifc and Technological Advanced Research; Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; College of EngineeringSilicon nanowires have attracted considerable interest due to their wide-ranging applications in nanoelectromechanical systems and nanoelectronics. Molecular dynamics simulations are powerful tools for studying the mechanical properties of nanowires. However, these simulations encounter challenges in interpreting the mechanical behavior and brittle to ductile transition of silicon nanowires, primarily due to surface effects such as the assumption of an unreconstructed surface state. This study specifically focuses on the tensile deformation of silicon nanowires with a native oxide layer, considering critical parameters such as cross-sectional shape, length -to -critical dimension ratio, temperature, the presence of nano -voids, and strain rate. By incorporating the native oxide layer, the article aims to provide a more realistic representation of the mechanical behavior for different critical dimensions and crystallographic orientations of silicon nanowires. The findings contribute to the advancement of knowledge regarding size -dependent elastic properties and strength of silicon nanowires.Publication Metadata only Rethinking news trust in post-truth Turkey: immediacy as the imagined affordance of television and search engines(SAGE PUBLICATIONS INC, 2024) Department of Media and Visual Arts; Çamurdan, Suncem Koçer; Ünal, Nazlı Özkan; Department of Media and Visual Arts; College of Social Sciences and HumanitiesIn today's post-truth world, news users grapple with the tension between growing distrust in news institutions and the need for "true" information. Based on a mixed-methods study conducted in Turkey, this paper examines strategies developed by news users to establish trust in media tools in the context of the COVID-19 pandemic and populist polarization. We first collected data with a nationally representative survey (N = 1089). Then, 30 media users filled out media diaries for 1 week. We interviewed diary participants at the end of the week. We also conducted a four-week-long participant observation in three locations. Based on this data, we argue that users build trust in news stories by attributing a sense of immediacy to specific media, namely television and search engines. This immediacy arises from people's desire to scrutinize the accuracy of news stories in Turkey's highly polarized media environment. We term this ascribed meaning of transparency the imagined affordance of immediacy, asserting that immediacy is crucial for forming trust in the post-truth era. Contrary to suggestions that news trust is diminishing in the post-truth era, our paper highlights citizens' creative strategies to reestablish trust in contemporary news media.Publication Metadata only Objective-free ultrasensitive biosensing on large-area metamaterial surfaces in the near-IR(AMER CHEMICAL SOC, 2024) Department of Physics; Ramazanoğlu, Serap Aksu; Öktem, Evren; Department of Physics; College of Sciences; Graduate School of Sciences and EngineeringPlasmonic metamaterials have opened new avenues in medical diagnostics. However, the transfer of the technology to the markets has been delayed due to multiple challenges. The need of bulky optics for signal reading from nanostructures patterned on submillimeter area limits the miniaturization of the devices. The use of objective-free optics can solve this problem, which necessitates large area patterning of the nanostructures. In this work, we utilize laser interference lithography (LIL) to pattern nanodisc-shaped metamaterial absorber nanoantennas over a large area (4 cm(2)) within minutes. The introduction of a sacrificial layer during the fabrication process enables an inverted hole profile and a well-controlled liftoff, which ensures perfectly defined uniform nanopatterning almost with no defects. Furthermore, we use a macroscopic reflection probe for optical characterization in the near-IR, including the detection of the binding kinematics of immunologically relevant proteins. We show that the photonic quality of the plasmonic nanoantennas commensurates with electron-beam-lithography-fabricated ones over the whole area. The refractive index sensitivity of the LIL-fabricated metasurface is determined as 685 nm per refractive index unit, which demonstrates ultrasensitive detection. Moreover, the fabricated surfaces can be used multiple times for biosensing without losing their optical quality. The combination of rapid and large area nanofabrication with a simple optical reading not only simplifies the detection process but also makes the biosensors more environmentally friendly and cost-effective. Therefore, the improvements provided in this work will empower researchers and industries for accurate and real-time analysis of biological systems.Publication Metadata only Event-triggered reinforcement learning based joint resource allocation for ultra-reliable low-latency V2X communications(Institute of Electrical and Electronics Engineers Inc., 2024) Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Khan, Nasir; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and EngineeringFuture 6G-enabled vehicular networks face the challenge of ensuring ultra-reliable low-latency communication (URLLC) for delivering safety-critical information in a timely manner. Existing resource allocation schemes for vehicle-toeverything (V2X) communication systems primarily rely on traditional optimization-based algorithms. However, these methods often fail to guarantee the strict reliability and latency requirements of URLLC applications in dynamic vehicular environments due to the high complexity and communication overhead of the solution methodologies. This paper proposes a novel deep reinforcement learning (DRL) based framework for the joint power and block length allocation to minimize the worst-case decoding-error probability in the finite block length (FBL) regime for a URLLC-based downlink V2X communication system. The problem is formulated as a non-convex mixed-integer nonlinear programming problem (MINLP). Initially, an algorithm grounded in optimization theory is developed based on deriving the joint convexity of the decoding error probability in the block length and transmit power variables within the region of interest. Subsequently, an efficient event-triggered DRL based algorithm is proposed to solve the joint optimization problem. Incorporating event-triggered learning into the DRL framework enables assessing whether to initiate the DRL process, thereby reducing the number of DRL process executions while maintaining reasonable reliability performance. The DRL framework consists of a twolayered structure. In the first layer, multiple deep Q-networks (DQNs) are established at the central trainer for block length optimization. The second layer involves an actor-critic network and utilizes the deep deterministic policy-gradient (DDPG)-based algorithm to optimize the power allocation. Simulation results demonstrate that the proposed event-triggered DRL scheme can achieve 95% of the performance of the joint optimization scheme while reducing the DRL executions by up to 24% for different network settings.