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Publication Metadata only Design and build of small-scale magnetic soft-bodied robots with multimodal locomotion(Nature Portfolio, 2023) Ren, Ziyu; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; College of Engineering; School of MedicineSmall-scale magnetic soft-bodied robots can be designed to operate based on different locomotion modes to navigate and function inside unstructured, confined and varying environments. These soft millirobots may be useful for medical applications where the robots are tasked with moving inside the human body. Here we cover the entire process of developing small-scale magnetic soft-bodied millirobots with multimodal locomotion capability, including robot design, material preparation, robot fabrication, locomotion control and locomotion optimization. We describe in detail the design, fabrication and control of a sheet-shaped soft millirobot with 12 different locomotion modes for traversing different terrains, an ephyra jellyfish-inspired soft millirobot that can manipulate objects in liquids through various swimming modes, a larval zebrafish-inspired soft millirobot that can adjust its body stiffness for efficient propulsion in different swimming speeds and a dual stimuli-responsive sheet-shaped soft millirobot that can switch its locomotion modes automatically by responding to changes in the environmental temperature. The procedure is aimed at users with basic expertise in soft robot development. The procedure requires from a few days to several weeks to complete, depending on the degree of characterization required. The protocol describes a sheet-shaped millirobot with 12 locomotion modes for traversing different terrains, a jellyfish-inspired millirobot for manipulating objects in liquids, a zebrafish-inspired millirobot for efficient swimming and a dual stimuli-responsive millirobot that can switch locomotion modes automatically by responding to the environmental temperature.Rigid-bodied robots lack deformation capabilities, limiting them to specific functions, whereas soft-bodied millibots display sophisticated locomotion strategies similar to those adopted by small-scale organisms. The detailed design and fabrication of small-scale magnetic soft-bodied robots with multimodal locomotion capability, including the processes required for locomotion control and optimization.Publication Metadata only Increasing the packing density of assays in paper-based microfluidic devices(Aip Publishing, 2021) Becher, Elaina; Ghaderinezhad, Fariba; Özkan, Mehmed; Yetişen, Ali Kemal; N/A; Department of Mechanical Engineering; N/A; Department of Media and Visual Arts; Dabbagh, Sajjad Rahmani; Taşoğlu, Savaş; Havlucu, Hayati; Özcan, Oğuzhan; N/A; Faculty Member; PhD Student; Faculty Member; Department of Mechanical Engineering; Department of Media and Visual Arts; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 291971; N/A; 12532Paper-based devices have a wide range of applications in point-of-care diagnostics, environmental analysis, and food monitoring. Paper-based devices can be deployed to resource-limited countries and remote settings in developed countries. Paper-based point-of-care devices can provide access to diagnostic assays without significant user training to perform the tests accurately and timely. The market penetration of paper-based assays requires decreased device fabrication costs, including larger packing density of assays (i.e., closely packed features) and minimization of assay reagents. In this review, we discuss fabrication methods that allow for increasing packing density and generating closely packed features in paper-based devices. To ensure that the paper-based device is low-cost, advanced fabrication methods have been developed for the mass production of closely packed assays. These emerging methods will enable minimizing the volume of required samples (e.g., liquid biopsies) and reagents in paper-based microfluidic devices.Publication Open 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; 291971High-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.Publication Metadata only Machine learning-enabled optimization of extrusion-based 3D printing(Academic Press Inc Elsevier Science, 2022) N/A; Department of Media and Visual Arts; Department of Mechanical Engineering; Dabbagh, Sajjad Rahmani; Özcan, Oğuzhan; Taşoğlu, Savaş; PhD Stud; ent; Faculty Member; Faculty Member; Department of Media and Visual Arts; Department of Mechanical Engineering; Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Sciences and Engineering; College of Social Sciences and Humanities; College of Engineering; N/A; 12532; 291971Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.Publication Metadata only MEMS biosensor for blood plasma viscosity measurements(Elsevier Science Bv, 2012) N/A; Department of Electrical and Electronics Engineering; Department of Mechanical Engineering; N/A; N/A; Department of Molecular Biology and Genetics; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Çakmak, Onur; Elbüken, Çağlar; Ermek, Erhan; Bulut, Selma; Kılınç, Yasin; Barış, İbrahim; Kavaklı, İbrahim Halil; Alaca, Burhanettin Erdem; Ürey, Hakan; PhD Student; Researcher; Other; PhD Student; PhD Student; Teaching Faculty; Faculty Member; Faculty Member; Faculty Member; Department of Molecular Biology and Genetics; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Sciences; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; N/A; 111629; 40319; 115108; 8579N/APublication Metadata only Microcantilever based disposable viscosity sensor for serum and blood plasma measurements(Academic Press Inc Elsevier Science, 2013) N/A; Department of Mechanical Engineering; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Çakmak, Onur; Elbüken, Çağlar; Ermek, Erhan; Mostafazadeh, Aref; Barış, İbrahim; Alaca, Burhanettin Erdem; Kavaklı, İbrahim Halil; Ürey, Hakan; PhD Student; Researcher; Faculty Member; Researcher; Teaching Faculty; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Sciences; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; 111629; 115108; 40319; 8579This paper proposes a novel method for measuring blood plasma and serum viscosity with a microcantilever-based MEMS sensor. MEMS cantilevers are made of electroplated nickel and actuated remotely with magnetic field using an electro-coil. Real-time monitoring of cantilever resonant frequency is performed remotely using diffraction gratings fabricated at the tip of the dynamic cantilevers. Only few nanometer cantilever deflection is sufficient due to interferometric sensitivity of the readout. The resonant frequency of the cantilever is tracked with a phase lock loop (PLL) control circuit. The viscosities of liquid samples are obtained through the measurement of the cantilever's frequency change with respect to a reference measurement taken within a liquid of known viscosity. We performed measurements with glycerol solutions at different temperatures and validated the repeatability of the system by comparing with a reference commercial viscometer. Experimental results are compared with the theoretical predictions based on Sader's theory and agreed reasonably well. Afterwards viscosities of different Fetal Bovine Serum and Bovine Serum Albumin mixtures are measured both at 23 degrees C and 37 degrees C, body temperature. Finally the viscosities of human blood plasma samples taken from healthy donors are measured. The proposed method is capable of measuring viscosities from 0.86 cP to 3.02 cP, which covers human blood plasma viscosity range, with a resolution better than 0.04 cP. The sample volume requirement is less than 150 mu l and can be reduced significantly with optimized cartridge design. Both the actuation and sensing are carried out remotely, which allows for disposable sensor cartridges. (C) 2013 Published by Elsevier Inc.Publication Open Access Tipping the scale from disorder to alpha-helix: folding of amphiphilic peptides in the presence of macroscopic and molecular interfaces(Public Library of Science, 2015) Globisch, Christoph; Peter, Christine; N/A; Department of Mechanical Engineering; Dalgıçdır, Cahit; Sayar, Mehmet; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 109820Secondary amphiphilicity is inherent to the secondary structural elements of proteins. By forming energetically favorable contacts with each other these amphiphilic building blocks give rise to the formation of a tertiary structure. Small proteins and peptides, on the other hand, are usually too short to form multiple structural elements and cannot stabilize them internally. Therefore, these molecules are often found to be structurally ambiguous up to the point of a large degree of intrinsic disorder in solution. Consequently, their conformational preference is particularly susceptible to environmental conditions such as pH, salts, or presence of interfaces. In this study we use molecular dynamics simulations to analyze the conformational behavior of two synthetic peptides, LKKLLKLLKKLLKL (LK) and EAA LAEALAEALAE (EALA), with built-in secondary amphiphilicity upon forming an alpha-helix. We use these model peptides to systematically study their aggregation and the influence of macroscopic and molecular interfaces on their conformational preferences. We show that the peptides are neither random coils in bulk water nor fully formed alpha helices, but adopt multiple conformations and secondary structure elements with short lifetimes. These provide a basis for conformation-selection and population-shift upon environmental changes. Differences in these peptides' response to macroscopic and molecular interfaces (presented by an aggregation partner) can be linked to their inherent alpha-helical tendencies in bulk water. We find that the peptides' aggregation behavior is also strongly affected by presence or absence of an interface, and rather subtly depends on their surface charge and hydrophobicity.