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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Mri-powered magnetic miniature capsule robot with hifu-controlled on-demand drug delivery(Institute of Electrical and Electronics Engineers Inc., 2023) Tiryaki, Mehmet Efe; Dogangun, Fatih; Dayan, Cem Balda; Wrede, Paul; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; College of Engineering; School of MedicineMagnetic resonance imaging (MRI)-guided robotic systems offer great potential for new minimally invasive medical tools, including MRI-powered miniature robots. By re-purposing the imaging hardware of an MRI scanner, the magnetic miniature robot could be navigated into the remote part of the patient's body without needing tethered endoscopic tools. However, state-of-art MRI-powered magnetic miniature robots have limited functionality besides navigation. Here, we propose an MRI-powered magnetic miniature capsule robot benefiting from acoustic streaming forces generated by MRI-guided high-intensity focus ultrasound (HIFU) for controlled drug release. Our design comprises a polymer capsule shell with a submillimeter-diameter drug-release hole that captures an air bubble functioning as a stopper. We use the HIFU pulse to initiate drug release by removing the air bubble once the capsule robot reaches the target location. By controlling acoustic pressure, we also regulate the drug release rate for multiple locations targeting during navigation. We demonstrated that the proposed magnetic capsule robot could travel at high speed, up to 1.13 cm/s in ex vivo porcine small intestine, and release drug to multiple target sites in a single operation, using a combination of MRI-powered actuation and HIFU-controlled release. The proposed MRI-guided microrobotic drug release system will greatly impact minimally invasive medical procedures by allowing on-demand targeted drug delivery.Publication Metadata only Effect of finger moisture on tactile perception of electroadhesion(Institute of Electrical and Electronics Engineers, 2024) Lefevre, Philippe; Martinsen, Orjan Grottem; Department of Mechanical Engineering; Aliabbasi, Easa; Muzammil, Muhammad; Şirin, Ömer; Başdoğan, Çağatay; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of EngineeringWe investigate the effect of finger moisture on the tactile perception of electroadhesion with 10 participants. Participants with moist fingers exhibited markedly higher threshold levels. Our electrical impedance measurements show a substantial reduction in impedance magnitude when sweat is present at the finger-touchscreen interface, indicating increased conductivity. Supporting this, our mechanical friction measurements show that the relative increase in electrostatic force due to electroadhesion is lower for a moist finger.Publication Metadata only ML-augmented bayesian optimization of pain induced by microneedles(Wiley, 2024) Department of Mechanical Engineering; Choukri, Abdullah Ahmed; Taşoğlu, Savaş; Department of Mechanical Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Sciences and Engineering; College of EngineeringMicroneedles (MNs) have emerged as a promising solution for drug delivery and extraction of body fluids. Pain is an important physiological attribute to be examined when designing MNs. There is no known representation of pain with geometric features of a MN despite the focus on experimental work. This study focuses on optimizing MN designs with the aim of minimizing pain through means of machine learning, finite element analysis, and optimization tools. Three distinct approaches are proposed. The first approach involves training multiple regression models on data obtained through finite element analysis in COMSOL. The second approach uses COMSOL's built-in nonlinear optimization solver. Finally, the third approach utilizes the LiveLink interface between COMSOL and MATLAB, combined with Bayesian optimization. Each approach presents unique strengths and challenges, with the third approach demonstrating significant promise due to its efficiency, practicality, and time-saving. A machine learning (ML)-augmented Bayesian framework is described in the article number by Ahmed Choukri Abdullah and Savas Tasoglu to optimize and minimize pain induced by microneedles. Introduction of ML-based optimization frameworks into microfabrication processes can pave the way for a much more effective and customized designs of minimally invasive microneedles.Publication Metadata only Single-step precision programming of decoupled multiresponsive soft millirobots(National Academy of Sciences, 2024) Zheng, Zhiqiang; Han, Jie; Shi, Qing; Demir, Sinan Özgün; Jiang, Weitao; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; College of EngineeringStimuli-responsive soft robots offer new capabilities for the fields of medical and rehabilitation robotics, artificial intelligence, and soft electronics. Precisely programming the shape morphing and decoupling the multiresponsiveness of such robots is crucial to enable them with ample degrees of freedom and multifunctionality, while ensuring high fabrication accuracy. However, current designs featuring coupled multiresponsiveness or intricate assembly processes face limitations in executing complex transformations and suffer from a lack of precision. Therefore, we propose a one-stepped strategy to program multistep shape-morphing soft millirobots (MSSMs) in response to decoupled environmental stimuli. Our approach involves employing a multilayered elastomer and laser scanning technology to selectively process the structure of MSSMs, achieving a minimum machining precision of 30 μm. The resulting MSSMs are capable of imitating the shape morphing of plants and hand gestures and resemble kirigami, pop-up, and bistable structures. The decoupled multistimuli responsiveness of the MSSMs allows them to conduct shape morphing during locomotion, perform logic circuit control, and remotely repair circuits in response to humidity, temperature, and magnetic field. This strategy presents a paradigm for the effective design and fabrication of untethered soft miniature robots with physical intelligence, advancing the decoupled multiresponsive materials through modular tailoring of robotic body structures and properties to suit specific applications.Publication Metadata only Versatile, modular, and customizable magnetic solid-droplet systems(National Academy of Sciences, 2024) Sun, Mengmeng; Wu, Yingdan; Zhang, Jianhua; Zhang, Hongchuan; Liu, Zemin; Li, Mingtong; Wang, Chunxiang; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; College of EngineeringMagnetic miniature robotic systems have attracted broad research interest because of their precise maneuverability in confined spaces and adaptability to diverse environments, holding significant promise for applications in both industrial infrastructures and biomedical fields. However, the predominant construction methodology involves the preprogramming of magnetic components into the system’s structure. While this approach allows for intricate shape transformations, it exhibits limited flexibility in terms of reconfiguration and presents challenges when adapting to diverse materials, combining, and decoupling multiple functionalities. Here, we propose a construction strategy that facilitates the on-demand assembly of magnetic components, integrating ferrofluid droplets with the system’s structural body. This approach enables the creation of complex solid-droplet robotic systems across a spectrum of length scales, ranging from 0.8 mm to 1.5 cm. It offers a diverse selection of materials and structural configurations, akin to assembling components like building blocks, thus allowing for the seamless integration of various functionalities. Moreover, it incorporates decoupling mechanisms to enable selective control over multiple functions, leveraging the fluidity, fission/fusion, and magneto-responsiveness properties inherent in the ferrofluid. Various solid-droplet systems have validated the feasibility of this strategy. This study advances the complexity and functionality achievable in small-scale magnetic robots, augmenting their potential for future biomedical and other applications. Copyright © 2024 the Author(s)Publication Metadata only Energetically autonomous soft robots: an embodied actuation strategy by liquid metal metabolism(Institute of Electrical and Electronics Engineers Inc., 2024) Liao, Jiahe; Bao, Xianqiang; Park, Minjo; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; School of Medicine; College of EngineeringThe level of energy autonomy in untethered robots is often physically limited by their onboard or remote energy supply, which often lacks the synergy and efficiency in living organisms.Embodied energy design emerges as a biologically inspired paradigm for energetically autonomous robots, where the energy source and actuator mechanisms are directly integrated into the materials and architecture for efficiency and functionality.In this work, we introduce an energy-embodied soft actuation strategy that is inspired by the metabolic processes in natural organisms.We present a self-powered, chemo-pneumatic actuation mechanism powered by a metabolic-like decomposition process of a gel-encapsulated liquid metal composite.We demonstrate the self-regulating locomotion capability of an energetically autonomous soft robotic crawler with this energy-actuation coupling. © 2024 IEEE.Publication Metadata only High-throughput vibrational testing of silicon nanowires(Institute of Electrical and Electronics Engineers Inc., 2024) Department of Mechanical Engineering; Zarepakzad, Sina; Ali, Basit; Muzammil, Muhammad; Alaca, Burhanettin Erdem; Department of Mechanical Engineering; Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); n2STAR-Koç University Nanofabrication and Nanocharacterization Center for Scientifc and Technological Advanced Research; Graduate School of Sciences and Engineering; College of EngineeringSilicon nanowires have emerged as essential components in nanoelectromechanical systems and nanoelectronics.Despite the associated challenges, investigation of their mechanical properties holds great significance due to their enormous potential in next-generation devices.Such challenges persist in the preparation and handling of samples, significantly impairing both throughput and reliability in experimentation.This paper introduces a comprehensive methodology integrating high-throughput resonance testing with co-fabrication techniques to enable simultaneous testing of multiple silicon nanowires under unique initial conditions.The proposed methodology aims to streamline testing processes while ensuring precise calibration and characterization of silicon nanowires.The study presents resonance testing conducted on multiple co-fabricated silicon nanowires, along with the quantification of intrinsic stresses through Raman characterization.Experimental results are compared with finite element modeling to analyze the vibration modes of the silicon nanowires under investigation.The developed methodology provides a foundational framework for scalable and reliable characterization of silicon nanowires, facilitating advancements in small-scale testing.In this context, this study paves the way into parallelization of incorporating intrinsic stresses into advanced nanomechanical modeling and highlights the importance of exploring multiscale theoretical frameworks for silicon nanowire mechanical characterization. © 2024 IEEE.Publication Metadata only Numerical simulation of milling operations on flexible composite parts(MATERIALS RESEARCH FORUM LLC, 2024) Nutte, Matthias; Riviere-Lorphevre, Edouard; Dambly, Valentin; Arrazola, Pedro-Jose; Ducobu, Francois; Department of Mechanical Engineering; Lazoğlu, İsmail; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); College of EngineeringFiber-reinforced polymers (FRPs) are a widely used and growing material in industry, thanks to their excellent mechanical properties. Manufactured FRPs parts usually have thin walls. These parts also require finishing operations such as edge trimming. Problems like those encountered when machining thin metal parts are also encountered with FRPs: form error, chatter vibrations and poor surface finish. However, the study and numerical modelling of thin FRP parts are not well developed up to now. The aim of this paper is to demonstrate the feasibility of adapting a numerical model for metals to FRPs. The modelling of the shape error during the thinning of a CFRP (Carbon Fiber Reinforced Polymers) part is studied in this paper using a quasi-static analysis. Compared to metals, two adaptations are introduced here for the FRPs. First, the material properties are adapted from isotropic to orthotropic. Secondly, a mechanical model was applied to calculate cutting forces for FRPs. The results of the study show the feasibility of this adaptation and examination of form error in the case of FRPs.Publication Metadata only Biodegradable sensor platforms(Elsevier, 2023) Department of Mechanical Engineering; Bathaei, Mohammad Javad; Singh, Rahul; İstif, Emin; Beker, Levent; Department of Mechanical Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; College of EngineeringRecent advances in materials chemistry and fabrication methods have led to the advent of an eco-friendly class of sensors utilizing biodegradable and sustainable materials to minimize electronic waste and hazardous by-products. Biodegradable sensors can be disintegrated and dissolved in the surrounding environment, remaining minimal trace after a prescribed functional lifetime due to their transient nature. This feature enables them to be widely used in numerous fields, ranging from medical diagnostics and point-of-care to environmental monitoring and food/agricultural analysis. The application of biodegradable materials has been extensively investigated in various sensor components, resulting in several unique features. In the first part, the overview of the recent developments in biodegradable sensor platforms will be introduced, followed by widely used biodegradable materials for building sensors. The recent progress and challenges in fabricating biodegradable sensors are reviewed in the second part. Finally, a range of applications enabled by the biodegradable sensors are represented. © 2023 Elsevier Ltd. All rights reserved.Publication Metadata only AI-based metamaterial design for wearables(Wiley, 2024) Department of Mechanical Engineering; Yığcı, Defne; Ahmadpour, Abdollah; Taşoğlu, Savaş; Department of Mechanical Engineering; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); School of Medicine; Graduate School of Sciences and Engineering; College of EngineeringContinuous monitoring of physiological parameters has remained an essential component of patient care. With an increased level of consciousness regarding personal health and wellbeing, the scope of physiological monitoring has extended beyond the hospital. From implanted rhythm devices to non-contact video monitoring for critically ill patients and at-home health monitors during Covid-19, many applications have enabled continuous health monitorization. Wearable health sensors have allowed chronic patients as well as seemingly healthy individuals to track a wide range of physiological and pharmacological parameters including movement, heart rate, blood glucose, and sleep patterns using smart watches or textiles, bracelets, and other accessories. The use of metamaterials in wearable sensor design has offered unique control over electromagnetic, mechanical, acoustic, optical, or thermal properties of matter, enabling the development of highly sensitive, user-friendly, and lightweight wearables. However, metamaterial design for wearables has relied heavily on manual design processes including human-intuition-based and bio-inspired design. Artificial intelligence (AI)-based metamaterial design can support faster exploration of design parameters, allow efficient analysis of large data-sets, and reduce reliance on manual interventions, facilitating the development of optimal metamaterials for wearable health sensors. Here, AI-based metamaterial design for wearable healthcare is reviewed. Current challenges and future directions are discussed. Artificial intelligence (AI)-based metamaterial design can support faster exploration of design parameters, allow efficient analysis of large data-sets, and reduce reliance on manual interventions, facilitating the development of optimal metamaterials for wearable health sensors. Here, AI-based metamaterial design for wearable healthcare is reviewed. Current challenges and future directions are discussed.