Researcher: Ahmadpour, Abdollah
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Ahmadpour, Abdollah
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Publication Metadata only Deep learning-augmented T-junction droplet generation(Elsevier Inc., 2024) N/A; Department of Mechanical Engineering; Department of Mechanical Engineering; Ahmadpour, Abdollah; Shojaeian, Mostafa; Taşoğlu, Savaş; 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 Open Access AI-based metamaterial design for wearables(Wiley, 2024) Department of Mechanical Engineering; Department of Mechanical Engineering; Yığcı, Defne; Ahmadpour, Abdollah; Taşoğlu, Savaş; 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.Publication Open Access AI-based metamaterial design(American Chemical Society, 2024) Department of Mechanical Engineering; Department of Mechanical Engineering; Tezsezen, Ece; Yığcı, Defne; Ahmadpour, Abdollah; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; School of Medicine; College of EngineeringThe use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.