https://scholar.dgist.ac.kr/handle/20.500.11750/155 Department of Robotics and Mechatronics Engineering, DGIST 2026-09-27T18:44:53Z https://scholar.dgist.ac.kr/handle/20.500.11750/60871 Title: Exploring the potential of multifunctional MWCNT/PDMS nanocomposites in thermal and mechanical energy harvesting Author(s): Talaniuk, Viktoriia; Mistewicz, Krystian; Gawron, Anna; Marcinkowski, Andrzej; Szeluga, Urszula; Myalska-Głowacka, Hanna; Kaja, Kushal Ruthvik; Hajra, Sugato; Kim, Hoe Joon; Godzierz, Marcin Abstract: In this work, it was shown that a nanocomposite of multi-walled carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS) is a versatile material that can convert both thermal and mechanical energy into electrical energy. The MWCNT/PDMS nanocomposite with a MWCNT concertation of 2 mass% was chemically etched to expose the carbon nanotubes on the surface of the nanocomposite. This resulted in a significant reduction in contact resistance, an increase in electrical conductivity, and consequently, an improvement in thermoelectric properties. For the first time, the MWCNT/PDMS nanocomposite was thoroughly analyzed to characterize its thermoelectric and triboelectric properties. Electrical conductivity, specific heat, thermal diffusivity, and thermal conductivity were investigated as a function of temperature in a wide range from 298 to 373 K. The optimized characteristics of the MWCNT/PDMS nanocomposite were achieved due to its relatively high electrical conductivity (22 S cm(-1)) and low thermal conductivity (0.22 W m(-1) K-1). The Seebeck coefficient and thermoelectric efficiency factor were found to increase with temperature, reaching the maximum values of 4.6 mu V K-1 and 7.4 & centerdot;10(-5), respectively. The MWCNT/PDMS nanocomposite was used as a negative friction layer in a triboelectric nanogenerator (TENG). This device operated in contact-disconection mode, generating an output voltage of 7 V and a current of 113 nA. During long-term testing, the TENG demonstrated exceptional stability and repeatability of its voltage response. It was shown that the MWCNT/PDMS-based TENG is suitable for harvesting mechanical energy from human body movements, such as finger tapping, foot tapping, and hammering. The developed MWCNT/PDMS nanocomposite shows great potential for use in flexible wearable sensors for self-powered temperature monitoring and motion detection. 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60870 Title: Self-poled and poling-free efficient piezoelectric nanogenerators for power generation and self-powered applications Author(s): Kumar, Naveen; Kaja, Kushal Ruthvik; Panda Swati; Hajra Sugato; Belal, Mohamed Ahmed; Bhosale, Premkumar Sharad; Khanapuram, Uday Kumar; Rajaboina, Rakesh Kumar; Keum, Hohyun; Lee, Kyoungtae; Kim, Hoe Joon Abstract: Piezoelectric nanogenerators (PENGs) have emerged as promising energy harvesters capable of converting waste mechanical energy into usable electrical power for self-powered electronic applications. Traditionally, PENGs require an external poling process to align ferroelectric dipoles and achieve efficient operation. However, reliance on high-voltage poling, time-consuming processing, and potential material degradation limits the scalability and practicality of conventional PENGs. Recent research has therefore focused on developing self-poled, polingfree PENGs that offer intrinsic polarization, enhanced stability, and simplified fabrication without external treatments. This review summarizes the fundamental working principles of PENGs and the effect of polarization on their performance. Then, an in-depth discussion of the concept and comparison between the self-poling and poling-free mechanisms is provided. Recent advances in self-poled or poling-free PENGs, material innovations, including polymers, ceramics, and composites, as well as device engineering strategies that enable efficient energy conversion without external poling, are also demonstrated. The review concludes with significant challenges, including material durability, large-scale fabrication, and integration into complex systems, as well as future research prospects for developing next-generation self-powered technology. By integrating current advances and highlighting key obstacles, the review attempts to offer valuable insights into the future development of efficient, scalable, and environmentally sustainable poling-free PENGs. To the best of our knowledge, this is the first comprehensive review of self-poled and poling-free PENGs, focusing on underlying mechanisms, material fabrication methods, and emerging applications. 2026-05-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60868 Title: AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms Author(s): Moon, Inkyu; Javidi, Bahram Abstract: Recent progress in artificial intelligence (AI) and digital holographic microscopy (DHM) has enabled quantitative, label-free, and noninvasive cellular imaging with unprecedented precision. This review provides an overview of AI-driven DHM technologies that transform classical holographic phase reconstruction and cellular analysis into real-time, portable biomedical solutions. After outlining the optical and computational fundamentals of DHM and quantitative phase imaging, we describe how deep generative and diffusion models substantially enhance phase retrieval accuracy under noisy or single-shot conditions. We then summarize recent biomedical applications, integrating blood, cancer, and cardiac cell analyses into a unified framework of AI-assisted quantitative phenotyping. Deep and self-supervised learning approaches are shown to enable high-accuracy classification of red blood cells and cancer cells and label-free evaluation of cardiomyocyte contractility and drug response. The combination of AI-based reconstruction, self-supervised learning, and physics-informed modeling demonstrates robust performance even with limited labeled data. Finally, we discuss the system-level transition toward low-cost, edge-AIenabled DHM platforms capable of real-time phase imaging in point-of-care or field environments. We highlight key challenges in data standardization, interpretability, and multimodal integration. Collectively, this review envisions AI-integrated DHM as a scalable, accessible technology bridging advanced quantitative imaging with practical biomedical diagnostics. 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60866 Title: Human-in-the-Loop Object Segmentation for 3D Gaussian Splatting via Finger-based VR Interface Author(s): Lee, Yongseok; Park, Hyunreal; Kim, Hyunsu; Ji, Harim; Yee, Dongho; Lee, Dongjun Abstract: 3D Gaussian Splatting has recently emerged as a powerful representation for photorealistic rendering and reconstruction of complex scenes. However, its practical applications in augmented/virtual reality, digital-twin, and robotics demand accurate and structurally consistent meaningful 3D segmentation, which remains a significant challenge. Existing 3D segmentation approaches, predominantly based on multiview 2D images, frequently rely on appearance-driven criteria, resulting in semantic misclassification-either incorrectly merging distinct object parts or excessively fragmenting coherent regions. Moreover, these methods significantly struggle with objects with multiple components and occluded scenes. To address these limitations, we propose an interactive human-in-the-loop segmentation framework that combines a fast optimization-based 3D segmentation algorithm with intuitive finger-based user interactions within a virtual reality environment. Our optimization-based segmentation module runs within a few seconds (tens of times faster than existing learning-based methods) providing users with real-time visual updates on current segmentation results, enabling them to refine outputs interactively by adjusting prompts and viewpoints in a human-in-the-loop manner. Our finger-based interface system allows precise 3D spatial prompting, enabling accurate and multiview consistent prompts, thereby overcoming the limitations of traditional 2D multiview prompts and segmentation. This combination significantly improves segmentation accuracy, semantic consistency, and robustness to occlusion and multipart structures, as demonstrated by experimental results showing fine-grained subpart segmentation in cluttered scenes. 2026-07-31T15:00:00Z