https://scholar.dgist.ac.kr/handle/20.500.11750/155 Department of Robotics and Mechatronics Engineering, DGIST 2026-06-14T12:43:01Z https://scholar.dgist.ac.kr/handle/20.500.11750/60416 Title: Structurally engineered ultrasoft PEDOT:PSS fiber microelectrodes with enhanced electrochemical performance for neural interfaces Author(s): Won, Chihyeong; Cho, Young Uk; Kweon, Siyeon; Cho, Sungjoon; Kwon, Chaebeen; Kim, Hyun Woo; Lee, Ju Young; Park, Sang Hoon; Han, Sorim; Kim, Yang Tae; Jang, Jumyoung; Jekal, Janghwan; Kim, Jae Geun; Jang, Kyung-In; Xu, Sheng; Gao, Wei; Cho, Il-Joo; Yu, Ki Jun; Lee, Taeyoon Abstract: Stable and reliable neural interfacing is essential for the diagnosis and treatment of chronic neurological disorders. Flexible neural probes are particularly important for this purpose, as they minimize tissue damage and inflammatory responses while maintaining stable electrode-tissue coupling; however, achieving both high electrical performance and tissue-like mechanics remains challenging. Here, we present a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) fiber microelectrode (PFME), an all-organic neural probe capable of recording single-neuron activities with potential for long-term interfacing. The PFME is entirely composed of organic components and fabricated without thermal processing. In addition, the posttreatment process enables to selectively remove PSS binder networks while promoting PEDOT chain alignment to optimize mechanical compliance and electrochemical performance. In vivo, the PFME enables stable single-unit recordings from the mouse hippocampus. Histological analysis after 1 week of implantation reveals minimal glial activation comparable to that elicited by a conventional probe. This structurally engineered PFME establishes a pathway to achieve minimally invasive neural interfacing platforms for chronic applications. 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60413 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-AI-enabled 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. (c) 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60377 Title: 유연관절 로봇의 제어시스템 Author(s): 이덕진; 오세훈 https://scholar.dgist.ac.kr/handle/20.500.11750/60361 Title: Transformation of rusted iron into an IDE-based sensor for ethanol detection and self-powered humidity sensing Author(s): Belal, Mohamed Ahmed; Hajra, Sugato; Bayoumy, Ahmed M.; Eldesouki, Mohammed H.; Kaja, Kushal Ruthvik; Panda, Swati; Ramu, Dandugudumula; Abd El-moneim, Ahmed; Achary, P. G. R.; Kim, Hoe Joon Abstract: Volatile organic compound (VOC) sensors and triboelectric nanogenerators (TENGs) are highly significant applications with broad potential across multiple fields, including non-invasive disease biomarker monitoring and sustainable energy harvesting for electronic devices. This study reports the synthesis of alpha-Fe2O3 nanoparticles derived from recycled iron screws using a closed-system nitric acid leaching process, followed by calcination, offering low-cost, eco-friendly, and added-value products that reduce the negative environmental impacts of waste materials. The synthesized material is thoroughly characterized to investigate its phase purity, surface morphology, and suitability for TENG and ethanolsensing applications. A spray coating technique was employed to deposit the alpha-Fe2O3 ink onto laserinduced graphene interdigitated electrodes (LIG-IDE) fabricated via CO2 laser engraving of a polyimide flexible substrate. The fabricated alpha-Fe2O3-based sensor exhibits multifunctional capabilities, owing to the material's biocompatibility. The alpha-Fe2O3-based sensor exhibits a high performance for ethanol detection at room temperature, with a sensor response of 47 and response/recovery times of 104/126 s, respectively, at 100 ppm. The TENG device exhibits stable output characteristics of 3 V and a maximum power of 9.5 nW. The electrical output from biomechanical motions confirms its potential for energy harvesting applications, and a further self-powered humidity sensor was demonstrated. These results highlight the excellent potential of alpha-Fe2O3 for both TENG applications and VOCs detection, recommending its use in environmental and industrial monitoring. 2026-06-30T15:00:00Z