MATERIALS · STRUCTURES · ELECTRIC FIELDS

Research

소재·구조와 전기장을 설계해 생체환경에서 신호와 물질의 거동을 이해하고 제어합니다.

소재의 조성과 구조, 전기적 조건이 생체와의 접촉, 신호 전달, 유체·입자·분자의 움직임에 미치는 영향을 탐구합니다. 이를 바탕으로 생체환경에서 신호를 측정하고 자극을 전달하며, 물질의 이동과 흐름을 조절하는 기술을 개발합니다.

소프트 전극, 생체신호 측정·자극, 땀 관리, 전기장 기반 물질 조작은 이러한 공통 질문을 구체적인 환경에서 탐구하는 연구입니다.

We design materials, structures, and electric fields to understand and control signals and matter in biological environments. Our research connects interfacial contact and electrical response with the transport of fluids, particles, and biomolecules.

연구과제 · Research Programs

다음 과제들은 이러한 상호작용을 생체·공학적 환경에 적용하는 연구입니다.

Development of AI-Based respiratory-synchronized wearable and implantable electronic diaphragm system

Funding Period | Apr. 2025 – Feb. 2027

In collaboration with Inha Univ. & Chung-Ang Univ. College of Medicine

Ultrathin Soft Bioelectronics
Development of tissue-conformal, highly conductive ultrathin electrodes for stable multichannel electrical stimulation of the diaphragm.

Minimally & Non-Invasive Interfaces
Design of minimally invasive and non-invasive soft electronic systems that provide efficient diaphragm stimulation while minimizing mechanical and biological burden.

AI-Driven Personalized Therapy
Real-time physiological signal analysis and adaptive stimulation control for personalized diaphragm rehabilitation and spontaneous breathing recovery.

Adaptive Sweat Control-Based Hybrid Multimodal Platform for Muscle Function Sensing and Augmentation in Space Environments

Funding Period | Mar. 2026 – Feb. 2031

Independent Project

Adaptive Sweat Management
Control of sweat accumulation at the skin–electrode interface through passive nanostructured wicking and electrowetting–dewetting strategies.

Multimodal Muscle Sensing
Simultaneous acquisition of electrophysiological signals and sweat-derived biomarkers using multifunctional wearable bioelectronic interfaces.

AI-Enabled Closed-Loop Stimulation
Real-time assessment of muscle function from multimodal biosignals and personalized closed-loop stimulation for maintaining and enhancing muscle performance, including under microgravity conditions.

Electrokinetic Biomolecular Manipulation for Biofluid-Based Diagnostics

Project Period | Oct. 2025 – Feb. 2027

In collaboration with Sangji Univ.

Electric-Field-Driven Manipulation
Application of electrophoresis (EP) and dielectrophoresis (DEP) for controlled transport and manipulation of biomolecules.

Filtration & Concentration
Selective filtration, separation, and enrichment of low-abundance biomarkers to improve downstream analytical performance.

Integrated Biosensing
Integration of electrokinetic biomolecular handling with multiplexed biosensors to enhance detection sensitivity and support early diagnosis of neurodegenerative diseases.

Multimodal Monitoring of Parkinson’s Disease

Independent Project — Open for Collaboration

Multimodal Gait Analysis
Quantitative assessment of gait and motor function using electromyography (EMG) and motion-derived signals.

Blood-Based Biomarker Profiling
Analysis of circulating biomarkers associated with disease status and motor dysfunction in Parkinson’s disease.

Motor–Biomarker Correlation
Integration of digital motor phenotypes with blood-based biomarkers to identify relationships between physiological changes and disease progression for longitudinal patient monitoring.

Hair-Adaptive Bioelectronic Interfaces for Neural Monitoring

In collaboration with SKKU

Hair-adaptive scalp bioelectronic technologies are developed to enable stable, high-fidelity neural signal acquisition through hair-covered scalp using ultrathin stretchable electrodes and morphing substrates. Integrated with AI-based biosignal coupling, the platform supports real-time monitoring and prediction of severe neurological conditions.

Hair-Adaptive Scalp Interface
Development of ultrathin stretchable electrodes integrated with morphing substrates to penetrate hair-covered regions and establish conformal contact with the scalp.

High-Fidelity Neural Sensing
Stable, long-term acquisition of EEG signals with reduced contact impedance and improved signal reliability in complex scalp environments.

AI-Enabled Neurological Monitoring
Integration of EEG with physiological signals through AI-based coupling models to enable real-time detection and prognosis prediction of severe neurological disorders.

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