Sang Won Lee

Dr. Sang Won Lee

orcid

Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2018 in Sensors and Actuators B: Chemical
Verified
Journal Article
Closed access
SLSang Won Lee
Sang Won Lee
WLWonseok Lee
Wonseok Lee
YHYoochan Hong
Yoochan Hong
Gyudo LeeGyudo Lee
Gyudo Lee
DYDae Sung Yoon
Dae Sung Yoon

14 February 2025 in Science Advances
Verified
Journal Article
Open access
YLYongju Lee
Yongju Lee
XTXinyu Tian
Xinyu Tian
JPJaewon Park
Jaewon Park
DNDong Hyun Nam
Dong Hyun Nam
ZWZhuohong Wu
Zhuohong Wu
Yangzhi ZhuYangzhi Zhu
Yangzhi Zhu
Emerging electronic skins (E-Skins) offer continuous, real-time electrophysiological monitoring. However, daily mechanical scratches compromise their functionality, underscoring urgent need for self-healing E-Skins resistant to mechanical damage. Current materials have slow recovery times, impeding reliable signal measurement. The inability to heal within 1 minute is a major barrier to commercialization. A composition achieving 80% recovery within 1 minute has not yet been reported. Here, we present a rapidly self-healing E-Skin tailored for real-time monitoring of physical and physiological bioinformation. The E-Skin recovers more than 80% of its functionality within 10 seconds after physical damage, without the need of external stimuli. It consistently maintains reliable biometric assessment, even in extreme environments such as underwater or at various temperatures. Demonstrating its potential for efficient health assessment, the E-Skin achieves an accuracy exceeding 95%, excelling in wearable muscle strength analytics and on-site AI-driven fatigue identification. This study accelerates the advancement of E-Skin through rapid self-healing capabilities.

30 November 2020 in ACS Sensors
Verified
Journal Article
Closed access
SLSang Won Lee
Sang Won Lee
WLWonseok Lee
Wonseok Lee
IKInsu Kim
Insu Kim
DLDongtak Lee
Dongtak Lee
DPDongsung Park
Dongsung Park
DYDae Sung Yoon
Dae Sung Yoon

11 September 2020 in ACS Applied Materials & Interfaces
Verified
Journal Article
Closed access
SLSang Won Lee
Sang Won Lee
HJHyo Gi Jung
Hyo Gi Jung
IKInsu Kim
Insu Kim
DLDongtak Lee
Dongtak Lee
WKWoong Kim
Woong Kim
DYDae Sung Yoon
Dae Sung Yoon

29 January 2021 in ACS Nano
Verified
Journal Article
Closed access
IKInsu Kim
Insu Kim
DLDongtak Lee
Dongtak Lee
SLSang Won Lee
Sang Won Lee
JLJeong Hoon Lee
Jeong Hoon Lee
Gyudo LeeGyudo Lee
Gyudo Lee
DYDae Sung Yoon
Dae Sung Yoon

2 August 2023 in IEEE Journal on Flexible Electronics
Verified
Journal Article
Closed access
SLSang Won Lee
Sang Won Lee
XPXiaochang Pei
Xiaochang Pei
JRJerome Rajendran
Jerome Rajendran
RERahim Esfandyarpour
Rahim Esfandyarpour
Wearable devices have received tremendous interest in healthcare monitoring due to their significant advantages in terms of flexibility, non-invasiveness, wireless capability, and portability. However, they still face challenges including the need for external power sources, bulky measuring instruments, and a lack of a wireless data transport system. Herein, we propose a PEDOT:PSS/MXene pressure sensing platform based on 2D e-textile and screen-printed interdigitated electrodes. Both materials improve the electrical conductivity, sensitivity, stability, flexibility, and mechanical durability of the pressure sensor. A near-field communication (NFC) readout system provides real-time wireless monitoring by transmitting signals from the sensor to a smartphone app, without external batteries. The pressure sensor has sensitivity, a wide detection range, excellent repeatability and stability, fast response/recovery time, and long-term durability. Long-term usability was verified through the bending test up to 21,400 cycles. The flexible pressure sensor successfully monitored wrist pulse, physical movements, and various muscle movements in the body. Repeatable, stable, reliable, and regular signals were obtained wirelessly through the NFC readout system. Our sensing platform could be a promising solution for healthcare monitoring, rehabilitation therapy, and early medical diagnosis. Additionally, it suggests a new platform for helping people with disabilities to communicate with others through human-machine interaction.