FEMS EUROMAT 2023
Poster
Energy-Harvesting, Self-Healing Sensor for Accurate Limb Motion and Respiratory Pattern Tracking
JK

Prof. Dr. Jong-Woong Kim

Sungkyunkwan University

Meena, J.S.¹; Khanh, T.D.¹; Kim, J.-W. (Speaker)¹
¹Sungkyunkwan University, Suwon (South Korea)

The rise in health issues related to sedentary behaviors and modern work environments necessitates the evolution of remote health monitoring systems. Triboelectric nanogenerators (TENGs) have shown great promise as innovative devices for detecting body movement and respiratory patterns, but significant hurdles remain. A successful TENG must be self-healing, air permeable, energy efficient, and made from optimal sensing materials that are flexible, lightweight, and exhibit superior triboelectric charging abilities. In our research, we explore the use of a self-healing polybutadiene-based urethane (PBU) as a positive triboelectric layer, and titanium carbide (Ti3C2Tx) MXene as a negative triboelectric layer for an energy-harvesting TENG. The PBU consists of maleimide and furfuryl components and contains hydrogen bonds that prompt the Diels-Alder reaction, leading to self-healing properties. It also contains various carbonyl and amine groups, creating dipole moments that enhance its triboelectric properties by facilitating electron transfer between the materials in contact [1].

Our TENG exhibits an impressive cyclic stability, producing a high, stable open-circuit voltage up to 30 V and a short-circuit current of 4 µA at a 4.0 Hz operation frequency. Notably, our TENG is self-healable, meaning it can restore its function and performance following damage. This unique feature ensures the device can maintain peak performance and continue to function effectively over multiple uses. When linked with a rectifier, our TENG can charge various capacitors and power 120 LEDs. Furthermore, we've used the TENG as a self-powered active motion sensor, affixed it to the human body for movement monitoring and energy harvesting. It also has the ability to identify breathing patterns in real-time, providing valuable insight into an individual's respiratory health.

References

[1] J.S. Meena, T.D. Khanh, S.-B. Jung, J.-W. Kim, ACS Appl. Mater. Interfaces, 2023, In-press, https://doi.org/10.1021/acsami.3c06060.

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