Multilayered Cylindrical Triboelectric Nanogenerator to Harvest Kinetic Energy of Tree Branches for Monitoring Environment Condition and Forest Fire

Forest fires present a great threat as they can rapidly grow and become large, resulting in tragic loss of life and property when occurring near occupied land. Here a self‐powered fire alarm system based on a novel multilayered cylindrical triboelectric nanogenerator (MC‐TENG) that can produce electrical power for the detection sensors by harvesting the kinetic energy of moving tree branches in a forest is presented. The major parameters for harvesting the kinetic energy using the proposed MC‐TENG are investigated, including the number of triboelectric layers, the frequency, the amplitude of external excitation, and the orientation between motion direction and device configuration. The fabricated MC‐TENG results in a peak power of 2.9 mW and a maximum average power of 1.2 mW at a low frequency of 1.25 Hz. The integrated self‐powered forest fire alarm system, consisting of fire sensors, a carbon‐based micro‐supercapacitor, and the MC‐TENG, is demonstrated to be able to report fire risk or hazard efficiently, accurately, and robustly. This study provides a new solution to reduce the forest fire risk through a portable and sustainable alarm system by effectively harvesting kinetic energies in natural environment with TENG technology.

[1]  Yunteng Cao,et al.  Robust and High-Performance Electrodes Via Crumpled Au-CNT Forests for Stretchable Supercapacitors , 2020, Matter.

[2]  Zhong Lin Wang.  On the first principle theory of nanogenerators from Maxwell's equations , 2020 .

[3]  Zhong Lin Wang,et al.  Matryoshka-inspired hierarchically structured triboelectric nanogenerators for wave energy harvesting , 2019 .

[4]  Zhong Lin Wang,et al.  High-performance triboelectric nanogenerators for self-powered, in-situ and real-time water quality mapping , 2019 .

[5]  Tao Jiang,et al.  Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics , 2019, Nature Communications.

[6]  Guoxu Liu,et al.  Self-powered intelligent buoy system by water wave energy for sustainable and autonomous wireless sensing and data transmission , 2019, Nano Energy.

[7]  Yunteng Cao,et al.  Highly Stretchable Supercapacitors via Crumpled Vertically Aligned Carbon Nanotube Forests , 2019, Advanced Energy Materials.

[8]  Wei Wang,et al.  Progress in Triboelectric Materials: Toward High Performance and Widespread Applications , 2019, Advanced Functional Materials.

[9]  Hyunok Cho,et al.  Natural wind-driven ultra-compact and highly efficient hybridized nanogenerator for self-sustained wireless environmental monitoring system , 2019, Nano Energy.

[10]  S. Qu,et al.  Recent Advances in Stretchable Supercapacitors Enabled by Low-Dimensional Nanomaterials. , 2018, Small.

[11]  Hengyu Guo,et al.  Triboelectric Nanogenerator: A Foundation of the Energy for the New Era , 2018, Advanced Energy Materials.

[12]  Bo Chen,et al.  Scavenging Wind Energy by Triboelectric Nanogenerators , 2018 .

[13]  Chi Zhang,et al.  An alginate film-based degradable triboelectric nanogenerator , 2018, RSC advances.

[14]  Zhong Lin Wang.  On Maxwell's displacement current for energy and sensors: the origin of nanogenerators , 2017 .

[15]  Wei Tang,et al.  Multilayered electret films based triboelectric nanogenerator , 2016, Nano Research.

[16]  Zhong Lin Wang,et al.  Progress in triboelectric nanogenerators as a new energy technology and self-powered sensors , 2015 .

[17]  Youmin Zhang,et al.  UAV-based forest fire detection and tracking using image processing techniques , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[18]  Simiao Niu,et al.  Theoretical systems of triboelectric nanogenerators , 2015 .

[19]  Youmin Zhang,et al.  A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques , 2015 .

[20]  Xuan Wu,et al.  An electromagnetic energy harvesting device based on high efficiency windmill structure for wireless forest fire monitoring application , 2014 .

[21]  Fengru Fan,et al.  Theoretical Comparison, Equivalent Transformation, and Conjunction Operations of Electromagnetic Induction Generator and Triboelectric Nanogenerator for Harvesting Mechanical Energy , 2014, Advanced materials.

[22]  Ahmad A. A. Alkhatib.  A Review on Forest Fire Detection Techniques , 2014, Int. J. Distributed Sens. Networks.

[23]  Zhong Lin Wang,et al.  Flexible triboelectric generator , 2012 .

[24]  Miguel Garcia,et al.  A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification , 2009, Sensors.

[25]  Patrick J. Reardon,et al.  Autonomous long-range open area fire detection and reporting , 2005, SPIE Defense + Commercial Sensing.

[26]  Tao Jiang,et al.  Spring-assisted triboelectric nanogenerator for efficiently harvesting water wave energy , 2017 .

[27]  Zhong Lin Wang.  Triboelectric nanogenerators as new energy technology and self-powered sensors - principles, problems and perspectives. , 2014, Faraday discussions.

[28]  Ingolf Willms,et al.  2012 International Symposium on Safety Science and Technology Early forest fire detection and verification using optical smoke, gas and microwave sensors , 2012 .

[29]  Dengyi Zhang,et al.  SVM based forest fire detection using static and dynamic features , 2011, Comput. Sci. Inf. Syst..