Assessment of Triboelectric Nanogenerators for Electric Field Energy Harvesting


Abstract:

Electric-field energy harvesters (EFEHs) have emerged as a promising technology for harnessing the electric field surrounding energized environments. Current research indicates that EFEHs are closely associated with Tribo-Electric Nano-Generators (TENGs). However, the performance of TENGs in energized environments remains unclear. This work aims to evaluate the performance of TENGs in electric-field energy harvesting applications. For this purpose, TENGs of different sizes, operating in single-electrode mode were conceptualized, assembled, and experimentally tested. Each TENG was mounted on a 1.5 HP single-phase induction motor, operating at nominal parameters of 8 A, 230 V, and 50 Hz. In addition, the contact layer was mounted on a linear motor to control kinematic stimuli. The TENGs successfully induced electric fields and provided satisfactory performance to collect electrostatic charges in fairly variable electric fields. Experimental findings disclosed an approximate increase in energy collection ranging from 1.51% to 10.49% when utilizing TENGs compared to simple EFEHs. The observed correlation between power density and electric field highlights TENGs as a more efficient energy source in electrified environments compared to EFEHs, thereby contributing to the ongoing research objectives of the authors.

Año de publicación:

2024

Keywords:

  • Capacitance transducers
  • Electromagnetic coupling
  • Electromagnetic induction
  • energy harvesting
  • nanogenerators

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

  • Energía
  • Energía
  • Energía

Áreas temáticas de Dewey:

  • Física aplicada
  • Electricidad y electrónica
  • Ingeniería y operaciones afines
Procesado con IAProcesado con IA

Objetivos de Desarrollo Sostenible:

  • ODS 7: Energía asequible y no contaminante
  • ODS 12: Producción y consumo responsables
  • ODS 9: Industria, innovación e infraestructura
Procesado con IAProcesado con IA