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Computational analysis of starch for sustainable power generation towards integrated wearable IoT

Author

Listed:
  • Bincy, Thanjan Shaji
  • Prasanna, Asokan Poorani Sathya
  • Balaji, A. Sakthi
  • Sivasankar, K. Janani
  • Thiruvadigal, D. John
  • Anithkumar, Monunith
  • Kim, Sang-Jae

Abstract

Green energy has gained immense attention recently due to its low environmental impact. Developing triboelectric nanogenerators (TENG) with polysaccharide-based materials will pave the way for green self-powered sensor and wireless communication systems for different applications. Herein, different edible starch-based TENG was fabricated using arrowroot, corn, potato, and tapioca starch as the active layer. Density functional theory (DFT) provided information on the triboactive sites with the electron difference density (EDD) mapping. The results suggest amylopectin has a low work function and high chemical potential to exhibit high reactivity compared with amylose. The cornstarch-based nanogenerator (CS-TENG) delivered the maximum output performance. The green wearable IoT (Internet of Things) was constructed using a mechano-electric sensor with a wireless physio health monitoring system (WPHM) to track the different exercises. The developed Android application was used to calculate different exercises and calories burnt. Further, this wearable IoT can be used in sports fitness monitoring and sports person analytics.

Suggested Citation

  • Bincy, Thanjan Shaji & Prasanna, Asokan Poorani Sathya & Balaji, A. Sakthi & Sivasankar, K. Janani & Thiruvadigal, D. John & Anithkumar, Monunith & Kim, Sang-Jae, 2024. "Computational analysis of starch for sustainable power generation towards integrated wearable IoT," Applied Energy, Elsevier, vol. 370(C).
    • Handle: RePEc:eee:appene:v:370:y:2024:i:c:s0306261924009735
    DOI: 10.1016/j.apenergy.2024.123590
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