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Thermodynamic driving forces in contact electrification between polymeric materials

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  • Hang Zhang

    (Princeton University)

  • Sankaran Sundaresan

    (Princeton University)

  • Michael A. Webb

    (Princeton University)

Abstract

Contact electrification, or contact charging, refers to the process of static charge accumulation after rubbing, or even simple touching, of two materials. Despite its relevance in static electricity, various natural phenomena, and numerous technologies, contact charging remains poorly understood. For insulating materials, even the species of charge carrier may be unknown, and the direction of charge-transfer lacks firm molecular-level explanation. Here, we use all-atom molecular dynamics simulations to investigate whether thermodynamics can explain contact charging between insulating polymers. Based on prior work suggesting that water-ions, such as hydronium and hydroxide ions, are potential charge carriers, we predict preferred directions of charge-transfer between polymer surfaces according to the free energy of water-ions within water droplets on such surfaces. Broad agreement between our predictions and experimental triboelectric series indicate that thermodynamically driven ion-transfer likely influences contact charging of polymers. Furthermore, simulation analyses reveal how specific interactions of water and water-ions proximate to the polymer-water interface explain observed trends. This study establishes relevance of thermodynamic driving forces in contact charging of insulators with new evidence informed by molecular-level interactions. These insights have direct implications for future mechanistic studies and applications of contact charging involving polymeric materials.

Suggested Citation

  • Hang Zhang & Sankaran Sundaresan & Michael A. Webb, 2024. "Thermodynamic driving forces in contact electrification between polymeric materials," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46932-2
    DOI: 10.1038/s41467-024-46932-2
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    References listed on IDEAS

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    1. Yijia Lu & Han Tian & Jia Cheng & Fei Zhu & Bin Liu & Shanshan Wei & Linhong Ji & Zhong Lin Wang, 2022. "Decoding lip language using triboelectric sensors with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Haiyang Zou & Ying Zhang & Litong Guo & Peihong Wang & Xu He & Guozhang Dai & Haiwu Zheng & Chaoyu Chen & Aurelia Chi Wang & Cheng Xu & Zhong Lin Wang, 2019. "Quantifying the triboelectric series," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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