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Hierarchically conductive electrodes unlock stable and scalable CO2 electrolysis

Author

Listed:
  • Simon Rufer

    (Massachusetts Institute of Technology 77 Massachusetts Avenue)

  • Michael P. Nitzsche

    (Massachusetts Institute of Technology 77 Massachusetts Avenue)

  • Sanjay Garimella

    (Massachusetts Institute of Technology 77 Massachusetts Avenue)

  • Jack R. Lake

    (Massachusetts Institute of Technology 77 Massachusetts Avenue)

  • Kripa K. Varanasi

    (Massachusetts Institute of Technology 77 Massachusetts Avenue)

Abstract

Electrochemical CO2 reduction has emerged as a promising CO2 utilization technology, with Gas Diffusion Electrodes becoming the predominant architecture to maximize performance. Such electrodes must maintain robust hydrophobicity to prevent flooding, while also ensuring high conductivity to minimize ohmic losses. Intrinsic material tradeoffs have led to two main architectures: carbon paper is highly conductive but floods easily; while expanded Polytetrafluoroethylene is flooding resistant but non-conductive, limiting electrode sizes to just 5 cm2. Here we demonstrate a hierarchically conductive electrode architecture which overcomes these scaling limitations by employing inter-woven microscale conductors within a hydrophobic expanded Polytetrafluoroethylene membrane. We develop a model which captures the spatial variability in voltage and product distribution on electrodes due to ohmic losses and use it to rationally design the hierarchical architecture which can be applied independent of catalyst chemistry or morphology. We demonstrate C2+ Faradaic efficiencies of ~75% and reduce cell voltage by as much as 0.9 V for electrodes as large as 50 cm2 by employing our hierarchically conductive electrode architecture.

Suggested Citation

  • Simon Rufer & Michael P. Nitzsche & Sanjay Garimella & Jack R. Lake & Kripa K. Varanasi, 2024. "Hierarchically conductive electrodes unlock stable and scalable CO2 electrolysis," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53523-8
    DOI: 10.1038/s41467-024-53523-8
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    1. Miao Zhong & Kevin Tran & Yimeng Min & Chuanhao Wang & Ziyun Wang & Cao-Thang Dinh & Phil De Luna & Zongqian Yu & Armin Sedighian Rasouli & Peter Brodersen & Song Sun & Oleksandr Voznyy & Chih-Shan Ta, 2020. "Accelerated discovery of CO2 electrocatalysts using active machine learning," Nature, Nature, vol. 581(7807), pages 178-183, May.
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