Abstract
The design of low-temperature fuel cells and electrolyzers involving proton exchange membranes (PEMs) has been dominated by the exceptional performance and industrial dominance of Nafion{\texttrademark}. With emerging regulations limiting the use of fluorinated compounds, the search for F-free alternatives must be accelerated. Here, we optimize a fluorine-free liquid crystalline poly(epichlorohydrin) membrane, based on the identification of key descriptors for proton mobility obtained using machine learning potentials derived from first-principles calculations for extensive molecular dynamics. Proton transfer is controlled by the interplay between water confinement and internal pore chemistry. This allows us to modify the active functional groups so that they directly participate in the proton diffusion process, increasing three times the diffusion rate in the membrane thus being close to Nafion standards. This work provides key descriptors for a targeted design approach essential for catalyzing the commercial development of next-generation fluorine-free PEMs.
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Contains experimental and computational details, and supplementary figures and tables discussed in the main text.
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ioChem-BD repository for DFT data
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DFT computational data in the ioChem-BD repository can be accessed by following the above link.
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