Issue 2, 2023

Effect of polyacrylamide morphology templated using lyotropic liquid crystal on the proton conductivity of acid hydrogels

Abstract

Acid hydrogels comprising polymer networks are promising soft electrolytes whose proton conductivities are most often regulated by acid content. Herein, promotion of conductivity by solely regulating polymer morphology has been demonstrated for acid hydrogels with identical acid content. Polymerization of acrylamide at different temperatures in the same aqueous solution, which is a lyotropic liquid crystal (LLC) of 4-(1-ethyldecyl)benzenesulfonic acid (EDBSA) exhibiting a phase transition at 30 °C, affords acid hydrogels comprising ordered and random polymer networks. The ordered polymer network templated by the lamellar liquid crystal at 15 °C possesses more interconnected and extended pores than that obtained in the isotropic solution at 45 °C. Electrochemical characterization shows that the ordered network affords 48% higher proton conductivity than the random network for hydrogels holding the EDBSA LLC. This higher conductivity is ascribed to more numerous long-range transport pathways formed in larger pores and fewer barriers in the network for protons to pass through. Enhanced conductivities are also obtained from the ordered polymer network for hydrogels comprising micellar EDBSA solution and H2SO4 solution, albeit to lesser degrees. These results shed light on the dependence of electrochemical performance on the polymer morphology of hydrogels and offer a strategy to enhance the conductivity of hydrogels without changing their polymer fraction.

Graphical abstract: Effect of polyacrylamide morphology templated using lyotropic liquid crystal on the proton conductivity of acid hydrogels

Supplementary files

Article information

Article type
Paper
Submitted
22 Sep 2022
Accepted
23 Nov 2022
First published
24 Nov 2022

Soft Matter, 2023,19, 268-275

Effect of polyacrylamide morphology templated using lyotropic liquid crystal on the proton conductivity of acid hydrogels

J. Chen, J. Luo, S. Tan, C. Wang and Y. Wu, Soft Matter, 2023, 19, 268 DOI: 10.1039/D2SM01282K

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