journal article·2017-07

Synthesis and characterization of Torlon-based polyion complex for direct methanol and polymer electrolyte membrane fuel cells

Harsha Nagar, V. V. Basava Rao, S. Sridhar

Journal of Materials Science· Volume 52· Issue 13
Publisher page

Abstract

An acid–base polyion complex membrane was synthesized by blending a polyamide-imide (Torlon) with sulfonated polyethersulfone (SPES), to make a sturdy proton-conducting electrolyte for portable fuel cells. The membrane was characterized by FTIR, XRD, TGA, SEM and UTM techniques. Chemical interactions between Torlon and SPES enabled the formation of ionic clusters that are useful for facilitating proton conduction and water sorption with prevention of fuel bypass. The complex exhibited high mechanical, thermal and oxidative stability with low methanol permeability (9.46 × 10−8 cm2/s). Sorption experiments showed the membrane to possess moderate affinity toward water with preferably low methanol uptake. Moreover, the blend membrane exhibited high proton conductivity (0.13 S/cm) at low humidity and high temperature conditions. To investigate the microstructure and transport behavior of the reported blend, molecular dynamics simulation based on COMPASS force field was performed. The diffusivity of hydronium ion obtained through simulation was used to calculate conductivity by Einstein equation. Proton conductivity obtained by simulation was validated by comparison with experimental data. Furthermore, the interaction of membrane functional groups with water and hydronium ion carrier was evaluated by radial distribution function. The study revealed the proposed complex to be an attractive, inexpensive alternative to state-of-the-art Nafion 117 membrane for fuel cell application.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Fuel Cells and Related Materials · Advanced Battery Technologies Research · Membrane-based Ion Separation Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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