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Mixed Ionic/Electronic Conducting Surface Layers Adsorbed on Colloidal Silica for Flow Battery Applications
Published
Author(s)
Jeffrey J Richards, Austin D. Scherbarth, Norman J. Wagner, Paul Butler
Abstract
Slurry base electrodes have shown promise as an energy dense and scalable platform for the flowable electrodes used in electrochemical flow battery technology. Key to their efficient operation is the use of a conductive additive which allows for volumetric charging and discharging of the electrochemically active species contained within the electrodes. Carbon block is commonly used for this purpose due to the relatively low concentrations needed to maintain electrical percolation. While carbon black supplies the desirable electrical properties for the application, it contributes detrimentally to the rheology characteristics of these concentration suspensions. In this work, we develop a synthesis protocol to produce inorganic oxide particles with electrostatically adsorbed PEDOT:PSS. Using a combination of small angle neutron scattering (SANS), electron microscopy and thin-film conductivity, we show that the synthesis scheme provides a flexible platform to form conductive PEDOT:PSS-SiO2 nanoparticle dispersions. Based on these measurements, we demonstrate that these particles are stable when dispersed in propylene carbonate. Using a combination of rheology and dielectric spectroscopy, we show that these stable dispersions facilitate electrical percolation at concentrations below their mechanical percolation threshold and this percolation is maintained under flow. These results show that strategies which seek to decouple mechanical and electrical percolation to allow for the development of higher performance conductive additives for slurry based flow batteries hold promise.
, J.
, , A.
, , N.
and Butler, P.
(2016),
Mixed Ionic/Electronic Conducting Surface Layers Adsorbed on Colloidal Silica for Flow Battery Applications, ACS Applied Materials and Interfaces, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=921055
(Accessed October 6, 2025)