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Get Free AccessFlowing electrode capacitive deionization systems (FE-CDI) have recently garnered attention because of their ability to prevent cross contamination and operate in uninterrupted cycles ad infinitum. Typically, FE-CDI electrodes suffer from low conductivity, reducing deionization performance. Utilization of higher mass loadings to combat this leads to poor rheological properties. Herein, Ti$_3$C$_2$T$_x$ MXene was introduced as 1 mg/mL slurry electrodes in an FE-CDI system for the removal and recovery of ammonia from simulated agricultural wastewater. The electrode performance was evaluated by operating the FE-CDI system with a feed solution of 500 mg/L NH$_4$Cl running in batch mode at a constant voltage of 1.20 V and -1.20 V in charging and discharging modes, respectively. Despite the low loading, Ti$_3$C$_2$T$_x$ flowing electrodes showed markedly improved performance, achieving 60% ion removal efficiency in a saturation time of 115 minutes with an adsorption capacity of 460 mg/g. To understand the high adsorption performance of the electrodes, physiochemical and structural analysis was done via a variety of characterization techniques such as SEM, TEM, XRD, DLS, and Raman Spectroscopy. Cyclic Voltammetry and Galvanostatic charge/discharge profiles were obtained to evaluate the electrochemical properties of the electrodes. The system proved to be an energy-saving technology by exhibiting a charge efficiency of 58-70% while operating at an energy consumption of 0.45 kWh/kg. A 92% regeneration efficiency showed that the electrodes were stable and suitable for long term and scalable usage. The results demonstrate that MXenes have the potential to improve the FE-CDI process for energy-efficient removal and recovery of ammonia.
Naqsh E. Mansoor, Luis A. Diaz, Christopher E. Shuck, Yury Gogotsi, Tedd E. Lister, David Estrada (2020). Removal and Recovery of Ammonia from Wastewater using Ti$_3$C$_2$T$_x$ MXenes in Flow Electrode Capacitive Deionization. , DOI: https://doi.org/10.48550/arxiv.2007.02853.
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Type
Preprint
Year
2020
Authors
6
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.48550/arxiv.2007.02853
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