Simple and Low-Cost Electroactive Membranes for Ammonia Recovery

Abstract

Ammonia is considered a contaminant to be removed from wastewater. However, ammonia is a valuable commodity chemical used as the primary feedstock for fertilizer manufacturing. Here we describe a simple and low-cost ammonia gas stripping membrane capable of recovering ammonia from wastewater. The material is composed of an electrically conducting porous carbon cloth coupled to a porous hydrophobic polypropylene support, that together form an electrically conductive membrane (ECM). When a cathodic potential is applied to the ECM surface, hydroxide ions are produced at the water-ECM interface, which transforms ammonium ions into higher-volatility ammonia that is stripped across the hydrophobic membrane material using an acid-stripping solution. The simple structure, low cost, and easy fabrication process make the ECM an attractive material for ammonia recovery from dilute aqueous streams, such as wastewater. When paired with an anode and immersed into a reactor containing synthetic wastewater (with an acid-stripping solution providing the driving force for ammonia transport), the ECM achieved an ammonia flux of 141.3 ± 14.0 g.cm–2.day–1 at a current density of 6.25 mA.cm–2 (69.2 ± 5.3 kg­(NH3–N)/kWh). It was found that the ammonia flux was sensitive to the current density and acid circulation rate.

Combining Multiple Remediation Techniques Is Effective for the Remediation of Eutrophic Flowing Water

“…However, the physical barrier may impede the penetration of oxygen from the water into the sediments, resulting in a shift from aerobic to anaerobic conditions and accelerating the release of NH 4 + -N from the sediments [45]. The use of CRB in this study also resulted in a transient increase in NH 4 + -N levels in the water, followed by a gradual decrease over time, possibly because the OH − in the water converted the NH 4 + -N into more volatile ammonia [46], which was released to the air under water flow conditions.…”

Computational Catalysis and Machine Learning Applications to Water Treatment Technologies

“…License: CC BY 4.0 complex and cannot be entirely modeled. Local environmental conditions such as pH, dissolved oxygen/hydrogen concentrations, and temperature at the electrode surface could deviate significantly from the bulk conditions at applied potentials [34,35], and it is challenging to measure these parameters and model them over extended time scales through computations. Nevertheless, historical successes and new developments in ML offer the possibility of continued extensions of what is possible in virtual catalyst design.…”