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E-WATER Lab @ Michigan State

Electrified WAstewater Treatment and Element Recovery

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Electrified WAstewater Treatment and Element Recovery (E-WATER) Lab

The E-WATER lab at Michigan State University develops affordable and reliable electrochemical solutions to help transform the resource-intensive wastewater management towards a resource-supplying hub. Our research synergistically integrates Applied Electrochemistry with Selective Separation and Process Engineering to (1) design energy-efficient engineering processes for multi-level resource recovery, (2) fundamentally understand rate-limiting step on the system level via thermodynamic and kinetic analysis, and (3) identify scaling-up challenges from energetic and techno-economic perspectives for better design of the treatment train. We welcome students and scholars from all over the world to join us!

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Research

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RSS Environ. Sci. Technol.

  • [ASAP] Decoding the Formation and Speciation of Emerging Halogenated Oxidation Byproducts by the UV/NHCl2 Process in the Absence and Presence of Bromide
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RSS Water Research

  • Nitrous oxide mitigation in biochar–pyrite-based bioretention systems via zonal microenvironment regulation
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  • Sequential natural rainfall erodes the efficacy of tillage practices on nitrogen loss: Insights from hydrological connectivity
  • The missing of iodide ions and formation of highly toxic iodinated intermediates in Bi/BiOI activated peroxydisulfate for the degradation of bisphenol A
  • In-situ chlorine-induced dynamic diatomic reconstruction for three electron transfer mineralization of high-salinity organic wastewater
  • Unveiling the transformation patterns and mechanisms of organic micropollutants and wastewater effluent organic matter during UV222-based oxidation systems
  • Prioritizing riparian scale landscape management is a spatially efficient strategy for controlling river water quality in China
  • Co-P-O bridges enabling spin-related interfacial charge regulation for synchronizing redox kinetics in dual-pollutant purification
  • A fluorescence index-informed δ¹⁸Oₚ-based Bayesian mixing framework for quantifying riverine phosphorus sources
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