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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!

About Us

If you want to know more about the E-WATER Lab, please click here!

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Research

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RSS Environ. Sci. Technol.

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RSS Water Research

  • Photoaging dynamics of tire wear particles in the terrestrial-aquatic interface: The crucial role of soil solution chemistry and dissolved organic matter evolution
  • Molecular-level transformation of algal organic matter during water treatment processes by FT-ICR MS integrated with reactomics and interpretable machine learning
  • Unexpected roles of aromatic ring-cleaving dioxygenases in oxygen-heterocycle degradation: Functional diversity, gene redundancy, and ecological implications
  • Exogenous dissolved organic matter redirects humic carbon fate at mineral interfaces through pre-contact molecular conditioning
  • Robust HPLC-MS/MS quantification of N-acyl homoserine lactones (AHLs) in biomass-associated solid matrices reveals their aqueous–solid distribution in wastewater treatment systems
  • Oxidation of biogenic U(IV) mediated by iron-bearing clay minerals, iron-reducing bacteria, and organic ligands
  • High-risk contaminants detected in wastewater effluent samples can be prioritized prior to structural assignment using machine learning tools
  • Exploring the reductive transformation pathways of perfluoroalkyl Sulfonylimides to legacy perfluoroalkyl sulfonates and perfluoroalkyl sulfonamide compounds
  • Process architecture governs nitrate fate by controlling dissimilatory nitrate reduction to ammonium (DNRA)-denitrification competition in industrial wastewater systems
  • Reactive organic matter destabilizes Fe-mediated phosphorus retention through Fe–S–P decoupling in lake sediments
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