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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.

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RSS Water Research

  • Enhancing oil-water separation in hydrocyclones using superhydrophobic/superoleophilic fiber microspheres
  • Sequential quorum-sensing regulation mitigates the trade-off between methane production and biofouling in anaerobic membrane bioreactors
  • A highly porous chitosan – carboxymethyl cellulose bio-hydrogel derived from biomass sources for sustainable atmospheric water harvesting and clean water production
  • Unlocking the PirAB Pandora's box: Genomic epidemiology and phage biocontrol of an emerging waterborne one health crisis
  • Concentration-dependent micropollutant oxidation by DOM-derived long-lived reactive species: roles of pollutant structure and DOM composition
  • Chain elongation under low-temperature conditions: Performance and bacterial-fungal interactions
  • Reactive chlorine species drive simultaneous removal of antibiotic resistant bacteria and antibiotic resistance genes by UV-LED/chlorine
  • Making waves: From mandates to mechanisms: A transatlantic framework for wastewater antimicrobial resistance management
  • Synergy of biochar-supported iron single-atom self-motivated catalysis and biochar-induced adsorption enables efficient low-consumption water purification
  • The necessity of physics-informed neural networks for adsorption-based water treatment machine learning models: A case study of biochar adsorption
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