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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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RSS Environ. Sci. Technol.

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RSS Water Research

  • Spectral–temporal autofluorescence analysis for rapid and label-free microplastic differentiation in aquatic environments
  • A hybrid nature-based ecological restoration system stabilizes water quality and food-web recovery in a continuous nutrient-loaded pond
  • The overlooked hydroxylation of NOM contributes to the additional generation of ·OH in H2O2/Fe(III)/NOM system
  • Ecological commissioning links microbial succession to treatment stabilization during managed aquifer recharge
  • A high-rate anaerobic/oxic membrane bioreactor for phosphorus removal and carbon capture from municipal wastewater: Operational limits and energy-recovery potential via sidestream anaerobic digestion
  • Impact of organic carbon on biogas valorization consortium: shift of intracellular storage and microbial succession
  • Genomic convergence and geographical divergence: multi-scale salinity adaptation mechanisms of microbiomes in shale gas flowback and produced water
  • Iron-associated functional differentiation between granules and flocs enhances resilience and phosphorus recovery in a CEPT–PDA treating low-strength wastewater
  • Physics-informed temporal convolutional network with auxiliary process-state trajectories for effluent quality prediction
  • MOF-derived CoO@C electrocatalyst for atomic hydrogen-mediated reductive decomplexation of refractory Cu-complexes toward resource valorization
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