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Wildcats in Water: Development and Deployment of an on-site Test Strip for Lead Detection in Tap Water with Garret Dildine and Edric Choi

Wednesday, January 14, 2026 | 11:30 AM - 12:30 PM CT
O. T. Hogan Biological Sciences Building, Hogan 1-160, 2205 Tech Drive, Evanston, IL 60208 map it

This Wednesday we are excited to welcome Garrett Dildine (PhD Candidate, Environmental Engineering) and Edric Choi (PhD Candidate, Interdisciplinary Biological Sciences) to share game-changing synthetic biology research on lead detection in drinking water.


📅 Wednesday, January 14, 2026
⏰ 11:30 AM - 12:30 PM
📍 2205 Tech Drive, First Floor, Room 1-160
🍽️ Lunch provided


Garrett and Edric have been part of a multidisciplinary team that has developed an accurate, low-cost test strip that detects lead at 5 parts per billion—empowering non-experts to assess their tap water with a quick, at-home test and a simple photo. Their research uncovered that Chicago may be violating national drinking water standards, with lead levels significantly higher than Evanston's despite sharing the same Lake Michigan source. The difference? Infrastructure and treatment practices.


This presentation will illuminate how lead persists in our water systems and demonstrate how accessible technology empowers individuals to take matters into their own hands when it comes to drinking water safety. 


This is more than research—it's a practical tool for water safety that could protect communities everywhere. Everyone is welcome. See you there!

This is a great chance to learn about innovative research in wastewater and connect with peers across disciplines. We encourage you to forward this to your peers and students. Lunch will be provided.

Please fill out this interest form or Email us at: water@northwestern.edu

 

Cost: Free!

Audience

  • Faculty/Staff
  • Student
  • Post Docs/Docs
  • Graduate Students

Contact

Northwestern Center for Water  

water@northwestern.edu

Group

NU Water

Interest

  • Arts/Humanities
  • Social Events
  • Fitness/Sports
  • Global/Multicultural
  • Environment
  • Data Science & AI

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