• Qingshan Wei, Richie Nagi, Kayvon Sadeghi, Steve Feng, Eddie Yan, So Jung Ki, Romain Caire, Derek Tseng, and Aydogan Ozcan.
• Electrical Engineering Department, ‡Bioengineering Department, §California NanoSystems Institute (CNSI), ∥Department of Physics & Astronomy, and ⊥Department of Chemistry and Biochemistry, University of California, Los Angeles (UCLA) , Los Angeles, California 90095, United States.
• ACS Nano. 2014 Feb 25; 8 (2): 1121-9.

AbstractDetection of environmental contamination such as trace-level toxic heavy metal ions mostly relies on bulky and costly analytical instruments. However, a considerable global need exists for portable, rapid, specific, sensitive, and cost-effective detection techniques that can be used in resource-limited and field settings. Here we introduce a smart-phone-based hand-held platform that allows the quantification of mercury(II) ions in water samples with parts per billion (ppb) level of sensitivity. For this task, we created an integrated opto-mechanical attachment to the built-in camera module of a smart-phone to digitally quantify mercury concentration using a plasmonic gold nanoparticle (Au NP) and aptamer based colorimetric transmission assay that is implemented in disposable test tubes. With this smart-phone attachment that weighs <40 g, we quantified mercury(II) ion concentration in water samples by using a two-color ratiometric method employing light-emitting diodes (LEDs) at 523 and 625 nm, where a custom-developed smart application was utilized to process each acquired transmission image on the same phone to achieve a limit of detection of ∼ 3.5 ppb. Using this smart-phone-based detection platform, we generated a mercury contamination map by measuring water samples at over 50 locations in California (USA), taken from city tap water sources, rivers, lakes, and beaches. With its cost-effective design, field-portability, and wireless data connectivity, this sensitive and specific heavy metal detection platform running on cellphones could be rather useful for distributed sensing, tracking, and sharing of water contamination information as a function of both space and time.

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