Portable detection of trace metals in airborne particulates and sediments via μ PADs and smartphone.

Particulate matter (PM), a key indicator of air pollution by natural and anthropogenic activities, contributes to a wide spectrum of diseases that lead to a shortening of life expectancy. It has been recognized that trace metals in airborne PM are highly toxic and can be correlated with lesion in respiratory, gastrointestinal, immunological, and hematological systems. Traditional methods for trace metal assay require sophisticated instrumentations and highly trained operators in centralized laboratories. In this work, by integrating the technologies of microfluidic paper-based analytical devices, additive manufacturing, smartphone, and colorimetric sensing, we developed the first smartphone based paper microfluidic platform for portable, disposable, and quantitative measurements of cobalt (Co), copper (Cu), and iron (Fe) in ambient air and street sediments. On a single A4-sized paper, 48 devices were fabricated in under 30 s with a total cost of ∼$1.9. On each device, 12 reaction units were patterned and used for colorimetric tests. Particulate samples from urban ambient air and street sediments were collected, processed, and analyzed. Signals of the on-chip complexation product were recorded using a smartphone camera and processed by a self-developed app on an iOS system. For precisely controlling the object distance, chip position, and luminance, a hand-held 3D cellphone housing was designed and printed. The detection limits of Co, Cu, and Fe were determined to be 8.2, 45.8, and 186.0 ng, while the linear dynamic ranges were calculated to be 8.2-81.6, 45.8-4.58 × 102 , and 1.86 × 102 -1.86 × 103  ng, representing a practically relevant device performance with a significant reduction in the detection cost and time consumption. Trace metals in ambient air and sediments of two cities in China have been quantified portably, thus demonstrating the utility of our system in improving strategies for air pollution control in low-resource settings.