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A Microfluidic Multispectral Diffraction Platform for Quantitative Analysis of Atmospheric Black Carbon Aerosols
Summary
Scientists have developed a small, affordable device that can accurately measure black carbon, a sooty air pollutant from car exhaust and burning fuels that's linked to heart and lung problems, even when it's mixed with other tiny particles like microplastics or mold spores. Using a special imaging technique that shines different colors of light on air samples, the tool can tell these pollutants apart and measure black carbon levels with high accuracy. Because it's compact and cheap compared to current bulky lab equipment, this technology could eventually enable widespread, real-time air quality monitoring in neighborhoods and cities, helping people better understand and
Abstract Atmospheric black carbon aerosol (BC) is a major environmental pollutant that contributes to climate warming and threatens respiratory and cardiovascular health, making its accurate quantification essential for coordinated pollution control. However, BC is mainly submicron in size and commonly mixed with multicomponent impurities, leading to complex optical coupling effects that hinder precise quantification. In addition, conventional instruments are often expensive and bulky, limiting their suitability for rapid on-site detection and high-density deployment. Here, we present a precise BC quantification system that integrates a microfluidic chip with multispectral diffraction imaging. First, a microfluidic chip integrating inertial separation, low-velocity sheath-flow regulation, and high-voltage electrostatic capture was used to remove interfering particles and uniformly enrich submicron BC particles within the imaging region. Subsequently, a lensless diffraction imaging system was developed to acquire hyperspectral diffraction fingerprints of aerosol particles over the wavelength range of 400–800 nm. These fingerprints not only enabled BC to be discriminated from similarly sized interfering particles, including microplastics and pathogenic spores, but also provided discriminative multispectral diffraction features for quantitative analysis. Finally, the selected spatial–spectral features were input into the BC-GCM model to establish a mapping between the multispectral diffraction responses and BC concentration. The system achieved a maximum enrichment efficiency of 92%. Over a concentration range of 0.01–0.20 mg/m3, the model yielded an R2 of 0.98 and an average test-set recognition accuracy of 84%. Owing to its compact structure and low cost, this platform shows strong potential for rapid on-site response and high-density BC monitoring in complex atmospheric environments.