Air quality monitoring using mobile microscopy and machine learning

Rapid,accurate and high-throughput sizing and quantification of particulate matter(PM)in air is crucial for monitoring and improving air quality.In fact,particles in air with a diameter of≤2.5μm have been classified as carcinogenic by the World Health Organization.Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning.This platform,termed c-Air,is also integrated with a smartphone application for device control and display of results.This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air.It provides statistics of the particle size and density distribution with a sizing accuracy of~93%.We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times,and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring,which showed strong correlation to c-Air measurements.Furthermore,we used c-Air to map the air quality around Los Angeles International Airport(LAX)over 24 h to confirm that the impact of LAX on increased PM concentration was present even at 47 km away from the airport,especially along the direction of landing flights.With its machinelearning-based computational microscopy interface,c-Air can be adaptively tailored to detect specific particles in air,for example,various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.

Pixel super-resolution using wavelength scanning

Undersampling and pixelation affect a number of imaging systems, limiting the resolution of the acquired images, whichbecomes particularly significant for wide-field microscopy applications. Various super-resolution techniques have been implemented to mitigate this resolution loss by utilizing sub-pixel displacements in the imaging system, achieved, for example, byshifting the illumination source, the sensor array and/or the sample, followed by digital synthesis of a smaller effective pixel bymerging these sub-pixel-shifted low-resolution images. Herein, we introduce a new pixel super-resolution method that is basedon wavelength scanning and demonstrate that as an alternative to physical shifting/displacements, wavelength diversity can beused to boost the resolution of a wide-field imaging system and significantly increase its space-bandwidth product. We confirmedthe effectiveness of this new technique by improving the resolution of lens-free as well as lens-based microscopy systems anddeveloped an iterative algorithm to generate high-resolution reconstructions of a specimen using undersampled diffraction patterns recorded at a few wavelengths covering a narrow spectrum (10–30 nm). When combined with a synthetic-aperture-baseddiffraction imaging technique, this wavelength-scanning super-resolution approach can achieve a half-pitch resolution of250 nm, corresponding to a numerical aperture of ~ 1.0, across a large field of view (420 mm^(2)). We also demonstrated theeffectiveness of this approach by imaging various biological samples, including blood and Papanicolaou smears. Compared withdisplacement-based super-resolution techniques, wavelength scanning brings uniform resolution improvement in all directionsacross a sensor array and requires significantly fewer measurements. This technique would broadly benefit wide-field imagingapplications that demand larger space-bandwidth products.

Computational cytometer based on magnetically modulated coherent imaging and deep learning

Detecting rare cells within blood has numerous applications in disease diagnostics.Existing rare cell detection techniques are typically hindered by their high cost and low throughput.Here,we present a computational cytometer based on magnetically modulated lensless speckle imaging,which introduces oscillatory motion to the magneticbead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions(3D).In addition to using cell-specific antibodies to magnetically label target cells,detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network(P3D CNN),which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force.To demonstrate the performance of this technique,we built a high-throughput,compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples.Through serial dilution experiments,we quantified the limit of detection(LoD)as 10 cells per millilitre of whole blood,which could be further improved through multiplexing parallel imaging channels within the same instrument.This compact,cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.