Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses

Current commercial air-quality monitoring devices lack a large dynamic range, especially at the small, ultrafine size scale. Furthermore, there is a low density of air-quality monitoring stations, reducing the precision with which local particulate matter hazards can be tracked. Here, we show a low-cost, lensfree, and portable air-quality monitoring device (LPAQD) that can detect and measure micron-sized particles down to 100 nm-sized particles, with the capability to track and measure particles in real time throughout a day and the ability to accurately measure particulate matter densities as low as 3 μg m-3. A vapor-condensed film is deposited onto the coverslip used to collect particles before the LPAQD is deployed at outdoor monitoring sites. The vapor-condensed film increases the scattering cross section of particles smaller than the pixel size, enabling the sub-pixel and sub-diffraction-limit-sized particles to be detected. The high dynamic range, low cost, and portability of this device can enable citizens to monitor their own air quality to hopefully impact user decisions that reduce the risk for particulate matter-related diseases.

Smartphone-based holographic measurement of polydisperse suspended particulate matter with various mass concentration ratios

Real-time monitoring of suspended particulate matter (PM) has become essential in daily life due to the adverse effects of long-term exposure to PMs on human health and ecosystems. However, conventional techniques for measuring micro-scale particulates commonly require expensive instruments. In this study, a smartphone-based device is developed for real-time monitoring of suspended PMs by integrating a smartphone-based digital holographic microscopy (S-DHM) and deep learning algorithms. The proposed S-DHM-based PM monitoring device is composed of affordable commercial optical components and a smartphone. Overall procedures including digital image processing, deep learning training, and correction process are optimized to minimize the prediction error and computational cost. The proposed device can rapidly measure the mass concentrations of coarse and fine PMs from holographic speckle patterns of suspended polydisperse PMs in water with measurement errors of 22.8 ± 18.1% and 13.5 ± 9.8%, respectively. With further advances in data acquisition and deep learning training, this study would contribute to the development of hand-held devices for monitoring polydisperse non-spherical pollutants suspended in various media.

Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning

Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions.

Accurate real-time monitoring of high particulate matter concentration based on holographic speckles and deep learning

Accurate real-time monitoring of particulate matter (PM) has emerged as a global issue due to the hazardous effects of PM on public health and industry. However, conventional PM monitoring techniques are usually cumbersome and require expensive equipments. In this study, Holo-SpeckleNet is proposed as a fast and accurate PM concentration measurement technique with high throughput using a deep learning based holographic speckle pattern analysis. Speckle pattern datasets of PMs for a wide range of PM concentrations were acquired by using a digital in-line holography microscopy system. Deep autoencoder and regression algorithms were trained with the captured speckle pattern datasets to directly measure PM concentration from speckle pattern images without any air intake device and time-consuming post image processing. The proposed technique was applied to predict various PM concentrations using the test datasets, optimize hyperparameters, and compare its performance with a convolutional neural network (CNN) algorithm. As a result, high PM concentration values can be measured over air quality index of 150, above which human exposure is unhealthy. In addition, the proposed technique exhibits higher measurement accuracy and less overfitting than the CNN with a relative error of 7.46 ± 3.92%. It can be applied to design a compact air quality monitoring device for highly accurate and real-time measurement of PM concentrations under hazardous environment, such as factories or construction sites.

Use of universal 3D-Printed smartphone spectrophotometer to develop a time-based analysis for hypochlorite

A fully-functional smartphone-based spectrophotometer was designed and built using 3D printing. The major advantage of this approach is its capacity to be interfaced with a variety of smartphones, allowing the use of the smartphone's camera and display, and regardless of the relative position of the camera. The analytical performance of the device was analyzed using a model dye (crystal violet), leading to a proportional response for concentrations in the 0.06-15.0 mg L^-1 range, with a variability of 1.0% (intra-day) and 2.6% (inter-day). To demonstrate the functionality of the device, the degradation process of the dye by sodium hypochlorite was studied. The results obtained were applied to develop a paper-based test for NaClO in sanitation solutions, in which the time required to bleach the dye was used to estimate the concentration of the solution. This device represents a simple and inexpensive tool for everyday laboratory use and could address important analytical challenges in low-income communities and features a versatile arrangement, that is compatible with a wide variety of smartphones and software platforms.