Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.

To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor's input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor.

Triple click chemistry for crosslinking, stiffening, and annealing of gelatin-based microgels.

Microgels are spherical hydrogels with physicochemical properties ideal for many biomedical applications. For example, microgels can be used as individual carriers for suspension cell culture or jammed/annealed into granular hydrogels with micron-scale pores highly permissive to molecular transport and cell proliferation/migration. Conventionally, laborious optimization processes are often needed to create microgels with different moduli, sizes, and compositions.

Photometric Monitoring of Electronic Cigarette Puff Topography.

To study and monitor the adverse health consequences of using electronic cigarettes, a user's puff topography, which are quantification parameters of the user's vaping habits, plays a central role. In this work, we introduce a topography sensor to measure the mass of total particulate matter generated in every puff and to estimate the nicotine yield. The sensor is compact and low-cost, and is integrated into the electronic cigarette device to promptly and conveniently monitor the user's daily puff topography.