How to Build Live-Cell Sensor Microdevices.

There is a lot of discussion on how viruses (such as influenza and SARS-CoV-2) are transmitted in air, potentially from aerosols and respiratory droplets, and thus it is important to monitor the environment for the presence of an active pathogen. Currently, the presence of viruses is being determined using primarily nucleic acid-based detection methods, such as reverse transcription- polymerase chain reaction (RT-PCR) tests. Antigen tests have also been developed for this purpose. However, most nucleic acid and antigen methods fail to discriminate between a viable and a non-viable virus. Therefore, we present an alternative, innovative, and disruptive approach involving a live-cell sensor microdevice that captures the viruses (and bacteria) from the air, becomes infected by them, and emits signals for an early warning of the presence of pathogens. This perspective outlines the processes and components required for living sensors to monitor the presence of pathogens in built environments and highlights the opportunity to use immune sentinels in the cells of normal human skin to produce monitors for indoor air pollutants.

Porphyrinoids coated Silica Nanoparticles Capacitive Sensors for COVID-19 Detection from the Analysis of Blood Serum Volatolome

The growing interest in analyzing human volatolome is among the primary motivations for developing gas sensors. Among the available sensor technologies, porphyrinoids coated quartz microbalance (QMBs) sensors demonstrate to be sensitive and selective enough to identify several diseases. Some drawbacks affect these transducers, such as difficult miniaturization and low-cost production. In this paper, we investigate capacitive sensors based on films of porphyrinoids functionalized silica nanoparticles as alternative and advantageous technology to match the performance of quartz microbalance sensors. The variation of sensor capacitance depended on the diffusion of airborne molecules into the sensing film, favoring the extraction of low-correlated dynamic features. As a demonstration of sensor properties, a three-sensor array has been used to measure the volatile compounds from blood serum to discriminate COVID-19 over other pathologies. Sensor data, processed by linear discriminant analysis, identified COVID-19 samples with 89% and 75% accuracy in training and test.