Small form factor flow virometer for SARS-CoV-2.

Current diagnostics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection heavily rely on reverse transcription-polymerase chain reaction (RT-PCR) or on rapid antigen detection tests. The former suffers from long time-to-result and high cost while the latter from poor sensitivity. Therefore, it is crucial to develop rapid, sensitive, robust, and inexpensive methods for SARS-CoV-2 testing. Herein, we report a novel optofluidic technology, a flow-virometry reader (FVR), for fast and reliable SARS-CoV-2 detection in saliva samples. A small microfluidic chip together with a laser-pumped optical head detects the presence of viruses tagged with fluorescent antibodies directly from saliva samples. The technology has been validated using clinical samples with high sensitivity (91.2%) and specificity (90%). Thanks also to its short time-to-result (<30 min) and small size (25 × 30 × 13 cm), which can be further reduced in the future, it is a strong alternative to existing tests, especially for point-of-care (POC) and low resource settings.

An ultra-compact particle size analyser using a CMOS image sensor and machine learning.

Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.