Optimization of SAW Sensors for Nanoplastics and Grapevine Virus Detection.

In this work, we report the parametric optimization of surface acoustic wave (SAW) delay lines on Lithium niobate for environmental monitoring applications. First, we show that the device performance can be improved by acting opportunely on geometrical design parameters of the interdigital transducers such as the number of finger pairs, the finger overlap length and the distance between the emitter and the receiver. Then, the best-performing configuration is employed to realize SAW sensors. As aerosol particulate matter (PM) is a major threat, we first demonstrate a capability for the detection of polystyrene particles simulating nanoparticulates/nanoplastics, and achieve a limit of detection (LOD) of 0.3 ng, beyond the present state-of-the-art. Next, the SAW sensors were used for the first time to implement diagnostic tools able to detect Grapevine leafroll-associated virus 3 (GLRaV-3), one of the most widespread viruses in wine-growing areas, outperforming electrochemical impedance sensors thanks to a five-times better LOD. These two proofs of concept demonstrate the ability of miniaturized SAW sensors for carrying out on-field monitoring campaigns and their potential to replace the presently used heavy and expensive laboratory instrumentation.