A Microfluidic MEMS-Microbalance Platform With Minimized Acoustic Radiation in Liquid.

In this article, the microfluidic channels that deliver liquid to a microscale thin-film piezoelectric-on-silicon (TPoS) gravimetric resonant sensor are incorporated into the backside of the silicon-on-insulator (SOI) wafer on which the resonator is fabricated. Specifically, a microwell is embedded at the bottom of the disk -shaped TPoS resonator, while a very thin layer of parylene covering the backside of the resonator and the microwell forms an isolation layer between the liquid and the top device-layer features. In this way, the liquid is in contact with the backside of the resonator, while the device-defining trenches and the electrical connections to the resonator stay clear, thus mitigating the acoustic energy loss and undesirable feedthroughs. The impact of the parylene layer thickness on a few symmetric ( S ) and antisymmetric ( A ) Lamb wave modes of the resonator is experimentally studied, and the performance of such modes in the liquid is characterized by filling the microwells through a PDMS-based microfluidic channel. The parylene layer, while marginally affecting the resonator in the air, is found to substantially enhance its performance in the liquid media. Strong resonance peaks with high quality factors ( Q ) are observed for the S modes, among which Q values above 400 are recorded for a specific mode named S (4, 2) (among the highest ever reported). This article can potentially facilitate the realization of highly stable and sensitive resonant mass sensors (i.e., microbalance) for real-time applications. Additionally, the effect of the acoustic energy radiation in the form of evanescent shear and longitudinal waves in liquid on the Q and resonance frequency of the disk resonators is experimentally validated.

Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing.

We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted.