Mar 2023
In this talk, we will present the design and implementation of monolithic and hybrid sensors using integrated circuits. We will showcase two examples in precision medicine and agriculture. First, we present a plant-wearable electrochemical sensor for in situ detection of salicylic acid. The sensor utilises microneedle-based electrodes that are functionalized with a layer of salicylic acid selective magnetic molecularly imprinted polymers. In addition, we will present laser-scribed graphene (LSG) sensors coupled with gold nanoparticles (AuNPs) as a stable promising biosensing platform. This Point of care (PoC) device is highly TP demanding to control the current pandemic, originating from severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The sensor was integrated into a homemade and portable potentiostat device, wirelessly connected to a smartphone having a customized application for easy operation.
Prof Khaled N. Salama received his B.S. degree from the Department of Electronics and Communications, Cairo University, Cairo, Egypt, in 1997, and his M.S. and PhD degrees from the Department of Electrical Engineering, Stanford University, Stanford, CA, USA, in 2000 and 2005, respectively. He was an Assistant Professor at Rensselaer Polytechnic Institute, NY, USA, between 2005 and 2009. He joined King Abdullah University of Science and Technology (KAUST) in January 2009 and was the founding Program Chair until August 2011. He was the director of the KAUST sensors initiative, the chair of the winter enrichment program (WEP) for two consecutive years (2019–2020) and Associate dean of the CEMSE division. His work on CMOS sensors for the National Institutes of Health (NIH) and the Defense Advanced Research Projects Agency (DARPA) have funded molecular detection, which was awarded the Stanford–Berkeley Innovators Challenge Award in biological sciences and was acquired by Ilumina Inc. He has authored 350 articles and 40 issued U.S. patents on low-power mixed-signal circuits for intelligent fully integrated sensors and neuromorphic circuits.