This work presents a breast cancer detection modality using a metasurface as the imaging medium and a microwave source. The fundamental concept is to record the electromagnetic energy, which, when transmitted through the breast, scatters differently according to the variation in the electrical properties of the breast tissue. The energy captured by the metasurface forms an impression image of the breast under test. This impression is used as a detection tool to determine the presence of a tumor inside the breast. A convolution neural network (CNN) is applied to enhance detection accuracy and provide quantitative data about tumors when present. The CNN results show the possibility of finding a tumor with a 10 mm diameter and distinguish between healthy and unhealthy breasts with 93% accuracy while determining the size and the size with location with an accuracy of 91% and 74%, respectively.

Herein, the engineering and characterization of a photodetector using reduced graphene oxide gel (femtogel) as an active material are reported for the first time. The photodetector is engineered such that it contains a large detection area (interdigitated) and a femtogel–metal interface. The femtogel film is precisely placed between the interdigitated electrodes, which allows us to study the performance of the photodetector that has an end-contact interface. The femtogel width (channel) is controlled by adjusting the reactive ion etching processing time during the fabrication process. In addition, the thickness of the poly(methyl methacrylate) (PMMA) and electron-beam dose during the lithography process plays a significant role in initializing a window for etching the femtogel, which affects the channel width. In the visible range, a high responsivity has been achieved as high as 0.73 A W⁻¹ at a low power intensity of 84 mW cm⁻². Moreover, 3.4 s and 5.2 s are recorded as the time response of the photocurrent for growth and decay, respectively, in the proposed femtogel photodetector. This could open a new avenue for the fabrication of graphene-based photodetectors since placing graphene and its derivatives that are in liquid form between electrodes in a nanoscale detector is still a challenge in advanced nanofabrication.

According to the International Diabetes Federation, 589 million adults worldwide live with diabetes in 2025 (approximately 1 in 9 adults). The development of convenient noninvasive blood glucose monitoring systems has been a central focus in diabetes management. Optical spectroscopy has advanced significantly among all noninvasive glucose detection techniques. A photoacoustic system has been developed using a single-wavelength near-infrared laser, operating at 1625 nm, where glucose exhibits an overtone absorption band with relatively low water interference. The noninvasive system has been evaluated using artificial skin phantoms, with different glucose concentrations, covering both normoglycemic and hyperglycemic blood glucose levels. The detection sensitivity of the developed system has been enhanced to ±15 mg/dL across the entire clinically relevant glucose range. K-nearest neighbours and wide neural network machine learning models were developed for noninvasive glucose classification. The models achieved prediction accuracies of 80.0% and 81.5%, respectively, with 100% of the predicted data located within zones A and B of Clarke’s error grid analysis. These findings satisfy the regulatory requirements for glucose monitors established by Health Canada and the U.S. Food and Drug Administration.