The Nernst equation defines the maximum sensitivity of metal oxide pH sensors, which has a theoretical value of 59.2 mV/pH. However, the phenomenon of super-Nernstian pH response of a metal oxide sensing membrane is related to the hydration of the sensing film. In this context, the purpose of this research is to compare the pH sensing capabilities of distilled water (DI)-pretreated RuO _x -based extended gate field-effect transistor (EGFET) pH sensors with those of as-deposited RuO _x sensing films. The surface composition of the film was explored by X-ray photoelectron spectroscopy. For the as-deposited film, the RuO _x sensor exhibited a sub-Nernstian pH sensitivity of 58.91 mV/pH. The DI-pretreated RuO _x sample, however, demonstrated a super-Nernstian pH response of 64.13 mV/pH, with a linearity of 0.997. It is possible to attribute the enhanced sensitivity to the hydration process, which increases the amount of effective surface sites available for pH sensing. The result is more proton (H ⁺ ) adsorption and desorption sites, exceeding the Nernstian limit of 59.2 mV/pH. Additionally, the DI-pretreated RuO _x EGFET sensor demonstrated several advantages, including a low hysteresis voltage (~2 mV), a minimal drift rate (0.25 mV/h), good flexibility, long-term stability (120 days), and excellent repeatability (with 97.95 % retention).

Tooth decay is a chronic disease resulting in pain, infection, and tooth loss. This illness is common because many factors, such as poor oral hygiene, sugar consumption, and microbial flora, contribute to dental cavities. Untreated or undetected tooth decay often escalates to a more severe stage, emphasizing the importance of early detection and intervention. We propose a portable, low-cost, and ergonomic optoelectronic device to provide a possible solution for the early detection of dental cavities before annual or regular dental checkups for the first time. This device integrates mini cameras on top of a dental impression tray to capture images of the teeth, and the photos can be transmitted via Wi-Fi to the cloud for real-time cavities detection through a YOLO (You Only Look Once) algorithm that is based on a convolutional neural network (CNN). Our results show the precision, recall, and (mean average precision)@0.5:0.95 for YOLOv5 (0.72, 0.70, 0.75), YOLOv6 (0.59, 0.50, 0.58), and YOLOv7 (0.93, 0.94, 0.82). We also compared the YOLO algorithm with traditional techniques such as support vector machine and k-nearest neighbor algorithms. This intra-oral cavity detection system paves the way for early detection of dental cavities with quick accessibility and affordable cost. We foresee that this optoelectronic device will play a role in advancing biomedical technologies, ultimately promoting the long-term well-being of individuals.

Large-scale location estimation is crucial for many Artificial Intelligence Internet of Things ( IoT ) applications in the era of smart cities. This letter proposes a deep learning-based outdoor positioning scheme for large-scale wireless settings using fingerprinting techniques. We first developed a feature extraction technique using t -distributed stochastic neighbor embedding ( t -SNE) to extract dominant and distinguishable features while eliminating noises from the radio fingerprints. Afterward, we developed a deep learning-based coarse-fine localization framework to improve positioning performance significantly. Based on our numerical analysis, the proposed scheme reduces computation time by 64.41%, and the average positioning error is 38 cm. Therefore, the proposed approach significantly improved positioning accuracy and reduced computation time.

A 15-nm-thick GdO x membrane in an electrolyte-insulator-semiconductor (EIS) structure shows a higher pH sensitivity of 54.2 mV/pH and enzyme-free hydrogen peroxide (H2O2) detection than those of the bare SiO2 and 3-nm-thick GdO x membranes for the first time. Polycrystalline grain and higher Gd content of the thicker GdO x films are confirmed by transmission electron microscopy (TEM) and X-ray photo-electron spectroscopy (XPS), respectively. In a thicker GdO x membrane, polycrystalline grain has lower energy gap and Gd2+ oxidation states lead to change Gd3+ states in the presence of H2O2, which are confirmed by electron energy loss spectroscopy (EELS). The oxidation/reduction (redox) properties of thicker GdO x membrane with higher Gd content are responsible for detecting H2O2 whereas both bare SiO2 and thinner GdO x membranes do not show sensing. A low detection limit of 1 μM is obtained due to strong catalytic activity of Gd. The reference voltage shift increases with increase of the H2O2 concentration from 1 to 200 μM owing to more generation of Gd3+ ions, and the H2O2 sensing mechanism has been explained as well.

In this research work, we present a flexible iridium oxide (IrO _x ) extended-gate field-effect transistor (EGFET) biosensor for label-free detection of the epidermal growth factor receptor (EGFR) biomarker. IrO _x was employed as the sensing membrane material due to its excellent electrochemical properties, high conductivity, stability, biocompatibility, and ability to facilitate redox reactions. The IrO _x film was deposited on a flexible polyimide (PI) substrate through a sol–gel process and characterized using atomic force microscopy and X-ray photoelectron spectroscopy. As a pH sensor, the IrO _x EGFET exhibited a super-Nernstian sensitivity of 68.54 mV/pH with high linearity, a low hysteresis of ∼ 6 mV, and a drift rate of 0.50 mV/h at pH 7 over 12 h. For EGFR detection, the IrO _x surface was functionalized with 3-aminopropyltriethoxysilane and glutaraldehyde to immobilize anti-EGFR antibodies. The EGFR sensor demonstrated a wide linear detection range from 1 to 1000 ng/mL with a sensitivity of 11.78 mV/log(ng/mL) and a linearity of 99.8%, making it suitable for clinical EGFR monitoring in cancer patients. The flexible IrO _x EGFET shows promise for reliable label-free detection of EGFR.