Phenylketonuria monitoring in human blood serum by mosses extract/ZnO@Au nanoarrays-loaded filter paper as a novel electrochemical biosensor

Phytic acid determination in food products using the extract of rice sprout and SBA@DABCO nanoparticle-modified filter paper as a novel electrochemical biosensor

We outline the fabrication of a highly sensitive biosensor for phytic acid (PA) determination using the extract of rice sprout and SBA@DABCO nanoparticles. The nanoparticles were first added to the surface of small paper disks. After drying, the extract of rice sprout in phosphate buffered saline was immobilized on the paper disks. FT-IR spectroscopy, XRD, and SEM were employed for the characterization of the nanoparticles. The assembly process of the modified paper disks on top of graphite screen-printed electrodes was investigated by cyclic voltammetry and electrochemical impedance spectroscopy. PA measurements were performed by differential pulse voltammetry (DPV) with a small amount of sample (7 μL) for analysis. The cooperation of the rice sprout extract and the nanostructure of this biosensor led to the selective and sensitive electrochemical determination of PA. The limit of detection (S/N = 3) and linear dynamic range for PA determination by DPV were obtained at 0.04 μM and 0.1-10.0 μM, respectively. The biosensor showed excellent sensitivity for the determination of PA in real samples such as biscuits and wheat flour.

Machine Learning-Reinforced Noninvasive Biosensors for Healthcare

The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health-related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning-reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning-reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning-reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real-time monitoring, out-of-clinic diagnosis, and onsite food safety detection are proposed.

Effect of ZnO surface morphology on its electrochemical performance

The purpose of this paper is to bridge the gap between ZnO surface morphology and its electrochemical performance. For this reason, ZnO nanowires (NWs) of different length were synthesized using an electrochemical method. Then, the electrochemical performance of the synthesized ZnO surfaces was studied using cyclic voltammetry and electrochemical impedance spectroscopy. The electrochemical analysis results revealed that the increase of ZnO NW length contributes to the retrogression of electrochemical performance. Indeed, the electrochemical performance is mainly related to the wettability behavior of the ZnO nanowire surfaces. When the ZnO NWs length increases, the surface become more hydrophobic, therefore, charge transfers between the electrode/electrolyte decrease. To improve the electrochemical performance of ZnO, we propose a new strategy combining NWs and microsheets (μSs) for further improving the morphology. Finally, the surfaces based on the double structure of ZnO provide good propagation of charge at the surface, good transfer in the electrode, good stability, and excellent scanning ability. In the present work we intend to pave the way for achieving high electrochemical performance ZnO-based layers.

The purpose of this paper is to bridge the gap between ZnO surface morphology and its electrochemical performance.