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Economical and nanomolar-level determination of the analgesic drug, acetaminophen (APAP), is reported in this work. A novel ternary nanocomposite based on silver-doped sugar apple-like cupric oxide (CuO)-decorated amine-functionalized multi-walled carbon nanotubes (fCNTs) was sonochemically prepared. CuO nanoparticles were synthesized based on the ascorbic acid-mediated low-temperature method, and sidewall functionalization of CNTs was carried out. Important characterizations of the synthesized materials were analyzed using SEM, TEM, HAADF-STEM, elemental mapping, EDX, lattice fringes, SAED pattern, XRD, EIS, UV-Vis, micro-Raman spectroscopy, and FTIR. It was noted the sonochemically prepared nanocomposite diligently fabricated on screen-printed carbon electrode showcased outstanding electrocatalytic performance towards APAP determination. The APAP sensor exhibited ultra-low limit of detection of 4 nM, wide linear concentration ranges of 0.02–3.77 and 3.77–90.02 μM, and high sensitivity of 30.45 μA μM−1 cm−2. Moreover, further evaluation of the sensor's performance based on electrochemical experiments showcased outstanding selectivity, stability, reproducibility, and repeatability. Further, excellent practical feasibility of the proposed APAP sensor was affirmed with excellent recovery larger than 96.86% and a maximum RSD of 3.67%. [ABSTRACT FROM AUTHOR]

The sensitive detection and degradation of synthetic dyes are pivotal to maintain safety owing to the adverse side effects they impart on living beings. In this work, we developed a sensitive electrochemical sensor for the nanomolar-level detection of rhodamine B (RhB) using a dual-functional, silver-decorated zinc oxide (Ag/ZnO) composite-modified, screen-printed carbon electrode. The plasmon-enhanced photocatalytic degradation of organic pollutant RhB was also performed using this nanocomposite prepared by embedding different weight percentages (1, 3, and 5 wt%) of Ag nanoparticles on the surface of a three-dimensional (3D), hierarchical ZnO nanostructure based on the photoreduction approach. The structure and morphology of an Ag/ZnO nanocomposite were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental mapping, ultraviolet-visible (UV-vis) spectroscopy, and X-ray diffraction (XRD). The electrochemical sensor exhibited a very high sensitivity of 151.44 µAµM−1cm−2 and low detection limit of 0.8 nM towards RhB detection. The selectivity, stability, repeatability, reproducibility, and practical feasibility were also analyzed to prove their reliability. Furthermore, the photocatalysis results revealed that 3 wt% of the Ag/ZnO hybrid nanostructure acquired immense photostability, reusability, and 90.5% degradation efficiency under visible light. Additionally, the pseudo-first-order rate constant of Ag-3/ZnO is 2.186 min−1 suggested promising activity in visible light photocatalysis. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a real-time wearable assist system for upper extremity throwing action based on the accelerometers of inertial measurement unit (IMU) sensors. This real-time assist system can be utilized to the learning, rectification, and rehabilitation for the upper extremity throwing action of players in the field of baseball, where incorrect throwing phases are recognized by a delicate action analysis. The throwing action includes not only the posture characteristics of each phase, but also the transition of continuous posture movements, which is more complex when compared to general action recognition with no continuous phase change. In this work, we have considered six serial phases including wind-up, stride, arm cocking, arm acceleration, arm deceleration, and follow-through in the throwing action recognition process. The continuous movement of each phase of the throwing action is represented by a one-dimensional data sequence after the three-axial acceleration signals are processed by efficient noise filtering based on Kalman filter followed by conversion processes such as leveling and labeling techniques. The longest common subsequence (LCS) method is then used to determine the six serial phases of the throwing action by verifying the sequence data with a sample sequence. We have incorporated various intelligent action recognition functions including automatic recognition for getting ready status, starting movement, handle interrupt situation, and detailed posture transition in the proposed assist system. Moreover, a liquid crystal display (LCD) panel and mobile interface are incorporated into the developed assist system to make it more user-friendly. The real-time system provides precise comments to assist players to attain improved throwing action by analyzing their posture during throwing action. Various experiments were conducted to analyze the efficiency and practicality of the developed assist system as part of this work. We have obtained an average percentage accuracy of 95.14%, 91.42%, and 95.14%, respectively, for all the three users considered in this study. We were able to successfully recognize the throwing action with good precision and the high percentage accuracy exhibited by the proposed assist system indicates its excellent performance. [ABSTRACT FROM AUTHOR]