3D Printed CNT/TPU Triboelectric Nanogenerator for Load Monitoring of Total Knee Replacement

This study presents the development and characterization of a novel triboelectric nanogenerator (TENG) designed as a self-powered sensor for load monitoring in total knee replacement (TKR) implants. The triboelectric layers comprise a 3D-printed thermoplastic polyurethane (TPU) matrix with carbon nanotube (CNT) nanoparticles and kapton tape, sandwiched between two copper electrodes. To optimize sensor performance, the proposed CNT/TPU TENG sensor is fabricated with varying CNT concentrations and thicknesses, enabling a comprehensive analysis of how material composition and structural parameters influence energy harvesting efficiency. The 1% CNT/TPU composite demonstrates the highest power output among the tested samples. The solid CNT/TPU-based TENG generated the apparent output power of 4.1 μW under a cyclic compressive load of 2100 N, measured across a 1.6 GΩ load resistance and over a nominal contact area of 15.9 cm², while the foam CNT/TPU film achieved a higher apparent output power of 6.9 μW measured across a 0.9 GΩ load resistance with the same nominal area. The generated power is sufficient to operate a power management and ADC circuit based on our earlier work. The sensors exhibit a stable open-circuit voltage of 320 V for the foam layer and 275 V for the solid one. Sensitivities are 80.50 mV/N (≤ 1600 N) and 24.60 mV/N (\u3e 1600 N) for foam CNT/TPU film, demonstrating the integrated sensor capability f or wide-range force sensing on TKR implants. The foam CNT/TPU-based TENG maintained stable performance over 16,000 load cycles, confirming its potential f or long-term use inside the TKR. Additionally, the dielectric constant of the CNT/TPU composite was found to increase with increasing CNT concentration. The proposed CNT/TPU TENG sensor offers a broad working range and robust energy-harvesting efficiency, making it appropriate for self-powered load sensing in biomedical applications

Wide Dynamic Range CMOS Potentiostat for Amperometric Chemical Sensor

Presented is a single-ended potentiostat topology with a new interface connection between sensor electrodes and potentiostat circuit to avoid deviation of cell voltage and linearly convert the cell current into voltage signal. Additionally, due to the increased harmonic distortion quantity when detecting low-level sensor current, the performance of potentiostat linearity which causes the detectable current and dynamic range to be limited is relatively decreased. Thus, to alleviate these irregularities, a fully-differential potentiostat is designed with a wide output voltage swing compared to single-ended potentiostat. Two proposed potentiostats were implemented using TSMC 0.18-μm CMOS process for biomedical application. Measurement results show that the fully differential potentiostat performs relatively better in terms of linearity when measuring current from 500 pA to 10 uA. Besides, the dynamic range value can reach a value of 86 dB

A miniature, lowpower , intelligent sensor node for persistent acoustic surveillance

ABSTRACT The desire for persistent, long term surveillance and covertness places severe constraints on the power consumption of a sensor node. To achieve the desired endurance while minimizing the size of the node, it is imperative to use application-specific integrated circuits (ASICs) that deliver the required performance with maximal power efficiency while minimizing the amount of communication bandwidth needed. This paper reviews our ongoing effort to integrate several micropower devices for low-power wake-up detection, blind source separation and localization and pattern classification, and demonstrate the utility of the system in relevant surveillance applications. The capabilities of each module are presented in detail along with performance statistics measured during recent experiments

Real-Time Telemetry System for Amperometric and Potentiometric Electrochemical Sensors

A real-time telemetry system, which consists of readout circuits, an analog-to-digital converter (ADC), a microcontroller unit (MCU), a graphical user interface (GUI), and a radio frequency (RF) transceiver, is proposed for amperometric and potentiometric electrochemical sensors. By integrating the proposed system with the electrochemical sensors, analyte detection can be conveniently performed. The data is displayed in real-time on a GUI and optionally uploaded to a database via the Internet, allowing it to be accessed remotely. An MCU was implemented using a field programmable gate array (FPGA) to filter noise, transmit data, and provide control over peripheral devices to reduce power consumption, which in sleep mode is 70 mW lower than in operating mode. The readout circuits, which were implemented in the TSMC 0.18-μm CMOS process, include a potentiostat and an instrumentation amplifier (IA). The measurement results show that the proposed potentiostat has a detectable current range of 1 nA to 100 μA, and linearity with an R2 value of 0.99998 in each measured current range. The proposed IA has a common-mode rejection ratio (CMRR) greater than 90 dB. The proposed system was integrated with a potentiometric pH sensor and an amperometric nitrite sensor for in vitro experiments. The proposed system has high linearity (an R2 value greater than 0.99 was obtained in each experiment), a small size of 5.6 cm × 8.7 cm, high portability, and high integration

Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field