Nanogenerator-based self-powered sensors for data collection

Implantable Triboelectric Nanogenerators for Self-Powered Cardiovascular Healthcare

Triboelectric nanogenerators (TENGs) have gained significant traction in recent years in the bioengineering community. With the potential for expansive applications for biomedical use, many individuals and research groups have furthered their studies on the topic, in order to gain an understanding of how TENGs can contribute to healthcare. More specifically, there have been a number of recent studies focusing on implantable triboelectric nanogenerators (I-TENGs) toward self-powered cardiac systems healthcare. In this review, the progression of implantable TENGs for self-powered cardiovascular healthcare, including self-powered cardiac monitoring devices, self-powered therapeutic devices, and power sources for cardiac pacemakers, will be systematically reviewed. Long-term expectations of these implantable TENG devices through their biocompatibility and other utilization strategies will also be discussed.

A holistic review on the recent trends, advances, and challenges for high-precision room temperature liquefied petroleum gas sensors

Liquefied petroleum gas (LPG), which is mainly composed of hydrocarbons, such as propane and butane, is a flammable gas that is considered a clean source of energy. Currently, the overwhelming use of LPG as fuel in vehicles, domestic settings, and industry has led to several incidents and deaths globally due to leakage. As a result, the appropriate detection of LPG is vital; thus, gas-sensing devices that can monitor this gas rapidly and accurately at room temperature, are required. This work reviews the current advances in LPG gas sensors, which operate at room temperature. The influences of the synthesis methods and parameters, doping, and use of catalysts on the sensing performance are discussed. The formation of heterostructures made from semiconducting metal oxides, polymers, and graphene-based materials, which enhance the sensor selectivity and sensitivity, is also discussed. The future trends and challenges confronted in the advancement of LPG room temperature operational gas sensors, and critical ideas concerning the future evolution of LPG gas sensors, are deliberated. Additionally, the advancements in the next-generation gas sensors, such as the wireless detection of LPG leakage, self-powered sensors driven by triboelectric/piezoelectric mechanisms, and artificial intelligent systems are also reviewed. This review further focuses on the use of smartphones to circumvent the use of costly instruments and paves the way for cost-efficient and portable monitoring of LPG. Finally, the approach of utilizing the Internet of Things (IoT) to detect/monitor the leakage of LPG has also been discussed, which will provide better alerts to the users and thus minimize the effects of leakages.

Low-cost composite film triboelectric nanogenerators for a self-powered touch sensor

In the last two decades, nanogenerators have been studied inorder to solve the power supply problems of electronic devices. Triboelectric nanogenerators (TENGs) have gained widespread attention due to their excellent properties and wide range of applications. However, previous studies frequently ignored numerous application-related issues and even wasted some of the benefits of the TENG itself in favor of enhanced performance. Here, we propose a TENG based on BaTiO₃ (BTO)-polydimethylsiloxane (PDMS) composite films with low cost and simple preparation, where its maximum output performance is obtained when the mass proportion of BTO to PDMS is 40%. In addition, we demonstrate how the single-electrode TENG may be used as a self-powered touch sensor that can communicate with a microcontroller unit (MCU) to turn LED lights on and off. This practical example will provide a valuable reference for the application of low-cost self-powered sensors in wearable devices, Internet of Things, human-machine interactions and other fields. Furthermore, we discovered a number of issues that were rarely or never addressed in previous studies and provide some solutions, such as a signal processing method for a TENG-based self-powered sensor. It serves as a foundation for future investigations on the performance assessment and application of TENGs.

Detect, predict, and prevent acute seizures and status epilepticus

Status epilepticus is one of the most frequent pediatric neurological emergencies. While etiology is often influencing the outcome, more easily modifiable risk factors of outcome include detection of prolonged convulsive seizures and status epilepticus and appropriately dosed and timely applied medication treatment. Unpredictability and delayed or incomplete treatment may at times lead to longer seizures, thereby affecting outcomes. Barriers in the care of acute seizures and status epilepticus include the identification of patients at greatest risk of convulsive status epilepticus, potential stigma, distrust, and uncertainties in acute seizure care, including caregivers, physicians, and patients. Furthermore, unpredictability, detection capability, and identification of acute seizures and status epilepticus, limitations in access to obtaining and maintaining appropriate treatment, and rescue treatment options pose challenges. Additionally, timing and dosing of treatment and related acute management algorithms, potential variations in care due to healthcare and physician culture and preference, and factors related to access, equity, diversity, and inclusion of care. We outline strategies for the identification of patients at risk of acute seizures and status epilepticus, improved status epilepticus detection and prediction, and acute closed-loop treatment and status epilepticus prevention. This paper was presented at the 8th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures held in September 2022.

Self-powered high-sensitivity all-in-one vertical tribo-transistor device for multi-sensing-memory-computing

Devices with sensing-memory-computing capability for the detection, recognition and memorization of real time sensory information could simplify data conversion, transmission, storage, and operations between different blocks in conventional chips, which are invaluable and sought-after to offer critical benefits of accomplishing diverse functions, simple design, and efficient computing simultaneously in the internet of things (IOT) era. Here, we develop a self-powered vertical tribo-transistor (VTT) based on MXenes for multi-sensing-memory-computing function and multi-task emotion recognition, which integrates triboelectric nanogenerator (TENG) and transistor in a single device with the simple configuration of vertical organic field effect transistor (VOFET). The tribo-potential is found to be able to tune ionic migration in insulating layer and Schottky barrier height at the MXene/semiconductor interface, and thus modulate the conductive channel between MXene and drain electrode. Meanwhile, the sensing sensitivity can be significantly improved by 711 times over the single TENG device, and the VTT exhibits excellent multi-sensing-memory-computing function. Importantly, based on this function, the multi-sensing integration and multi-model emotion recognition are constructed, which improves the emotion recognition accuracy up to 94.05% with reliability. This simple structure and self-powered VTT device exhibits high sensitivity, high efficiency and high accuracy, which provides application prospects in future human-mechanical interaction, IOT and high-level intelligence.