In this paper, we designed a triboelectric acceleration sensor with excellent multiple parameters. To more easily detect weak vibrations, the sensor was founded on a multilayer suspension structure. To effectively improve the electrical properties of the sensor, a surface roughening and internal doping friction film, which was refined with a room temperature vulcanized silicone rubber (RTV) and some thermoplastic polyurethanes (TPU) powder in a certain proportion, was integrated into the structure. It was found that the optimization of the RTV film increases the open circuit voltage and short circuit current of the triboelectric nanogenerator (TENG) by 223% and 227%, respectively. When the external vibration acceleration is less than 4 m/s, the sensitivity and linearity are 1.996 V/(m/s) and 0.999, respectively. Additionally, when it is in the range between 4 m/s and 15 m/s, those are 23.082 V/(m/s) and 0.975, respectively. Furthermore, the sensor was placed in a simulated truck vibration environment, and its self-powered monitoring ability validated by experiments in real time. The results show that the designed sensor has strong practical value in the field of monitoring mechanical vibration acceleration.
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http://dx.doi.org/10.3390/nano11102763 | DOI Listing |
Background: Interest in use of digital technology to advance AD/ADRD research has been growing exponentially over the last few years. This acceleration is fueled in part by growing awareness that both well used research methods as well as newer biomarker approaches are 1) inadequate for clinical symptom detection in the earliest stages of an insidious onset disease and 2) have resulted in inaccurate as well as biased data that is generating treatment and prevention solutions that are insufficiently relevant to some and potentially not relevant to many.
Methods: Sensors embedded in mobile devices such as smartphones and wearables deliver a high penetration, low-cost solution for overcoming previous limitations of early detection sensitivity and limited representative reach.
Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Nanotechnology, Faculty of Chemistry, Urmia University, Urmia, Iran.
In the present work, nitrogen-doped carbon was synthesized starting from a chitosan/urea mixture and immobilized at the surface of a bare glassy carbon electrode to detect Cd(II) ions using differential pulse-anodic stripping voltammetry method (DP-ASV). The synthesized nitrogen-doped carbon showed a significant potential for determining Cd(II) ions. Doping carbon with nitrogen atoms gives a structure with increased valence band energy, leading to acceleration of the electron transfer by creating an interaction of nitrogen's free electrons with Cd(II), which subsequently increases the peak current value.
View Article and Find Full Text PDFAccid Anal Prev
December 2024
Zachry Department of Civil & Environmental Engineering, Texas A&M University, College Station, TX 77843, USA.
Near-miss traffic risk estimation using Extreme Value Theory (EVT) models within a real-time framework offers a promising alternative to traditional historical crash-based methods. However, current approaches often lack comprehensive analysis that integrates diverse roadway geometries, crash patterns, and two-dimensional (2D) vehicle dynamics, limiting both their accuracy and generalizability. This study addresses these gaps by employing a high-fidelity, 2D time-to-collision (TTC) near-miss indicator derived from autonomous vehicle (AV) sensor data.
View Article and Find Full Text PDFMikrochim Acta
December 2024
State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, 541004, China.
An electrochemiluminescence (ECL) immunosensor was developed for the highly sensitive and specific detection of heart-type fatty acid binding protein (H-FABP) and the rapid diagnosis of acute myocardial infarction (AMI). H-FABP is a biomarker that is highly specific to cardiac tissue and is associated with a range of cardiac diseases. Following myocardial injury, the rate of increase in H-FABP levels is greater than that observed for myoglobin and troponin.
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