A robust power device for wearable technologies and soft electronics must feature good encapsulation, high deformability, and reliable electrical outputs. Despite substantial progress in materials and architectures for two-dimensional (2D) planar power configurations, fiber-based systems remain limited to relatively simple configurations and low performance due to challenges in processing methods. Here, we extend complex 2D triboelectric nanogenerator configurations to 3D fiber formats based on scalable thermal processing of water-resistant thermoplastic elastomers and composites. We perform mechanical analysis using finite element modeling to understand the fiber's deformation and the level of control and engineering on its mechanical behavior and thus to guide its dimensional designs for enhanced electrical performance. With microtexture patterned functional surfaces, the resulting fibers can reliably produce state-of-the-art electrical outputs from various mechanical deformations, even under harsh conditions. These mechanical and electrical attributes allow their integration with large and stretchable surfaces for electricity generation of hundreds of microamperes.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651858 | PMC |
http://dx.doi.org/10.1126/sciadv.abo0869 | DOI Listing |
Glia
January 2025
Neurophysiology Research Center, Institute of Neuroscience and Cognition, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Autism spectrum disorder (ASD) is marked by neurobehavioral developmental deficits, potentially linked to disrupted neuron-glia interactions. The astroglia Kir4.1 channel plays a vital role in regulating potassium levels during neuronal activation, and mutations in this channel have been associated with ASD.
View Article and Find Full Text PDFAnal Chim Acta
February 2025
Key Laboratory of Chemical Sensing & Analysis in Universities of Shandong, School of Chemistry and Chemical Engineering, University of Jinan, 250022, Jinan, PR China; Department of Chemistry, Sungkyunkwan University, 16419, Suwon, Republic of Korea. Electronic address:
Photoelectrochemical (PEC) immunosensors are highly promising tools for monitoring biochemical molecules. Constructing high-performance heterojunctions is a general method to improve the sensitivity of PEC immunosensors. The internal electric field (IEF) formed at the heterojunction interface plays a crucial role in coordinating the separation of photogenerated carriers.
View Article and Find Full Text PDFJ Neurosci Methods
January 2025
School of Mathematics and Statistics, Ludong University, Yantai 264025, China. Electronic address:
Background: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more effective prediction methods based on artificial intelligence (AI) are needed to assist physicians in timely diagnosis.
New Methods: An explainable deep learning-based early Parkinson's disease diagnostic model, Parkinson's Integrative Diagnostic Gated Network (PIDGN), was designed by fusing Single Nucleotide Polymorphism (SNP) and brain sMRI data.
Chem Soc Rev
January 2025
State Key Laboratory of Multiphase Flow in Power Engineering & School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710054, China.
Organic thermoelectric (TE) materials are of great interest for researchers in waste heat recovery, especially for waste heat harvesting at near room temperature. Significant progress has been achieved in terms of their figure of merit () values recently, which has presented new insights into the development of organic TE materials. For numerous practical applications of thermoelectric generators, where waste heat is unlimited and cost negligible, the primary goal has been switched to achieve high power output density rather than improving their heat-to-electricity conversion efficiency.
View Article and Find Full Text PDFProc IEEE Int Symp Biomed Imaging
May 2024
Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States.
Physics-driven deep learning (PD-DL) has become a powerful tool for accelerated MRI. Recent developments have also developed unsupervised learning for PD-DL, including self-supervised learning. However, at very high acceleration rates, such approaches show performance deterioration.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!