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Strain sensors based on conductive elastomers face challenges like baseline drift and noise due to inherent viscoelasticity and weak electrode interfaces under dynamic strains. Herein, a synergistic structure with biphasic hierarchical networks and stable electrode interfaces is proposed to address these issues. The sensor employs a multilayer structure with polydimethylsiloxane (PDMS) substrate, carbon nanotube-doped PDMS (CNT-PDMS), and Ag film. Electrodes are fixed using a rigid island reinforced mortise and tenon joint formed with PDMS and CNT-PDMS. The Ag film dominates resistance during release, significantly reducing baseline drift. Strain-insensitive electrode interfaces further reduce baseline drift and noise. This optimized design ensures 99.999% resistance recovery without delay, even at high-speed (800 mm/min) and large (80%) strains. The sensor exhibits a high gauge factor of 55442, low detection limit (0.02%), and excellent stability (5000 cycles). With the designed algorithms, the single-channel sensor achieves 98.2% decoding accuracy for various gestures, demonstrating great potential for wearable electronics.
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http://dx.doi.org/10.1021/acs.nanolett.5c00327 | DOI Listing |
Nat Methods
September 2025
Department of Radiology, Michigan State University, East Lansing, MI, USA.
Concurrent recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) signals reveals cross-scale neurovascular dynamics crucial for explaining fundamental linkages between function and behaviors. However, MRI scanners generate artifacts for EEG detection. Despite existing denoising methods, cabled connections to EEG receivers are susceptible to environmental fluctuations inside MRI scanners, creating baseline drifts that complicate EEG signal retrieval from the noisy background.
View Article and Find Full Text PDFJ Clin Epidemiol
August 2025
Monash Centre for Health Research and Implementation (MCHRI), Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. Electronic address:
Objectives: This study aimed to follow best practice by temporally evaluating existing GDM prediction models, updating them where needed, and comparing the temporal evaluation performance of the ML-based models with that of regression-based models.
Study Design And Setting: We utilised new data for the temporal validation dataset with 12,722 singleton pregnancies at the Monash Health Network from 2021 to 2022. The Monash GDM Logistic Regression (LR) model with six categorical variables (version 2) and the Monash GDM Machine Learning model (version 3), along with an extended LR GDM model (version 3), each with eight categorical and continuous variables, were evaluated.
J Appl Clin Med Phys
September 2025
Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: The poor soft tissue resolution of four-dimensional computed tomography (4D-CT) limits its utility in delineating liver cancer target volumes.
Purpose: To compare the consistency between four-dimensional magnetic resonance imaging (4D-MRI) using T1-weighted (T1w) radial stack-of-stars (SOS) gradient echo (GRE) sequences and 4D-CT in assessing tumor motion and morphology, for defining internal target volume in liver tumor radiotherapy.
Materials And Methods: Position and geometric accuracy and the impact of baseline drift between 4D-MRI (using T1w radial SOS GRE sequence) and 4D-CT were evaluated using a motion phantom.
Sensors (Basel)
August 2025
Department of Environmental and Biological Chemistry, Chungbuk National University, Cheongju 28644, Chungbuk, Republic of Korea.
Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible-near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels.
View Article and Find Full Text PDFSensors (Basel)
August 2025
School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
To address the decline in the localization accuracy of magnetic adhesion wall-climbing robots operating on large steel structures, caused by visual occlusion, sensor drift, and environmental interference, this study proposes a simulation-based multi-sensor fusion localization method that integrates an Inertial Measurement Unit (IMU), Wheel Odometry (Odom), and Ultra-Wideband (UWB). An Extended Kalman Filter (EKF) is employed to integrate IMU and Odom measurements through a complementary filtering model, while a geometric residual-based weighting mechanism is introduced to optimize raw UWB ranging data. This enhances the accuracy and robustness of both the prediction and observation stages.
View Article and Find Full Text PDF