98%
921
2 minutes
20
In this work, we present a multi-mode resonator based on SU-8 polymer and experimentally verify that the resonator showed mode discrimination can be used as a sensor with high performance. According to field emission scanning electron microscopy (FE-SEM) images, the fabricated resonator shows sidewall roughness which is canonically considered to be undesirable after a typical development process. In order to analyze the effect of sidewall roughness, we conduct the resonator simulation considering the roughness under various conditions. Mode discrimination still occurs even in the presence of sidewall roughness. In addition, waveguide width controllable by UV exposure time effectively contributes to mode discrimination. To verify the resonator as a sensor, we perform a temperature variation experiment, which results in a high sensitivity of about 630.8 nm/RIU. This result shows that the multi-mode resonator sensor fabricated via a simple process is competitive with other single-mode waveguide sensors.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/OE.489554 | DOI Listing |
Front Mol Biosci
August 2025
Department of Rheumatology and Immunology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Background: The clinical differentiation between obstetric antiphospholipid syndrome (OAPS) and undifferentiated connective tissue disease (UCTD) presents significant diagnostic challenges. This study employs metabolomics to investigate metabolic reprogramming patterns in OAPS and UCTD, aiming to identify potential biomarkers for early diagnosis.
Methods: Using LC-MS-based metabolomics, we analyzed serum profiles from 40 OAPS patients (B1), 30 OAPS + UCTD patients (B2), 27 UCTD patients (B3), and 30 healthy controls (A1).
Neuroimage
September 2025
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, PR China; Brain-Computer Interface & Brain-Inspired Intelligence Key Laboratory of Sichuan Province, University of Electronic S
Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels.
View Article and Find Full Text PDFNeuroscience
September 2025
College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security., Xi'an 710054, China.
Motor imagery (MI) based brain-computer interfaces (BCI) decode neural activity to generate command outputs. However, the limited number of distinguishable commands in traditional MI-BCI systems restricts practical applications. To overcome this limitation, we propose a multi-character classification framework based on Electroencephalography (EEG) signals.
View Article and Find Full Text PDFBiology (Basel)
August 2025
Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya 572000, China.
Although previous studies have investigated the reproductive (performance and mode) and lifespan traits of parthenogenetic , ploidy level has not been considered. Four parthenogenetic lineages, i.e.
View Article and Find Full Text PDF