Integration of plasmonic nanostructures with fiber-optics-based neural probes enables label-free detection of molecular fingerprints via surface-enhanced Raman spectroscopy (SERS), and it represents a fascinating technological horizon to investigate brain function. However, developing neuroplasmonic probes that can interface with deep brain regions with minimal invasiveness while providing the sensitivity to detect biomolecular signatures in a physiological environment is challenging, in particular because the same waveguide must be employed for both delivering excitation light and collecting the resulting scattered photons. Here, a SERS-active neural probe based on a tapered optical fiber (TF) decorated with gold nanoislands (NIs) that can detect neurotransmitters down to the micromolar range is presented. To do this, a novel, nonplanar repeated dewetting technique to fabricate gold NIs with sub-10 nm gaps, uniformly distributed on the wide (square millimeter scale in surface area), highly curved surface of TF is developed. It is experimentally and numerically shown that the amplified broadband near-field enhancement of the high-density NIs layer allows for achieving a limit of detection in aqueous solution of 10 m for rhodamine 6G and 10 m for serotonin and dopamine through SERS at near-infrared wavelengths. The NIs-TF technology is envisioned as a first step toward the unexplored frontier of in vivo label-free plasmonic neural interfaces.
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http://dx.doi.org/10.1002/adma.202200902 | DOI Listing |
Anal Chim Acta
March 2025
Artificial Intelligence Research Center, Chang Gung University, Taoyuan, 333323, Taiwan; Department of Artificial Intelligence, College of Intelligent Computing, Chang Gung University, Taoyuan, 333323, Taiwan. Electronic address:
Background: In recent years, employing deep learning methods in the biosensing area has significantly reduced data analysis time and enhanced data interpretation and prediction accuracy. In some SPR fields, research teams have further enhanced detection capabilities using deep learning techniques. However, the application of deep learning to spectroscopic surface plasmon resonance (SPR) biosensors has not been reported.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, 14115-194, Iran.
With the increasing global attention to deep learning and the advancements made in applying convolutional neural networks in electromagnetics, we have recently witnessed the utilization of deep learning-based networks for predicting the spectrum and electromagnetic properties of structures instead of traditional tools like fully numerical-based methods. In this study, a Convolutional Neural Network (CNN is proposed for predicting spoof surface plasmon polaritons, enabling the examination of the absorption spectrum of metallic multilevel-grating structures (MMGS) and designing various sensor devices and absorbers in the shortest time possible. To expedite the training process of this network, a semi-analytical method of rigorous coupled-wave analysis (RCWA) enhanced with the fast Fourier factorization (FFF) technique has been employed, significantly reducing the data generation time for training.
View Article and Find Full Text PDFStem Cell Res Ther
January 2025
Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, China.
Background: Understanding how enteric neural crest cells (ENCCs) differentiate into neurons is crucial for neurogenesis therapy and gastrointestinal disease research. This study explores how magnesium ions regulate the glycolytic pathway to enhance ENCCs differentiation into neurons.
Materials And Methods: We used polymerase chain reaction, western blot, immunofluorescence, and multielectrode array techniques to assess magnesium ions' impact on ENCCs differentiation.
ACS Omega
January 2025
Nanotechnology, IoT and Applied Machine Learning Research Group, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh.
Nanoparticles embedded in polymer matrices play a critical role in enhancing the properties and functionalities of composite materials. Detecting and quantifying nanoparticles from optical images (fixed samples-in vitro imaging) is crucial for understanding their distribution, aggregation, and interactions, which can lead to advancements in nanotechnology, materials science, and biomedical research. In this article, we propose an ensembled deep learning approach for automatic nanoparticle detection and oligomerization quantification in a polymer matrix for optical images.
View Article and Find Full Text PDFACS Sens
January 2025
Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden.
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model for accelerated sensing (LEMAS) speeds up the response of an optical plasmonic hydrogen sensor by up to a factor of 40 and eliminates its intrinsic pressure dependence in an environment emulating the inert gas encapsulation of large-scale hydrogen installations by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware.
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