Diffuse Raman spectroscopy (DRS) allows subsurface molecular analysis of optically turbid samples. Numerical modeling of light propagation was used as a method for improving the design of an DRS instrument to maximize the signal to noise ratio () while ensuring safe laser exposure parameters required for measurements. Experimental validation of the model was performed on both phantom samples and disks implanted postmortem to mimic the typical response to foreign bodies (formation of a fibrotic capsule around an implant). A reduction of laser exposure of over 1500-fold was achieved over previous studies whilst maintaining the same Raman collection rates and reaching the safe power density of 3 mW/mm. The validation of this approach in a subcutaneous implant in a mouse cadaver showed a further improvement of 1.5-fold SNR, with a thickness limit of detection for the fibrotic layer of 23 µm, under the same acquisition times. In the animal body, a thickness limit of detection of 16 µm was achieved. These results demonstrate the feasibility of numerical model-based optimization for DRS, and that the technique can be improved sufficiently to be used for measurement of collagenous capsule formation as a result of the foreign body response in murine models.
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http://dx.doi.org/10.1364/BOE.512118 | DOI Listing |
J Biophotonics
December 2024
Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, USA.
We report on the development of a multimodal spectroscopy system, combining diffuse reflectance spectroscopy (DRS) and spatially offset Raman spectroscopy (SORS). A fiber optic probe was designed with spatially offset source-detector fibers to collect subsurface measurements for each modality, as well as ball lens-coupled fibers for superficial measurements. The system acquires DRS, zero-offset Raman spectroscopy (RS) and SORS with good signal-to-noise ratio.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
Department of Agriculture, Forestry and Bioresources, Seoul National University, Seoul, 08826, Republic of Korea.
Plants communicate through volatile organic compounds (VOCs), but real-time monitoring of VOCs for plant intercommunication is not practically possible yet. A nanobionic VOC sensor plant is created to study VOC-mediated plant intercommunication by incorporating surface-enhanced Raman scattering (SERS) nanosensors into a living plant. This sensor allows real-time monitoring of VOC with a sensitivity down to the parts per trillion level.
View Article and Find Full Text PDFACS Omega
December 2024
School of Chemical and Materials Engineering (SCME), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan.
Electrochemical sensing has shown great promise in monitoring neurotransmitter levels, particularly dopamine, essential for diagnosing neurological illnesses like Parkinson's disease. Such techniques are easy, cost-effective, and extremely sensitive. The present investigation discusses the synthesis, characterization, and potential use of a cysteine-grafted Cu MOF/ZnO/PANI nanocomposite deposited on the modified glassy carbon electrode surface for nonenzymatic electrochemical sensing of dopamine.
View Article and Find Full Text PDFACS Photonics
December 2024
School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT, U.K.
Tightly confined plasmons in metal nanogaps are highly sensitive to surface inhomogeneities and defects due to the nanoscale optical confinement, but tracking and monitoring their location is hard. Here, we probe a 1-D extended nanocavity using a plasmonic silver nanowire (AgNW) on mirror geometry. Morphological changes inside the nanocavity are induced locally using optical excitation and probed locally through simultaneous measurements of surface enhanced Raman scattering (SERS) and dark-field spectroscopy.
View Article and Find Full Text PDFAnal Chem
December 2024
Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China.
Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ineffective. Although feature extraction methods have been applied, the chemical meaning of the compressed variables is difficult to interpret, limiting their further applications. An unmixing autoencoder (UAE) was developed in this work for the separation of the mixed spectra in HSI.
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