Tip-enhanced Raman spectroscopy (TERS) is a promising label-free super-resolving imaging technique, and the electric field gradient of nanofocusing plays a role in TERS performance. In this paper, we theoretically investigated the enhancement and manipulation of the electric field gradient in a bottom-illumination TERS configuration through a tightly focused perfect radially polarized beam (PRPB). Improvement and manipulation in electric field enhancement and field gradient of the gap-plasmon mode between a plasmonic tip and a virtual surface plasmons (SPs) probe are achieved by adjusting the ring radius of the incident PRPB. Our results demonstrate that the method of optimizing the ring radius of PRPB is to make the illumination angle of incident light as close to the surface plasmon resonance (SPR) excitation angle as possible. Under the excitation of optimal parameters, more than 10 folds improvement of field enhancement and 3 times of field gradient of the gap-plasmon mode is realized compared with that of the conventional focused RPB. By this feat, our results indicate that such a method can further enhance the gradient Raman mode in TERS. We envision that the proposed method, to achieve the dynamic manipulation and enhancement of the nanofocusing field and field gradient, can be more broadly used to control light-matter interactions and extend the reach of tip-enhanced spectroscopy.
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http://dx.doi.org/10.1364/OE.460394 | DOI Listing |
Sensors (Basel)
January 2025
Departamento de Geografía, Facultad de Ciencias, Universidad de la República, Montevideo 4225, Uruguay.
Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran.
Forward modeling the magnetic effects of an inferred source is the basis of magnetic anomaly inversion for estimating subsurface magnetization parameters. This study uses numerical least-squares Gauss-Legendre quadrature (GLQ) integration to evaluate the magnetic potential, anomaly, and gradient components of a cylindrical prism element. Relative to previous studies, it quantifies for the first time the magnetic gradient components, enabling their applications in the interpretation of cylindrical bodies.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia.
Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability.
View Article and Find Full Text PDFPlants (Basel)
December 2024
Gansu Liancheng National Nature Reserve Administration, Lanzhou 730300, China.
Topography has an important influence on plant-soil relationships. However, research on plant-soil relationships in alpine grassland at the slope aspect and slope position scales is currently inadequate. In this paper, based on the topographic and geomorphological characteristics of the study area, alpine grassland with typical slope aspect and slope position conditions was selected as the research object.
View Article and Find Full Text PDFNat Commun
January 2025
Hangzhou Institute of Technology, Xidian University, Hangzhou, 311231, China.
Edge detection is one of the most essential research hotspots in computer vision and has a wide variety of applications, such as image segmentation, target detection, and other high-level image processing technologies. However, efficient edge detection is difficult in a resource-constrained environment, especially edge-computing hardware. Here, we report a low-power edge detection hardware system based on HfO-based ferroelectric field-effect transistor, which is one of the most potential non-volatile memories for energy-efficient computing.
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