Nonlinear enhancement is experimentally demonstrated by depositing graphene scraps from graphene ethanol dispersion onto a tapered microfiber. The enhancement of the nonlinearity is verified by observing the four-wave mixing (FWM) effect in the homemade graphene-deposited microfiber (GDMF). When the incident pump power is 24.2 dBm, the FWM conversion efficiency in the GDMF reaches -57.1 dB. Compared to the bare microfiber with the same dimensions, the conversion efficiency is improved by more than 3 dB. Our fabricated GDMF provides a simple way to enhance the fiber nonlinearity, and it will be suitable for nonlinear applications such as wavelength conversion and other optical signal processing operations.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/AO.56.005242 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multi-scale temporal and spatial variations of soil heat flux under varying riparian forests: From a day to a year.
J Environ Manage
December 2024
School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address:
This study delves into the multi-scale temporal and spatial variations of soil heat flux (G) within riparian zones and its correlation with net radiation (Rn) across six riparian woodlands in Shanghai, each characterized by distinct vegetation types. The objective is to assess the complex interrelations between G and Rn, and how these relationships are influenced by varying vegetation and seasons. Over the course of a year, data on G and Rn is collected to investigate their dynamics.
View Article and Find Full Text PDFStacking Engineering toward Giant Second Harmonic Generation in Twisted Graphene Superstructures.
J Am Chem Soc
December 2024
Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
The nonlinear optical response in graphene is finding increasing applications in nanophotonic devices. The activation and enhancement of second harmonic generation (SHG) in graphene, which is generally forbidden in monolayer and AB-stacked bilayer graphene due to their centrosymmetry, is of urgent need for nanophotonic applications. Here, we present a comprehensive study of SHG performance of twisted multilayer graphene structures based on stacking engineering.
View Article and Find Full Text PDFReservoir computing with generalized readout based on generalized synchronization.
Sci Rep
December 2024
Department of Applied Mathematics, Tokyo University of Science, Shinjuku, Tokyo, 162-8601, Japan.
Reservoir computing is a machine learning framework that exploits nonlinear dynamics, exhibiting significant computational capabilities. One of the defining characteristics of reservoir computing is that only linear output, given by a linear combination of reservoir variables, is trained. Inspired by recent mathematical studies of generalized synchronization, we propose a novel reservoir computing framework with a generalized readout, including a nonlinear combination of reservoir variables.
View Article and Find Full Text PDFAssociation between triglyceride to high-density lipoprotein cholesterol ratio and microalbuminuria in the Chinese population.
Sci Rep
December 2024
Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, No.1 Maoyuan South Rd, Nanchong, 637000, Sichuan Province, China.
The evidence for the association between the triglyceride (TG) to high-density lipoprotein cholesterol (HDL-c) ratio and the risk of developing microalbuminuria is still limited in the Chinese population. Therefore, our research will endeavor to explore the relationship between the two. The cross-sectional survey enrolled 32,877 general population from eight regional centers in China.
View Article and Find Full Text PDFDevelopment of a novel modeling framework based on weighted kernel extreme learning machine and ridge regression for streamflow forecasting.
Sci Rep
December 2024
Department of Civil, Construction and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND, 58108-6050, USA.
A precise streamflow forecast is crucial in hydrology for flood alerts, water quantity and quality management, and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain drawbacks, such as the need to optimize the appropriate predictors, the ability of the models to generalize across different time horizons, and the analysis of high-dimensional time series. This research aims to address these specific drawbacks by developing a novel framework for streamflow forecasting.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!