Numerical study of the effects of polymeric shell on plasmonic resonance of gold nanorods

Author(s):  
A. Akouibaa ◽  
A. Derouiche ◽  
H. Redouane

Gold nanorods (GNRs) have great potential widespread applications in biomedical imaging, drug delivery and photothermal therapy due to unique surface plasmon resonance (SPR) ranging from visible to near infrared (NIR) region, facile synthesis and easy functionalization. The frequency of SPR is not only determined by the nature of the metal, but also by a number of other parameters, such as particle size and shape, the presence of a capping shell on the particle surface, or the dielectric properties of the surrounding medium. In this study, use is made of finite element method (FEM) for the calculation of the complex effective permittivity of the gold-core nanorod coated with polymer shells. Such a permittivity allows the determination of the absorption spectrum of these clothed nanoparticles. A simple scheme based on FEM is developed, which enables us to compute the optical properties, such as the effective dielectric function and absorption cross-section of coated-GNRs which are embedded in a dielectric matrix. The influences of the aspect ratio, shell thickness and dielectric constant of the shell on the longitudinal and transversal resonances modes are investigated.

2013 ◽  
Vol 41 (12) ◽  
pp. 1928
Author(s):  
Zong-Liang CHI ◽  
Miao-Miao WANG ◽  
Xiao-Dong CONG ◽  
Shao-Guang LIU ◽  
Bao-Chang CAI

2021 ◽  
Vol 13 (11) ◽  
pp. 2045
Author(s):  
Anaí Caparó Bellido ◽  
Bradley C. Rundquist

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.


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