Modeling absorption and scattering cross sections of the multilayer nanoshells in the near infrared spectrum region

2016 ◽  
Author(s):  
T. O. Bulavinets ◽  
I. Ya. Yaremchuk ◽  
A. Y. Kotsko ◽  
Ya. V. Bobitski
Keyword(s):  
Infrared Spectrum ◽  
Cross Sections ◽  
Near Infrared ◽  
Scattering Cross ◽  
Spectrum Region ◽  
Scattering Cross Sections ◽  
Near Infrared Spectrum ◽  
Multilayer Nanoshells

The Electron‐Excited Mid‐Ultraviolet to Near‐Infrared Spectrum of H2: Cross Sections and Transition Probabilities

2008 ◽  
Vol 177 (1) ◽  
pp. 388-407 ◽  
Author(s):  
Alejandro Aguilar ◽  
Joseph M. Ajello ◽  
Rao S. Mangina ◽  
Geoffrey K. James ◽  
Hervé Abgrall ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Cross Sections ◽  
Near Infrared ◽  
Transition Probabilities ◽  
Near Infrared Spectrum

Absolute inner-shell cross section determination for EELS analysis

1988 ◽  
Vol 46 ◽  
pp. 534-535
Author(s):  
P.A. Crozier
Keyword(s):  
Cross Section ◽  
Cross Sections ◽  
Absolute Cross Section ◽  
Specimen Thickness ◽  
Mass Thickness ◽  
Scattering Cross ◽  
Before And After ◽  
Estimated Error ◽  
Scattering Cross Sections ◽  
Electron Microscopist

Absolute inelastic scattering cross sections or mean free paths are often used in EELS analysis for determining elemental concentrations and specimen thickness. In most instances, theoretical values must be used because there have been few attempts to determine experimental scattering cross sections from solids under the conditions of interest to electron microscopist. In addition to providing data for spectral quantitation, absolute cross section measurements yields useful information on many of the approximations which are frequently involved in EELS analysis procedures. In this paper, experimental cross sections are presented for some inner-shell edges of Al, Cu, Ag and Au.Uniform thin films of the previously mentioned materials were prepared by vacuum evaporation onto microscope cover slips. The cover slips were weighed before and after evaporation to determine the mass thickness of the films. The estimated error in this method of determining mass thickness was ±7 x 107g/cm2. The films were floated off in water and mounted on Cu grids.

scholarly journals Nanopatterned metallic transparent electrodes for the near-infrared spectrum

AIP Advances ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 045005
Author(s):  
Aliaksandr Hubarevich ◽  
Mikita Marus ◽  
Yauhen Mukha ◽  
Aliaksandr Smirnov ◽  
Xiao Wei Sun
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Transparent Electrodes ◽  
Near Infrared Spectrum

An electrically driven whispering gallery polariton microlaser

Nanoscale ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 5448-5459
Author(s):  
Mingming Jiang ◽  
Peng Wan ◽  
Kai Tang ◽  
Maosheng Liu ◽  
Caixia Kan
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Whispering Gallery ◽  
Near Infrared Spectrum

An electrically driven whispering gallery polariton microlaser composed of a ZnO:Ga microwire and a p-GaAs template was fabricated. Its working characteristics of polariton lasing in the near-infrared spectrum were demonstrated.

scholarly journals Towards fully nonperturbative computations of inelastic ℓN scattering cross sections from lattice QCD

2020 ◽  
Vol 102 (11) ◽  
Author(s):  
Hidenori Fukaya ◽  
Shoji Hashimoto ◽  
Takashi Kaneko ◽  
Hiroshi Ohki
Keyword(s):  
Lattice Qcd ◽  
Cross Sections ◽  
Scattering Cross ◽  
Scattering Cross Sections

scholarly journals Combining ADF-EDX scattering cross-sections for elemental quantification of nanostructures

2021 ◽  
Vol 27 (S1) ◽  
pp. 600-602
Author(s):  
Zezhong Zhang ◽  
Annick De Backer ◽  
Ivan Lobato ◽  
Sandra Van Aert ◽  
Peter Nellist
Keyword(s):  
Cross Sections ◽  
Scattering Cross ◽  
Scattering Cross Sections

scholarly journals Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

2021 ◽  
pp. 000370282110279
Author(s):  
Justyna Grabska ◽  
Krzysztof B. Beć ◽  
Sophia Mayr ◽  
Christian W. Huck
Keyword(s):  
Infrared Spectrum ◽  
Regression Models ◽  
Near Infrared ◽  
Computing Time ◽  
Black Pepper ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Nature ◽  
Chemical Information ◽  
Near Infrared Spectrum

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.

Functional Classification of Feed Items in Pampa Grassland, Based on Their Near-Infrared Spectrum

2020 ◽  
Vol 73 (3) ◽  
pp. 358-367
Author(s):  
Júlio Cezar Rebés Azambuja Filho ◽  
Paulo Cesar de Faccio Carvalho ◽  
Olivier Jean François Bonnet ◽  
Denis Bastianelli ◽  
Magali Jouven
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Functional Classification ◽  
Near Infrared Spectrum
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