Remote detection of liquids by Raman line imaging

2008 ◽  
Author(s):  
Chris R. Howle ◽  
Christopher D. Dyer ◽  
Kurt J. Baldwin ◽  
Richard J. White
Keyword(s):  
Raman Line ◽  
Remote Detection ◽  
Line Imaging

Investigation of CO2 sorption in molten polymers at high pressures using Raman line imaging

Polymer ◽  
2013 ◽  
Vol 54 (2) ◽  
pp. 812-818 ◽  
Author(s):  
Oliver Sven Knauer ◽  
Maria Giovanna Pastore Carbone ◽  
Andreas Braeuer ◽  
Ernesto Di Maio ◽  
Alfred Leipertz
Keyword(s):  
Raman Line ◽  
High Pressures ◽  
Co2 Sorption ◽  
Molten Polymers ◽  
Line Imaging

Hyperspectral Raman Line Imaging of Syndiotactic Polystyrene Crystallinity

1998 ◽  
Vol 52 (10) ◽  
pp. 1264-1268 ◽  
Author(s):  
Shuliang L. Zhang ◽  
Jerilyn A. Pezzuti ◽  
Michael D. Morris ◽  
Aruna Appadwedula ◽  
Chang-Meng Hsiung ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Raman Line ◽  
The Other ◽  
Moderate Intensity ◽  
Syndiotactic Polystyrene ◽  
Raman Intensity ◽  
Structure Information ◽  
Intensity Image ◽  
Line Imaging ◽  
Noise Factors

The crystallinity of syndiotactic polystyrene (sPS) is studied by hyperspectral Raman line imaging. Images of a 140 × 1200 μm region of an sPS test piece containing 39 200 pixels/image were generated from spectra taken over the wavenumber interval between 300 and 875 cm−1. The spectral region includes the moderate-intensity crystallinity-sensitive bands in the 770 800 cm−1 region, as well as other useful but weaker marker bands. Factor analysis was used to extract structure information from the set of spectra. Four non-noise factors were extracted; two were assigned to crystalline and amorphous sPS and the other two to background. Score images of the crystalline and amorphous sPS factors were used to visualize distribution of those species. The results were compared to the integrated Raman intensity image for the 773 cm−1 band of crystalline sPS.

Practical applications in UV Raman line imaging of water vapor

1994 ◽  
Author(s):  
Peter A. DeBarber ◽  
Cecil F. Hess ◽  
Robert W. Pitz ◽  
Sastri P. Nandula ◽  
Thomas M. Brown III ◽  
...  
Keyword(s):  
Water Vapor ◽  
Raman Line ◽  
Practical Applications ◽  
Uv Raman ◽  
Line Imaging

Hyperspectral Raman Microscopic Imaging Using Powell Lens Line Illumination

1998 ◽  
Vol 52 (9) ◽  
pp. 1145-1147 ◽  
Author(s):  
Kenneth A. Christensen ◽  
Michael D. Morris
Keyword(s):  
Raman Line ◽  
Signal To Noise Ratio ◽  
Numerical Aperture ◽  
Acquisition Time ◽  
Charge Coupled Device ◽  
Illumination System ◽  
Image Acquisition Time ◽  
Intensified Charge Coupled Device ◽  
Line Imaging

The design and characterization of a simple and robust hyperspectral Raman line imaging illumination system with the use of a Powell lens is reported. The generated line uniformity is ±5% of total intensity with a laser power density of 12 mW/μm2 at the sample with a 50×/0.8 NA (numerical aperture) objective. Similar results were obtained by using other objectives. Linewidths remained near the diffraction limit for all objectives tested. Significant decreases in image acquisition time are also reported with the use of a Powell lens-illuminated hyperspectral Raman line imaging microscope equipped with an intensified charge-coupled device (CCD) detector. Hyperspectral images (100 × 350 pixels) were acquired in as little as 8 with a corresponding signal-to-noise ratio of 24.

Hyperspectral Raman Line Imaging of an Aluminosilicate Glass

1998 ◽  
Vol 52 (1) ◽  
pp. 64-69 ◽  
Author(s):  
Nancy L. Jestel ◽  
Jeremy M. Shaver ◽  
Michael D. Morris
Keyword(s):  
Factor Analysis ◽  
Raman Line ◽  
The Other ◽  
Aluminosilicate Glass ◽  
Nonbridging Oxygen ◽  
Silica Network ◽  
Data Set ◽  
Dental Restorations ◽  
Line Imaging ◽  
Significant Factors

An aluminosilicate glass, which is a model for glass formulations used as dental restorations, was examined by hyperspectral Raman line imaging. The data set consisted of more than 30000 spectra, which were analyzed by using factor analysis. Nine score images were constructed from the nine significant factors identified. Three factors represent convolutions of noise, background, and offset. The other six factors represent Raman spectra of different bonding environments of the silicate tetrahedron. Three of those factors contain narrow Raman features. These are associated with a fully polymerized silica network, with a silicate tetrahedron with one nonbridging oxygen, and with an alumina-related inclusion or a silicate tetrahedron with two nonbridging oxygens. The last three significant factors contain broad Raman bands representing continua of slightly different bonding environments of silicate tetrahedra with 0–4 nonbridging oxygens. The score images reveal that the glass, although not homogeneous, has few regions with discrete heterogeneities. The different bonding networks commingle and could be interconnected.

18Fluoro-choline PET/CT is a useful tool for patients with primary hyperparathyroidism negative at first-line imaging localization tecniques

2020 ◽  
Author(s):  
Matteo Apicella ◽  
Stefano Bola ◽  
Duccio Volterrani ◽  
Laura Mazoni ◽  
Gianluca Frustaci ◽  
...  
Keyword(s):  
Primary Hyperparathyroidism ◽  
First Line ◽  
Choline Pet ◽  
Pet Ct ◽  
Line Imaging
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