Gaussian Band Shape

2016 ◽  
Author(s):  
S. E. Braslavsky
Keyword(s):  
Band Shape ◽  
Gaussian Band

Anomalous Bands in Infrared Difference Spectra Due to Frequency Shifts

1982 ◽  
Vol 36 (5) ◽  
pp. 539-544 ◽  
Author(s):  
Chris W. Brown ◽  
Patricia F. Lynch ◽  
Robert J. Obremski
Keyword(s):  
Molecular Interactions ◽  
Solvent Effects ◽  
Experimental Error ◽  
Difference Spectra ◽  
Band Shape ◽  
Frequency Shifts ◽  
Gaussian Band

Artifacts appearing in infrared difference spectra can be caused by the lack of wavelength reproducibility and/or molecular interactions such as solvent effects. These artifacts appear as derivatives in difference spectra. Experimental spectra containing a number of bands with a variety of bandwidths were shifted over a range of frequencies and subtracted from the original spectra to intentionally produce anomalous bands. Models based on both Lorentzian and Gaussian band shapes were used to fit the observed data, and it was found that either band shape could be used to adequately predict the observed results within the experimental error of the measurements. Moreover, the models can be used to determine the values of the frequency shifts from the peak-to-peak differences of artifacts.

Gaussian Band Shape Derived from Finite-Rate Oscillator Growth

1987 ◽  
Vol 41 (1) ◽  
pp. 139-141
Author(s):  
P. Torkington
Keyword(s):  
Time Dependence ◽  
Frequency Spectrum ◽  
Growth Rates ◽  
Classical Model ◽  
Decay Function ◽  
Band Shape ◽  
Finite Rate ◽  
Rate Of Decay ◽  
Band Contour ◽  
Gaussian Band

The accepted classical model for derivation of the frequency spectrum from the time-dependence of oscillator activity is considered. The exponential decay function giving rise to a Lorentzian band is modified to allow for a finite rate of growth of the oscillator, the resulting frequency spectrum is obtained, and the form normalized to unit band half-width is compared to the equivalent Lorentzian and Gaussian bands. It is shown that as the rate of oscillator growth decreases from infinity to one approximating the rate of decay, the resulting band contour changes from Lorentzian to near-Gaussian. At sufficiently fast growth-rates the band closely approximates a linear combination of Lorentzian and Gaussian.

Gaussian Band Shape

2016 ◽  
Author(s):  
S. E. Braslavsky ◽  
K. N. Houk
Keyword(s):  
Band Shape ◽  
Gaussian Band

Method of calculating band shape for molecular electronic spectra

1998 ◽  
Vol 19 (7) ◽  
pp. 781-796 ◽  
Author(s):  
Greg M. Pearl ◽  
M. C. Zerner ◽  
Anders Broo ◽  
John McKelvey
Keyword(s):  
Electronic Spectra ◽  
Molecular Electronic ◽  
Band Shape

Use of Multivariate Analysis to Determine Temperature from Low-Resolution Infrared Spectra of Carbon Dioxide

2000 ◽  
Vol 54 (2) ◽  
pp. 197-201 ◽  
Author(s):  
Michael P. Szczepanski ◽  
Augustus W. Fountain
Keyword(s):  
Carbon Dioxide ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Industrial Applications ◽  
Low Resolution ◽  
Band Shape ◽  
Temperature Spectra ◽  
Plume Temperature ◽  
Industrial Monitoring

The remote optical monitoring of gaseous contaminants is important for both military and industrial applications. An important parameter for quantifying chemical species and for predicting plume dynamics is the temperature. While in some industrial monitoring situations it may be practical to independently measure the temperature of stack emissions, for compliance monitoring and military chemical reconnaissance a remote optical means of estimating gas plume temperature is required. It was noticed that the band shape of low-resolution spectra of carbon dioxide in equilibrium with an exhaust plume was very sensitive to temperature. Spectra of carbon dioxide were acquired under controlled laboratory conditions in 5° increments from 20 to 200 °C. Various multivariate models were used to predict the temperature. It was found that partial least-squares (PLS) was unable to effectively model the simultaneous changes in amplitude and bandwidth with temperature. However, principal component regression (PCR) was found to be well correlated with temperature and allowed cross-validated prediction within 4% error.

scholarly journals Corrigendum to: The room temperature emission band shape of the lowest energy chlorophyll spectral form of LHCI (FEBS 27430)

FEBS Letters ◽  
2003 ◽  
Vol 549 (1-3) ◽  
pp. 181-181 ◽  
Author(s):  
Robert C Jennings ◽  
Flavio M. Garlaschi ◽  
Tomas Morosinotto ◽  
Enrico Engelmann ◽  
Giuseppe Zucchelli
Keyword(s):  
Emission Band ◽  
Room Temperature ◽  
Spectral Form ◽  
Band Shape
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