Spectral emissivity and the relation of true temperatures and brightness temperatures of platinum
Robert E. Stephens
JOSA, Vol. 29, Issue 4, pp. 158-161 (1939)
R. E. Stephens, “Spectral emissivity and the relation of true temperatures and brightness temperatures of platinum,” J. Opt. Soc. Am. 29, 158-161 (1939)
San Diego CA, USA –Surface Optics’ ET10 measures emissivity values in two most commonly used spectral regions, 3 to 5 and 8 to 12 microns.
Its main application is to produce emissivity values for the infrared cameras.
Advanced IR cameras require the input of an emissivity value for accurate temperature calculations. The emissivity values obtained from tables can be far from real leading to large temperature uncertainties.
The ET10 can be used in the lab or in the field and on small or large objects. With the ET10 one can measure emissivity of any surface in just a few seconds.
The spectral emissivity of the anode of a carbon arc
Applied Optics, Vol. 7, Issue 3, pp. 461-
K. Schurer, “The spectral emissivity of the anode of a carbon arc,” Appl. Opt. 7, 461- (1968)
Noninvasive Polymer Reaction Monitoring by Infrared Emission Spectroscopy with Multivariate Statistical Modeling
Randy J. Pell, James B. Callis, and Bruce R. Kowalski
Applied Spectroscopy, Vol. 45, Issue 5, pp. 808-818 (1991)
“Infrared absorption and emission spectroscopy have been used to monitor the curing of a commercial paint product. Principal component analysis of the absorption data indicates that three factors are needed to explain the observed spectral/temporal variance. The interpretation of this finding in terms of changes in the physical state of the reaction mixture is discussed. A similar analysis of the emission data proved more difficult due to a nonlinear concentration/response relationship. A linearization step based on an approximate theoretical model is suggested. The absorption, linearized emittance, and raw emittance data are fit to a two-step sequential rate model using multivariate nonlinear optimization and error estimates derived by Monte Carlo calculations. Better agreement of the model parameters between the absorbance and emittance data is found after linearization, but it is found that linearization introduces large errors in the nonlinear parameter estimates. Comparisons of model parameters for the raw emittance data at different temperatures are made.”
R. J. Pell, J. B. Callis, and B. R. Kowalski, “Noninvasive Polymer Reaction Monitoring by Infrared Emission Spectroscopy with Multivariate Statistical Modeling,” Appl. Spectrosc. 45, 808-818 (1991)
IR Emission Spectroscopy of Molten Salts and Other Liquids Using Thick Samples as Reference
J. Hvistendahl, E. Rytter, and H. A. Øye
Applied Spectroscopy, Vol. 37, Issue 2, pp. 182-187 (1983)
“The IR emittance of liquids relative to a blackbody is dependent on the reflectivity at the surface of the sample. This dependency leads to distortions in the bandshapes except when the absorption coefficient or the sample thickness is very low. The use of an opaque (i.e. very thick) sample as a reference eliminates the distortions in the bandshapes. A new emittance ?* = (emission of a thin sample)/(emission of an opaque sample) has been introduced. A theoretical analysis as well as experimental work on chloroaluminate melts demonstrate that the emittance ?* gives a better representation of the ideal sample property of interest, i.e., the internal transmittance of the sample, than the usual emittance with a blackbody as a reference.”
J. Hvistendahl, E. Rytter, and H. A. Øye, “IR Emission Spectroscopy of Molten Salts and Other Liquids Using Thick Samples as Reference,” Appl. Spectrosc. 37, 182-187 (1983)