Critical inverse wavelength

The method used to derive polymer concentration profiles from neutron reflectivity experiments is based on the sensitivity of the reflectivity to the isotopic composition, when one approaches total reflection. It requires a very accurate knowledge of the critical inverse wavelength, ka associated with each isotopic bulk composition. 

Aside from univariate linear regression models, inverse MLR models are probably the simplest types of models to construct for a process analytical application. Simplicity is of very high value in PAC, where ease of automation and long-term reliability are critical. Another advantage of MLR models is that they are rather easy to communicate to the customers of the process analytical technology, since each individual X-variable used in the equation refers to a single wavelength (in the case of NIR) that can often be related to a specific chemical or physical property of the process sample. 

In fact, the broadening of particle size distribution can interfere sufficiently in the absorbance spectrum near the onset to deviate gap measurements significantly by the onset of extrapolation. Pesika and collaborators [77,78] proposed the inverse observation, i.e., particle size distribution by absorbance spectra measurements. The absorbance A at any wavelength A in the quantum regime is related to the total volume of particles with radius greater than or equal to the size corresponding to the onset of absorption, in a diluted concenttation limit (absorbance will occur continuously since the critical size is reached). For spherical particles, assuming that the absorption coefQcient is independent of particle size, we have... 

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