Abstract
Time-varying linear regression via flexible least squares is used to determine temperature-dependent kinetic parameters during low-pressure, steady-state, temperature-programmed desorption from catalytic surfaces. The flexible least squares approach optimizes time-varying parameters by minimizing dynamic and measurement discrepancies between a linear theoretical model and experimental data using linear regression. The effectiveness of this approach is demonstrated by calculation of accurate temperature-dependent activation energies, preexponential factors, and differential conversion functions for the evolution of 3-methyl-2-oxetanone (β-lactone) during the selective oxidation of isobutane over aluminum phosphomolybdates.
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