Cavity enhanced absorption spectroscopy (CEAS) is a promising technique for studying chemical reactions due to its desirable characteristics of high sensitivity and fast time response by virtue of the increased path length and relatively short photon residence time inside the cavity. Offaxis CEAS (OA-CEAS) is particularly suited for the shock tube applications as it is insensitive to slight misalignments, and cavity noise is suppressed due to non-overlapping multiple reflections of the probe beam inside the cavity. Here, OA-CEAS is demonstrated in the mid-IR region at 1310.068 cm-1 to monitor trace concentrations of hydrogen peroxide (H2O2). This particular probe frequency was chosen to minimize interference from other species prevalent in combustion systems and in the atmosphere. The noise-equivalent detection limit is found to be 3.25 x 10 −5 cm −1 , and the gain factor of the cavity is 131. This corresponds to a detection limit of 74 ppm of H2O2 at typical high-temperature combustion conditions (1200 K and 1 atm) and 12 ppm of H2O2 at ambient conditions (296 K and 1 atm). To our knowledge, this is the first successful application of the OA-CEAS technique to detect H2O2 which is vital species in combustion and atmospheric science.
Spatial distribution of the light scattered by a disperse system of particles depends on their sizes, shapes, positions, etc., which can be used for experimental determination of the parameters mentioned. For stochastic systems with the particles’ sizes exceeding the radiation wavelength, most of the scattered radiation concentrates near the incident beam axis. In this small-angle approximation, the scattering pattern is especially simple and regular, which enables to develop efficient procedures for the disperse system investigation. We describe the algorithm for determination of the mean particle radius in the system with lognormal distribution of the particle sizes and negligible multiple scattering. The algorithm’s performance is demonstrated on the practical example of the “fog” generated by a gasoline injector. The ways are discussed for further algorithm generalization and its extension to a non-parametric analysis of disperse systems with a priori unknown form of the particle sizes’ distribution.
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