Laboratory-based grain-shape models for simulating dust infrared spectra

Context. Analysis of thermal dust emission spectra for dust mineralogy and physical grain properties depends on comparison spectra, which are either laboratory-measured infrared extinction spectra or calculated extinction cross sections based on certain grain models. Often, the agreement between these two kinds of spectra, if available, is not yet satisfactory because of the strong influence of the grain morphology on the spectra. Aims. We investigate the ability of the statistical light-scattering model with a distribution of form factors (DFF) to reproduce measured infrared dust extinction spectra for particles that are small compared to the wavelength, i.e. in the size range of 1 μm and smaller. Methods. We take advantage of new experimental spectra measured for free particles dispersed in air with accompanying information on the grain morphology. For the calculations, we used DFFs that were derived for aggregates of spherical grains, as well as for compact grain shapes corresponding to Gaussian random spheres. In addition we used a fitting algorithm to obtain the best-fit DFFs for the various laboratory samples. In this way we can independently derive information on the shape of the grains from their infrared spectra. Results. With the DFF model, we achieve an adequate fit of the experimental IR spectra. The differences in the IR band profiles between the spectra of particulates with different grain shapes are simply reflected by different DFFs. Irregular particle shapes require a DFF similar to that of a Gaussian Random Sphere with $\sigma=0.3$, whereas roundish grain shapes are best fitted with that of a fractal aggregate of $D_{\rm f}=2.4$–1.8. The fitted DFFs generally reproduce the measured spectral shapes quite well. For anisotropic materials, different DFFs are needed for the different crystallographic axes. The implications of this finding are discussed. Conclusions. The use of this model could be a step forward toward more realistic comparison data in infrared spectral analysis of thermal dust emission spectra, provided that these spectra are dominated by emission from submicron grains