Oxygen deficiency (O-vacancy) contributes to the photoefficiency of TiO2 semiconductors by generating electron rich active sites. In this paper, the dispersion of O-vacancies in both bulk and surface of anatase and rutile phases was computationally investigated. The results showed that the O-vacancies dispersed in single- and double-cluster forms in the anatase and rutile phases, respectively, in both bulk and surface. The distribution of the O-vacancies was (roughly) homogeneous in anatase, and heterogenous in rutile bulk. The O-vacancy formation energy, width of defect band, and charge distribution indicated the overlap of the defect states in the rutile phase and thus eased the formation of clusters. Removal of the first and the second oxygen atoms from the rutile surface took less energy than the anatase one, which resulted in a higher deficiency concentration on the rutile surface. However, these deficiencies formed one active site per unit cell of rutile. On the other hand, the first O-vacancy formed on the surface and the second one formed in the subsurface of anatase (per unit cell). Supported by previous studies, we argue that this distribution of O-vacancies in anatase (surface and subsurface) could potentially create more active sites on its surface.

Abstract. Mineral dust particles dominate aerosol mass in the atmosphere and directly modify Earth's radiative balance through absorption and scattering. This radiative forcing varies strongly with mineral composition, yet there is still limited knowledge on the mineralogy of atmospheric dust. In this study, we performed X-ray diffraction (XRD) and reflectance spectroscopy measurements on 37 different dust deposition samples collected as airfall in an urban setting to determine mineralogy and the relative proportions of minerals in the dust mixture. Most commonly, XRD has been used to characterize dust mineralogy; however, without prior special sample preparation, this technique is less effective for identifying poorly crystalline or amorphous phases. In addition to XRD measurements, we performed visible and short-wave infrared (VSWIR) reflectance spectroscopy for these natural dust samples as a complementary technique to determine mineralogy and mineral abundances. Reflectance spectra of dust particles are a function of a nonlinear combination of mineral abundances in the mixture. Therefore, we used a Hapke radiative transfer model along with a linear spectral mixing approach to derive relative mineral abundances from reflectance spectroscopy. We compared spectrally derived abundances with those determined semi-quantitatively from XRD. Our results demonstrate that total clay mineral abundances from XRD are correlated with those from reflectance spectroscopy and follow similar trends; however, XRD underpredicts the total amount of clay for many of the samples. On the other hand, calcite abundances are significantly underpredicted by SWIR compared to XRD. This is caused by the weakening of absorption features associated with the fine particle size of the samples, as well as the presence of dark non-mineral materials (e.g., asphalt) in these samples. Another possible explanation for abundance discrepancies between XRD and SWIR is related to the differing sensitivity of the two techniques (crystal structure vs. chemical bonds). Our results indicate that it is beneficial to use both XRD and reflectance spectroscopy to characterize airfall dust because the former technique is good at identifying and quantifying the SWIR-transparent minerals (e.g., quartz, albite, and microcline), while the latter technique is superior for determining abundances for clays and non-mineral components.

Mineral dust aerosols are soil particles entrained into the atmosphere by wind, primarily from arid and semi-arid regions of the world (Gillies, 2013). Once suspended, dust interacts with visible and short-wave infrared radiation from the sun, as well as long-wave radiation emitted from Earth (

Abstract. Mineral dust particles dominate aerosol mass in the atmosphere and directly modify Earth’s radiative balance through absorption and scattering. This radiative forcing varies strongly with mineral composition, yet there is still limited knowledge on the mineralogy of atmospheric dust. In this study, we performed X-ray diffraction (XRD) and reflectance spectroscopy measurements on 37 different atmospheric dust samples collected as airfall in an urban setting to determine mineralogy and the relative proportions of minerals in the dust mixture. Most commonly, XRD has been used to characterize dust mineralogy; however, without prior special sample preparation, this technique is less effective for identifying poorly crystalline or amorphous phases. In addition to XRD measurements, we performed visible, near-infrared, and short-wave infrared (VNIR/SWIR) reflectance spectroscopy for these natural dust samples as a complementary technique to determine minerology and mineral abundances. Reflectance spectra of dust particles are a function of a nonlinear combination of mineral abundances in the mixture. Therefore, we used a Hapke radiative transfer model along with a linear spectral mixing approach to derive relative mineral abundances from reflectance spectroscopy. We compared spectrally derived abundances with those determined semi-quantitatively from XRD. Our results demonstrate that total clay mineral abundances from XRD are correlated with those from reflectance spectroscopy and follow similar trends; however, XRD underpredicts the total amount of clay for many of the samples. On the other hand, calcite abundances are significantly underpredicted by SWIR compared to XRD. This is caused by the weakening of absorption features associated with the fine particle size of the samples, as well as the presence of dark non-mineral materials (e.g., asphalt) in these samples. Another possible explanation for abundance discrepancies between XRD and SWIR is related to the differing sensitivity of the two techniques (crystal structure vs chemical bonds). Our results indicate that it is beneficial to use both XRD and reflectance spectroscopy to characterize airfall dust, because the former technique is good at identifying and quantifying the SWIR-transparent minerals (e.g., quartz, albite, and microcline), while the latter technique is superior for determining abundances for clays and non-mineral components.