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.

We investigated the variation in the mineralogical composition of surface soil samples from a wide range of atmospheric dust aerosol generating regions. These soils likely have mineral compositions similar to dust aerosols. We measured the visible, shortwave infrared (VSWIR) and long wave infrared (LWIR) reflectance spectra, as well as LWIR transmission spectra and carried out linear spectral mixture modeling on the transmission data. The spectroscopically identified minerals were compared with the mineralogy previously obtained using X‐ray diffraction (XRD) and optical microscopy (OM). The results showed that VSWIR is very sensitive to iron‐bearing minerals and better identifies their presence and type compared to XRD and OM. Clay minerals and carbonates have multiple distinct absorptions features in SWIR but are often overlapping, limiting our ability to uniquely assign spectral features. LWIR reflectance easily distinguishes carbonates, even in trace amounts that were not identified with other techniques. LWIR reflectance spectra for silicates were more complicated to interpret. In some samples, we were able to identify Si‐O features diagnostic of common silicates; however, in most cases these features were absent, and an unusual new peak (∼7.4–7.9 μm) was observed. LWIR transmission is characterized by strong absorption that arises from overlapping features from multiple silicate minerals, but carbonates are readily identifiable. Spectroscopy is complementary to XRD and can help identify additional minerals in these soil samples. Our results can help improve global soil atlases and can be used to support interpretation and validation of data acquired by current and future remote sensing instruments.

We explored spectral diversity in infrared (IR, 4–25 μm) imaginary (k) and real (n) indices of refraction (optical constants) for 26 surface soils that originated from global dust entrainment regions. The k spectra were derived from optical transmission spectra of KBr pellets containing dispersed dust and the n spectra were estimated with the subtractive Kramers–Kronig (SKK) method. We compared k and n obtained by this study with previously published values. Our optical constants from the KBr technique are comparable in magnitude and spectral shape to those from previous studies that used dust aerosols re-suspended in a chamber. This suggests that the SKK method can offer a reliable mechanism for measuring IR spectra of both optical constants for soils. The soils used here exhibit additional spectral variability compared with past studies. We note that the appearance and the shift in positions of the strongest absorption peaks are due to combinations of silicates with overlapping absorption, rather than being related to a single mineral. We note that all dust optical constants have substantially lower magnitudes compared to those of pure minerals. Our results can improve estimates of mineral dust radiative effects and support quantification of surface mineral estimates using satellite and ground-based remote sensing.

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.

A document by Mohammad Reza Sadrian. Click on the document to view its contents.

Mineral dust particles originate from a variety of arid regions around the world. Mineral dust directly modifies the 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 global dust source regions. Previously, 65 surface soil samples were collected worldwide, sieved to < 38 μm fraction and analyzed using XRD, SEM and re-suspended to determine scattering and absorption coefficients at three visible wavelengths (Engelbrecht et al. Atmos. Chem. Phys. 16, 2016). This dust collection represents global surface soils with comparable mineral compositions to windblown dust. For this research, we measured spectra of 26 of these samples selected from major dust source regions with compositional diversity. We measured these samples using laboratory reflectance spectroscopy in the visible and near-infrared (0.4 to 2.5 μm, VNIR) and long-wave infrared (2.5 to 25 μm, LWIR). These data are relevant to satellite imaging spectrometers, but will particularly inform measurements planned for EMIT and SBG. We compared the measured spectra to standard spectral libraries to identify dominant materials and to compare and contrast these with the major minerals identified via XRD. VNIR spectral analysis detected diagnostic absorptions for minerals such kaolinite, calcite, hematite, and goethite. Common silicates, quartz and feldspar, are abundant in the majority of these samples and are expected to have diagnostic features in the LWIR. LWIR reflectance is strongly dependent on particle size (e.g. Salisbury et al., 1991), though the 0-74 μm grain size fraction of pure silicate minerals still show characteristic signatures between 8 and 10 µm. Surprisingly, diagnostic silicate features were not observed in many of the samples. We identified quartz absorptions between 4.8 and 5.4 μm and at 14.3 μm. We are still trying to understand why the fundamental vibrational features of silicates are obscured, but it may be related to multiple overlapping features in mixtures, grain coatings, or anomalous dispersion. LWIR spectra also revealed numerous diagnostic carbonate features, particularly those near 4, 5.6, and 11.4 μm. We also identified carbonate in several samples where it had not been identified with XRD.