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1. Classification of Zirconium-Rich Engineered and Natural Nano Particles using Single Particle ICP-TOFMS

   https://doi.org/10.1039/D4JA00094C  

   Karkee, Hark; Kyte, Chloe; Gundlach-Graham, Alexander (May 2024, Journal of Analytical Atomic Spectrometry)

   Zirconium (Zr) is an important material in the field of ceramics, dentistry, and nuclear energy. It is also present in particulate form in our environment and can come from naturally occurring minerals such as zircon (ZrSiO4) or from anthropogenic sources such as zirconia (ZrO₂). In this study, we present the detection and classification of Zr-particles at the individual particle level by using single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS). Neat suspensions of engineered zirconia particles (Zr-eng) and natural zircon particles (Zr-nat) were analyzed by spICP-TOFMS, and a decision tree-based classification strategy was developed to distinguish the particle types based on their multi-elemental compositions. In both Zr-eng and Zr-nat particles, the only well-correlated element with Zr was hafnium (Hf), with Zr:Hf mass ratios converging to 47:1 and 75:1 for Zr-eng and Zr-nat, respectively. The detection of Hf along with Zr is indicative of both Zr-eng and Zr-nat particle types; however, the Zr:Hf mass ratios are too similar to be used to distinguish between individual nano- and sub-micron Zr-eng and Zr-nat particles. Instead, Zr-nat particles can be distinguished from Zr-eng particles based on the detection of minor elements, such as iron, yttrium, lanthanum, cerium, and thorium, along with Hf in the Zr-nat particles. With our classification scheme, we demonstrate true-positive classification rates of 40% and 80% for Zr-eng and Zr-nat particle types, respectively. False-positive classification of Zr-nat as Zr-eng was below 2%. We validate our classification scheme by classifying the Zr-particles in controlled mixtures of Zr-nat and Zr-eng particles. In these mixtures, Zr-eng particles are classified at particle-number concentrations (PNCs) down to 49-times lower than that of Zr-nat particles and across a PNC range of 3 orders of magnitude. 

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2. Nixonite, Na2Ti6O13, a new mineral from a metasomatized mantle garnet pyroxenite from the western Rae Craton, Darby kimberlite field, Canada

   https://doi.org/10.2138/am-2019-7023  

   Anzolini, Chiara; Wang, Fei; Harris, Garrett A.; Locock, Andrew J.; Zhang, Dongzhou; Nestola, Fabrizio; Peruzzo, Luca; Jacobsen, Steven D.; Pearson, D.Graham (September 2019, American Mineralogist)

   Abstract Nixonite (IMA 2018-133), ideally Na2Ti6O13, is a new mineral found within a heavily metasomatized pyroxenite xenolith from the Darby kimberlite field, beneath the west-central Rae Craton, Canada. It occurs as microcrystalline aggregates, 15 to 40 μm in length. Nixonite is isostructural with jeppeite, K2Ti6O13, with a structure consisting of edge- and corner-shared titanium-centered octahedra that enclose alkali-metal ions. The Mohs hardness is estimated to be between 5 and 6 by comparison to jeppeite, and the calculated density is 3.51(1) g/cm3. Electron microprobe wavelength-dispersive spectroscopic analysis (average of 6 points) yielded: Na2O 6.87, K2O 5.67, CaO 0.57, TiO2 84.99, V2O3 0.31, Cr2O3 0.04, MnO 0.01, Fe2O3 0.26, SrO 0.07, total 98.79 wt%. The empirical formula, based on 13 O atoms, is: (Na1.24K0.67Ca0.06)Σ1.97(Ti5.96V0.023Fe0.018)Σ6.00O13 with minor amounts of Cr and Mn. Nixonite is monoclinic, space group C2/m, with unit-cell parameters a = 15.3632(26) Å, b = 3.7782(7) Å, c = 9.1266(15) Å, β = 99.35(15)°, and V = 522.72(1) Å3, Z = 2. Based on the average of seven integrated multi-grain diffraction images, the strongest diffraction lines are [dobs in Å (I in %) (hkl)]: 3.02 (100) (310), 3.66 (75) (110), 7.57 (73) (200), 6.31 (68) (201), 2.96 (63) (311), 2.96 (63) (203), and 2.71 (62) (402). The five main Raman peaks of nixonite, in order of decreasing intensity, are at 863, 280, 664, 135, and 113 cm–1. Nixonite is named after Peter H. Nixon, a renowned scientist in the field of kimberlites and mantle xenoliths. Nixonite occurs within a pyroxenite xenolith in a kimberlite, in association with rutile, priderite, perovskite, freudenbergite, and ilmenite. This complex Na-K-Ti-rich metasomatic mineral assemblage may have been produced by a fractionated Na-rich kimberlitic melt that infiltrated a mantle-derived garnet pyroxenite and reacted with rutile during kimberlite crystallization. 

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3. Experimental Measurement of Enhanced and Hindered Particle Settling in Turbulent Gas‐Particle Suspensions, and Geophysical Implications

   https://doi.org/10.1029/2022JB025809  

   Penlou, Baptiste; Roche, Olivier; Manga, Michael; van den Wildenberg, Siet (March 2023, Journal of Geophysical Research: Solid Earth)

   Abstract The dynamics of geophysical dilute turbulent gas‐particles mixtures depends to a large extent on particle concentration, which in turn depends predominantly on the particle settling velocity. We experimentally investigate air‐particle mixtures contained in a vertical pipe in which the velocity of an ascending air flux matches the settling velocity of glass particles. To obtain local particle concentrations in these mixtures, we use acoustic probing and air pressure measurements and show that these independent techniques yield similar results for a range of particle sizes and particle concentrations. Moreover, we find that in suspensions of small particles (78 μm) the settling velocity increases with the local particle concentration due to the formation of particle clusters. These clusters settle with a velocity that is four times faster than the terminal settling velocity of single particles, and they double settling speeds of the suspensions. In contrast, in suspensions of larger particles (467 μm) the settling velocity decreases with increasing particle concentration. Although particle clusters are still present in this case, the settling velocity is decreased by 30%, which is captured by a hindered settling model. These results suggest an interplay between hindered settling and cluster‐induced enhanced settling, which in our experiments occur respectively at Stokes number O(100) and O(1). We discuss implications for volcanic plumes and pyroclastic currents. Our study suggests that clustering and related enhanced or hindered particle settling velocities should be considered in models of volcanic phenomena and that drag law corrections are needed for reliable predictions and hazard assessment. 

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4. TiO ₂ exposure alters transition metal ion quota in Rhodococcus ruber GIN-1

   https://doi.org/10.1039/c9mt00305c  

   Gallo, Annastassia D.; Zierden, Mark R.; Profitt, Lauren A.; Jones, Kayleigh E.; Bonafide, Christopher P.; Valentine, Ann M. (January 2020, Metallomics)

   null (Ed.)

   After exposure to micron-sized TiO 2 particles, anatase and/or rutile, Rhodococcus ruber GIN-1 accumulates an increased concentration (2.2 ± 0.2 mg kg −1 ) of mobilized Ti into its biomass with concomitant decreases in cellular biometals Fe, Zn, and possibly Mn, while levels of Cu and Al are unaffected. 

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5. A transfer learning approach for improved classification of carbon nanomaterials from TEM images

   https://doi.org/10.1039/D0NA00634C  

   Luo, Qixiang; Holm, Elizabeth A.; Wang, Chen (January 2021, Nanoscale Advances)

   The extensive use of carbon nanomaterials such as carbon nanotubes/nanofibers (CNTs/CNFs) in industrial settings has raised concerns over the potential health risks associated with occupational exposure to these materials. These exposures are commonly in the form of CNT/CNF-containing aerosols, resulting in a need for a reliable structure classification protocol to perform meaningful exposure assessments. However, airborne carbonaceous nanomaterials are very likely to form mixtures of individual nano-sized particles and micron-sized agglomerates with complex structures and irregular shapes, making structure identification and classification extremely difficult. While manual classification from transmission electron microscopy (TEM) images is widely used, it is time-consuming due to the lack of automation tools for structure identification. In the present study, we applied a convolutional neural network (CNN) based machine learning and computer vision method to recognize and classify airborne CNT/CNF particles from TEM images. We introduced a transfer learning approach to represent images by hypercolumn vectors, which were clustered via K -means and processed into a Vector of Locally Aggregated Descriptors (VLAD) representation to train a softmax classifier with the gradient boosting algorithm. This method achieved 90.9% accuracy on the classification of a 4-class dataset and 84.5% accuracy on a more complex 8-class dataset. The developed model established a framework to automatically detect and classify complex carbon nanostructures with potential applications that extend to the automated structural classification for other nanomaterials. 

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