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Keywords: EarthCARE, Saharan dust, validation, aerosol, airborne measurements
Published in RUNG: 09.09.2024; Views: 408; Downloads: 4
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Abstract: The NASA Convective Processes Experiment - Cabo Verde (CPEX-CV) field campaign took place in September 2022 out of Sal Island, Cabo Verde. A unique payload aboard the NASA DC-8 aircraft equipped with advanced remote sensing and in situ instrumentation, in conjunction with radiosonde launches and satellite observations, allowed CPEX-CV to target the coupling between atmospheric dynamics, marine boundary layer properties, convection, and the dust-laden Saharan Air Layer in the data-sparse tropical East Atlantic region. CPEX-CV provided measurements of African Easterly Wave environments, diurnal cycle impacts on convective lifecycle, and several Saharan dust outbreaks, including the highest dust optical depth observed by the DC-8 interacting with what would become Tropical Storm Hermine. Preliminary results from CPEX-CV underscore the positive impact of dedicated tropical East Atlantic observations on downstream forecast skill, including sampling environmental forcings impacting the development of several non-developing and developing convective systems such as Hurricanes Fiona and Ian. Combined airborne radar, lidar, and radiometer measurements uniquely provide near-storm environments associated with convection on various spatiotemporal scales and, with in situ observations, insights into controls on Saharan dust properties with transport. The DC-8 also collaborated with the European Space Agency to perform coordinated validation flights under the Aeolus spaceborne wind lidar and over the Mindelo ground site, highlighting the enhanced sampling potential through partnership opportunities. CPEX-CV engaged in professional development through dedicated team building exercises that equipped the team with a cohesive approach for targeting CPEX-CV science objectives and promoted active participation of scientists across all career stages.
Keywords: convective processesž, Saharan dust, aerosol, airborne measurements
Published in RUNG: 09.09.2024; Views: 401; Downloads: 5
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Abstract: The emergence of antimicrobial resistance due to antibiotics in the environment presents significant public health, economic, and societal risks. This study addresses the need for effective strategies to reduce antibiotic residues, focusing on ciprofloxacin degradation. Magnetic iron oxide nanoparticles (IO NPs), approximately 13 nm in size, were synthesized and functionalized with branched polyethyleneimine (bPEI) to obtain a positive charge. These IO-bPEI NPs were combined with negatively charged titanium dioxide NPs (TiO2@CA) to form magnetically photocatalytic IO-TiO2 nanocomposites. Characterization techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), infrared spectroscopy (IR), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), electrokinetic measurements, and a vibrating sample magnetometer (VSM), confirmed the successful formation and properties of the nanocomposites. The nanocomposites exhibited a high specific surface area, reduced mobility of photogenerated charge carriers, and enhanced photocatalytic properties. Testing the photocatalytic potential of IO-TiO2 with ciprofloxacin in water under UV-B light achieved up to 70% degradation in 150 min, with a degradation rate of 0.0063 min−1. The nanocomposite was magnetically removed after photocatalysis and successfully regenerated for reuse. These findings highlight the potential of IO-TiO2 nanocomposites for reducing ciprofloxacin levels in wastewater, helping curb antibiotic resistance.
Keywords: photocatalytic degradation, magnetic iron oxide-TiO2 nanocomposites, hetero-agglomeration, multifunctionality, antibiotic ciprofloxacin, antimicrobial resistance
Published in RUNG: 09.09.2024; Views: 437; Downloads: 6
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Abstract: Dark matter (DM), known to be a dominant matter component in the Universe, has been searched for extensively, yet remains undetected. One of the promising avenues of detecting a DM signal is to observe the so called ’DM sub-halos’ within our galaxy. These sub-halos, which are numerous within the Milky Way, are formed by the clustering of DM, as predicted by cosmological simulations, and most of them lack baryonic matter counterparts, making them challenging to detect. How- ever, the annihilation or decay of Weakly Interacting Massive Particles (WIMPs), a leading candidate for DM, within these sub-halos is expected to produce very high-energy (VHE) photons (called gamma-rays) at TeV energies, offering possible indirect DM detection. In this thesis, we focus on the Galactic Plane Survey (GPS) of the Cherenkov Tele- scope Array Observatory (CTAO), an upcoming ground-based gamma-ray obser- vatory, which promises unprecedented sensitivity and resolution in the detection of cosmic gamma-ray sources in the ∼ 30 GeV to ∼ 100 TeV energy range. As dark sub-halos are expected to appear as unidentified (point) sources in the CTAO GPS data, we employ a machine learning (ML)-based approach, the AutoSour- ceID framework, leveraging U-shaped networks (U-Nets) and Laplacian of Gaus- sian (LoG) filter, for automatic source detection and localization, and apply it to simulated GPS data. We establish detection thresholds for U-Nets trained on dif- ferently scaled counts (counts, square root or log of counts) and identify which approach offers best results (in terms of flux sensitivity and location accuracy). Our findings suggest that using log-scaled counts yields a factor of 1.7 lower flux threshold compared to counts alone. In addition, we compare our ML outcomes with traditional methods; however, this comparison is not straightforward, as ML and traditional approaches fundamentally differ in their methodologies and un- derlying assumptions. Nevertheless, The flux threshold obtained using log-scaled counts is comparable to that of the traditional likelihood-based detection method implemented in the Gammapy library, although further study is needed to estab- lish a more definitive comparison. These preliminary results also suggest that the flux threshold for detecting 90% of true sources with the ML approach is approx- imately two times lower than the sensitivity reported for the GPS in the CTAO publication. Although these results are not directly comparable due to differences in methodology, they hint that ML methods may offer superior performance in certain scenarios. Furthermore, we discuss the implications of our results on the sensitivity to DM sub-halos, improving it by a factor of 4, highlighting the possi- bility of detecting at least one sub-halo with a cross section approximately ⟨σv⟩ = 2.4 × 10−23 cm3 /s.
Keywords: Cherenkov Telescope Array Observatory, dark matter, sub-halos, machine learning, gamma-rays, master's thesis
Published in RUNG: 06.09.2024; Views: 477; Downloads: 7
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Abstract: X-ray absorption spectroscopy of gases is seldom used for structural analysis, except in as much as the extended X-ray absorption fine-structure (EXAFS) signal is used to monitor some specific molecular process. Gas spectroscopy, however, has been the main tool in the systematic study of higher-order photoabsorption processes, multielectron photoexcitations (MEEs), which were introduced with an experiment at the K edge of argon. Along with noble gases, where MEEs are studied in the pure form, free of the structural signal, measurements have been extended to monatomic metal vapours, although at the cost of considerable experimental difficulties. Several types of absorption cells have been devised, among them the ceramic double cell and the heat-pipe cell for experiments in the low-energy region. In a high-temperature oven with considerable technical refinement, edge profiles of nonvolatile metals were measured up to 2500 K. Edge profiles and MEEs, which are strongly dependent on electron correlation, provide sensitive tests of the theory of atomic and molecular structure.
Keywords: X-ray absorption spectroscopy, absorption cells, gas and vapour samples, multielectron photoexcitation, effects of electron correlation
Published in RUNG: 05.09.2024; Views: 495; Downloads: 4
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