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The influence of the ambient temperature and the thickness of the paint coating on the magnetic-noise parameters is investigated, in the diagnostics of steel structures. It is shown that nondestructive magnetic-noise diagnostics may be used to monitor the state of load-bearing structural elements in situ at any temperature, in extreme conditions.
Electrically conductive adhesive layers were deposited on top of aerospace grade carbon fiber reinforced plastic (CFRP) panels using a small-scale 3D printer. Polylactic acid (PLA) filaments with copper filler (CU-PLA) and graphene filler (GO-PLA) were used to print around 0.7 mm thick electrically conductive layer on top of CFRP panels. 3D printed polymeric layers were tested for their effectiveness as a lightning strike protection (LSP) material by subjected to a simulated lightning strike. A painted, electrically non-conductive unprotected panel was also tested for comparison. In the case of the CU-PLA protected sample, a high electrical conductivity proved to be useful in fast dissipation of the lightning current. Fast current dissipation helped to reduce the resistive heating after a lightning strike. Thermography and high-speed camera analysis were employed to study the heat generation and current dissipation during the lightning strike. These results established that a useful Faraday Cage was applied via additive manufacturing successfully. This work shows the successful application of 3D printing for producing LSP technologies, with future work aimed at investigating optimal printable electrically conductive thermoset material candidates.
Market-driven deployment of inexpensive (but intermittent) renewable energy sources, such as wind and solar, in the electric power grid necessitates grid-stabilization through energy storage systems Redox flow batteries (RFBs), with their rated power and energy decoupled (resulting in a sub-linear scaling of cost), are an inexpensive solution for the efficient electrochemical storage of large amounts of electrical energy. Titanium-based RFBs, first developed by NASA in the 1970s, are an interesting albeit less examined chemistry and are the focus of the present review. Ti, constituting 0.6% of the Earth’s crust and an ingredient in inexpensive white paints, is amongst the few elements (V and Mn being some others) which exhibit multiple soluble oxidation states in aqueous electrolytes. Further, the very high (approaching 10 M) solubility of Ti in low pH solutions suggests the possibility of developing exceptionally high energy density aqueous Redox Flow Batteries systems. With these advantages in mind, we present the state-of the-art in Ti-RFBs with a focus on Ti/Mn, Ti/Fe and Ti/Ce couples and systems that use Ti as an additive (such as Ti/V/Mn). The inherent advantages of inexpensive Ti actives and relatively high energy density is contrasted with potential side-reactions resulting in reduced energy efficiency. Technological pathways are presented with a view to overcoming critical bottlenecks and a vision is presented for the future development of Ti-RFBs.
The domestic fossil feedstock in recent years is shifting towards light hydrocarbons due to abundance of shale gas from hydraulic fracturing. This shift induces a need for greater flexibility in both new and existing processing plants to produce consumer products (polymers, paints, lubricants, etc.) from new feedstocks. The oxidative catalytic reactions operate at milder conditions than the processing of feedstocks through steam cracking. The conversion of light feedstocks (C3 and shorter hydrocarbons) to high value chemicals through highly selective catalysts in the presence of oxygen plays a crucial role in eliminating wastes, reducing greenhouse gas emissions and lowering market prices. Among all catalysts for light hydrocarbon processing through oxidation reactions, bulk mixed metal oxides such as MoVTe(Sb)NbO x catalysts are the most promising due to their performance under favorable reaction conditions (temperature, pressure, etc). Here, state-of-the-art in situ/operando techniques along with transient kinetics can revolutionize the development of catalysts by providing information about the nature of active sites, intermediates and kinetics under realistic industrial conditions. Only through detailed understanding of these catalyst behaviors can new synthesis methods be developed that will improve reactivity, selectivity and lifetimes of these catalysts. In this review, dynamic changes of this mixed oxide catalyst during the reaction (such as changes in surface composition, oxidation states, acidity, etc) are discussed mainly from knowledge and insights obtained from these in situ/operando approaches. The most common oxidation reactions driven by the MoVTeNbO x catalysts and studied under operando/in situ conditions to be discussed here are: (1) oxidative dehydrogenation of light alkanes (ethane and propane), (2) propane ammoxidation to acrylonitrile and (3) selective oxidation of propane to acrylic acid.
We investigated copper electrodes deposited onto a BaZr0.7Ce0.2Y0.1O3-δ (BZCY72) proton-conducting membrane via a novel electroless plating method, which resulted in significantly improved performance when compared to a traditional painted copper electrode. The increased performance was examined with a multiscale multitechnique characterization method including time-of-flight secondary-ion mass spectroscopy (TOF-SIMS), transmission electron microscopy (TEM), scanning spreading-resistance microscopy (SSRM), and atom-probe tomography (APT). Through this method, we observed that a palladium catalyst layer alloys with the copper electrode. We also explored the nature of a non-coking-induced carbon-rich phase that may be involved with the improved performance of the electrode.
Characterizing the mechanical behavior of rocks plays a crucial role to optimize the fracturing process in unconventional reservoirs. However, due to the intrinsic anisotropy and heterogeneity in unconventional resources, fracture process prediction remains the most significant challenge for sustainable and economic hydrocarbon production. During the deformation tracking under compression, deploying conventional methods (strain gauge, extensometer, etc.) is insufficient to measure the deformation since the physical attachment of the device is restricted to the size of the sample, monitoring limited point-wise deformation, producing difficulties in data retrieval, and a tendency to lose track in failure points, etc. Where conventional methods are limited, the application of digital image correlation (DIC) provides detailed and additional information of strain evolution and fracture patterns under loading. DIC is an image-based optical method that records an object with a camera and monitors the random contrast speckle pattern painted on the facing surface of the specimen. To overcome the existing limitations, this paper presents numerical modeling of Brazilian disc tests under quasi-static conditions to understand the full-field deformation behaviors and finally, it is validated by DIC. As the direct tensile test has limitations in sample preparation and test execution, the Brazilian testing principle is commonly used to evaluate indirectly the tensile strength of rocks. The two-dimensional numerical model was built to predict the stress distribution and full-field deformation on Brazilian disc under compression based on the assumptions of a homogenous, isotropic and linear elastic material. The uniaxial compression test was conducted using the DIC technique to determine the elastic properties of Spider Berea sandstone, which were used as inputs for the simulation model. The model was verified by the analytical solution and compared with the digital image correlation. The numerical simulation results showed that the solutions matched reasonably with the analytical solutions where the maximum deviation of stress distribution was obtained as 14.59%. The strain evolution (normal and shear strains) and displacements along the central horizontal and vertical planes were investigated in three distinguishable percentages of peak loads (20%, 40%, and 90%) to understand the deformation behaviors in rock. The simulation results demonstrated that the strain evolution contours consistently matched with DIC generated contours with a reasonable agreement. The changes in displacement along the central horizontal and vertical planes showed that numerical simulation and DIC generated experimental results were repeatable and matched closely. In terms of validation, Brazilian testing to measure the indirect tensile strength of rocks is still an issue of debate. The numerical model of fracture propagation supported by digital image correlation from this study can be used to explain the fracturing process in the homogeneous material and can be extended to non-homogeneous cases by incorporating heterogeneity, which is essential for rock mechanics field applications.
This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.
This study analyzes the prevalence of elevated blood lead levels (BLLs) in children across Chicagoland zip codes from 2019 to 2021, linking them to socioeconomic, environmental, and racial factors. Wilcoxon tests and generalized additive model (GAM) regressions identified economic hardship, reflected in per capita income and unemployment rates, as a significant contributor to increased lead poisoning (LP) rates. Additionally, LP rates correlate with the average age of buildings, particularly post the 1978 lead paint ban, illustrating policy impacts on health outcomes. The study further explores the novel area of land surface temperature (LST) effects on LP, finding that higher nighttime LST, indicative of urban heat island effects, correlates with increased LP. This finding gains additional significance in the context of anthropogenic climate change. When these factors are combined with the ongoing expansion of urban territories, a significant risk exists of escalating LP rates on a global scale. Racial disparity analysis revealed that Black and Hispanic/Latino populations face higher LP rates, primarily due to unemployment and older housing. The study underscores the necessity for targeted public health strategies to address these disparities, emphasizing the need for interventions that cater to the unique challenges of these at-risk communities.
Assuring a proper environment for the fulfillment of professional activities is one of the Sustainable Development Goals and is contemplated in the One Health approach assumed by the World Health Organization. This particular study is applied to an often neglected sector of our society—the conservators/restorers—despite the many health issues reported by these professionals. Three different specialties (textiles, paintings and wood sculpture) and locations were selected for evaluation by placement of electrostatic dust cloths. After treatment of the samples, bacterial and fungal contamination were assessed, as well as mycotoxin determination, the presence of azole-resistant strains and cytotoxicity of the microorganisms encountered. Bacteria were only present in one of medias used and showed relatively low numbers. The highest level of contamination by fungi was identified in one of the textiles settings. The textile area also showed the highest variability for fungi. Aspergillus sp. are one indicator of possible environmental issues, and A. sections Fumigati and Circumdati were particularly relevant in two of the settings and identified in all of them. No mycotoxins were detected and the large majority of the fungi identified were non-cytotoxic. Overall, these can be considered low-contaminated environments but attention should be given to the Aspergillus sp. contamination. Additional studies are needed not only to make these results more robust, but also to test if the environmental sampling alone is the best approach in a setting where there is very little movement and dust displacement and where professionals are in very close proximity to the artefacts being treated, which may suggest the existence of a micro-atmosphere worth evaluating and comparing to the obtained results.
Lignin is the by-product of pulp and paper industries and bio-refining operations. It is available as the leading natural phenolic biopolymer in the market. It has chromophore functional groups and can absorb a broad spectrum of UV light in range of 250–400 nm. Using lignin as a natural ingredient in sunscreen cream, transparent film, paints, varnishes and microorganism protection has been actively investigated. Both in non-modified and modified forms, lignin provides enhancing UV protection of commercial products with less than a 10% blend with other material. In mixtures with other synthetic UV blockers, lignin indicated synergic effects and increased final UV blocking potential in compare with using only synthetic UV blocker or lignin. However, using lignin as a UV blocker is also challenging due to its complex structure, polydispersity in molecular weight, brownish color and some impurities that require more research in order to make it an ideal bio-based UV blocker.
We present the first robust detection of H i 21 cm emission in the blue compact galaxy Haro 11 using the 100 m Robert C. Byrd Green Bank Telescope (GBT). Haro 11 is a luminous blue compact galaxy with emission in both Ly α and the Lyman continuum. We detect (5.1 ± 0.7 × 10{sup 8}) M {sub ⊙} of H i gas at an assumed distance of 88 Mpc, making this galaxy H i deficient compared to other local galaxies with similar optical properties. Given this small H i mass, Haro 11 has an elevated M{sub H2}/ M{sub Hi} ratio and a very low gas fraction compared to most local galaxies, and contains twice as much mass in ionized hydrogen as in neutral hydrogen. The H i emission has a linewidth of 71 km s{sup −}1 and is offset 60 km s{sup −1} redward of the optical line center. It is undergoing a starburst after a recent merger that has elevated the star formation rate, and will deplete the gas supply in <0.2 Gyr. Although this starburst has elevated the star formation rate (SFR) compared to galaxies with similar H i masses and line widths, Haro 11 matches a trend of lower gas fractions toward higher SFRs and is below the general trend of increasing H i mass with increasing luminosity. Taken together, our results paint Haro 11 as a standard low-mass galaxy that is undergoing an unusually efficient star formation episode.
We present a study of the recently discovered compact stellar system Triangulum II. From observations conducted with the DEIMOS spectrograph on Keck II, we obtained spectra for 13 member stars that follow the CMD features of this very faint stellar system and include two bright red giant branch stars. Tri II has a very negative radial velocity (〈v{sub r}〉=−383.7{sub −3.3}{sup +3.0} km s{sup −1}) that translates to 〈v{sub r,gsr}〉≃−264 km s{sup −1} and confirms it is a Milky Way satellite. We show that, despite the small data set, there is evidence that Tri II has complex internal kinematics. Its radial velocity dispersion increases from 4.4{sub −2.0}{sup +2.8} km s{sup −1} in the central 2{sup ′} to 14.1{sub −4.2}{sup +5.8} km s{sup −1} outwards. The velocity dispersion of the full sample is inferred to be σ{sub vr}=9.9{sub −2.2}{sup +3.2} km s{sup −1}. From the two bright RGB member stars we measure an average metallicity 〈[Fe/H]〉=−2.6±0.2, placing Tri II among the most metal-poor Milky Way dwarf galaxies. In addition, the spectra of the fainter member stars exhibit differences in their line widths that could be the indication of a metallicity dispersion in the system. All these properties paint a complex picture for Tri II, whose nature and current state are largely speculative. The inferred metallicity properties of the system however lead us to favor a scenario in which Tri II is a dwarf galaxy that is either disrupting or embedded in a stellar stream.
Using the Atacama Large Millimeter/submillimeter Array, we have made the first high spatial and spectral resolution observations of the molecular gas and dust in the prototypical blue compact dwarf galaxy II Zw 40. The {sup 12}CO(2−1) and {sup 12}CO(3−2) emission is clumpy and distributed throughout the central star-forming region. Only one of eight molecular clouds has associated star formation. The continuum spectral energy distribution is dominated by free–free and synchrotron; at 870 μm, only 50% of the emission is from dust. We derive a CO-to-H{sub 2} conversion factor using several methods, including a new method that uses simple photodissocation models and resolved CO line intensity measurements to derive a relationship that uniquely predicts α{sub co} for a given metallicity. We find that the CO-to-H{sub 2} conversion factor is 4–35 times that of the Milky Way (18.1–150.5 M{sub ⊙}(Kkms{sup −1}pc{sup 2}){sup −1}). The star formation efficiency of the molecular gas is at least 10 times higher than that found in normal spiral galaxies, which is likely due to the burst-dominated star formation history of II Zw 40 rather than an intrinsically higher efficiency. The molecular clouds within II Zw 40 resemble those in other strongly interacting systems like the Antennae: overall they have high size–linewidth coefficients and molecular gas surface densities. These properties appear to be due to the high molecular gas surface densities produced in this merging system rather than to increased external pressure. Overall, these results paint a picture of II Zw 40 as a complex, rapidly evolving system whose molecular gas properties are dominated by the large-scale gas shocks from its ongoing merger.
Dating back to the first observations of the on-disk corona, there has been a qualitative link between the photosphere’s magnetic network and enhanced transition-temperature plasma emission. These observations led to the development of a general model that describes emission structures through the partitioning of the atmospheric volume with different magnetic loop geometries that exhibit different energetic equilibria. Does the internetwork produce transition-temperature emission? What fraction of network flux connects to the corona? How does quiet Sun emission compare with low-activity Sun-like stars? In this work, we revisit the canonical model of the quiet Sun, with high-resolution observations from the Interface Region Imaging Spectrograph ( IRIS ) and HMI in hand, to address those questions. We use over 900 deep exposures of Si iv 1393 Å from IRIS along with nearly simultaneous HMI magnetograms to quantify the correlation between transition-temperature emission structures and magnetic field concentrations through a number of novel statistics. Our observational results are coupled with analysis of the Bifrost MHD model and a large-scale potential field model. Our results paint a complex portrait of the quiet Sun. We measure an emission signature in the distant internetwork that cannot be attributed to network contribution. We find that the dimmest regions of emission are not linked to the local vertical magnetic field. Using the MHD simulation, we categorize the emission contribution from cool mid-altitude loops and high-altitude coronal loops and discuss the potential emission contribution of spicules. Our results provide new constraints on the coupled solar atmosphere so that we can build on our understanding of how dynamic thermal and magnetic structures generate the observed phenomena in the transition region.
COCOA-PUFS is an energy-diverse, time-domain study of the ultra-fast spinning, heavily spotted, yellow giant FK Comae Berenices (FK Com: HD117555; G4 III). This single star is thought to be a recent binary merger, and is exceptionally active by measure of its intense ultraviolet (UV) and X-ray emissions, and proclivity to flare. COCOA-PUFS was carried out with the Hubble Space Telescope in the UV (1200–3000 Å), using mainly its high-performance Cosmic Origins Spectrograph, but also high precision Space Telescope Imaging Spectrograph; Chandra X-ray Observatory in the soft X-rays (0.5–10 keV), utilizing its High-Energy Transmission Grating Spectrometer; together with supporting photometry and spectropolarimetry in the visible from the ground. This is an introductory report on the project. FK Com displayed variability on a wide range of timescales over all wavelengths during the week-long main campaign, including a large X-ray flare; “super-rotational broadening” of the far-ultraviolet “hot lines” (e.g., Si iv 1393 Å; 8 × 10{sup 4} K) together with chromospheric Mg ii 2800 Å and C ii 1335 Å (1–3 × 10{sup 4} K); large Doppler swings suggestive of bright regions alternately on advancing and retreating limbs of the star; and substantial redshifts of the epoch-average emission profiles. These behaviors paint a picture of a highly extended, dynamic, hot (∼10 MK) coronal magnetosphere around the star, threaded by cooler structures perhaps analogous to solar prominences and replenished continually by surface activity and flares. Suppression of angular momentum loss by the confining magnetosphere could temporarily postpone the inevitable stellar spindown, thereby lengthening this highly volatile stage of coronal evolution.
The lack of arm tracers, especially remote tracers, is one of the most difficult problems preventing us from studying the structure of the Milky Way. Fortunately, with its high-sensitivity CO survey, the Milky Way Imaging Scroll Painting (MWISP) project offers such an opportunity. Since completing about one-third of its mission, an area of l = [100, 150]°, b = [−3, 5]° has nearly been covered. The Outer arm of the Milky Way first clearly revealed its shape in the second galactic quadrant in the form of molecular gas—this is the first time that the Outer arm has been reported in such a large-scale mapping of molecular gas. Using the 115 GHz {sup 12}CO(1–0) data of MWISP at the LSR velocity ≃[−100, −60] km s{sup −1} and in the area mentioned above, we have detected 481 molecular clouds in total, and among them 332 (about 69%) are newly detected and 457 probably belong to the Outer arm. The total mass of the detected Outer arm clouds is ∼3.1 × 10{sup 6} M {sub ⊙}. Assuming that the spiral arm is a logarithmic spiral, the pitch angle is fitted as ∼13.°1. Besides combining both the CO data from MWISP and the 21 cm H i data from the Canadian Galactic Plane Survey (CGPS), the gas distribution, warp, and thickness of the Outer arm are also studied.
It is generally assumed that galaxies are a bimodal population in both star formation and structure; star-forming galaxies are disks, while passive galaxies host large bulges or are entirely spheroidal. Here we test this scenario by presenting a full census of the kinematic morphologies of a volume-limited sample of galaxies in the local universe extracted from the MaNGA galaxy survey. We measure the integrated stellar line-of-sight velocity to velocity dispersion ratio (V/σ) for 4574 galaxies in the stellar mass range 9.75 < logM $\star$ [M ⊙ ] < 11.75. We show that at fixed stellar mass, the distribution of V/σ is not bimodal, and that a simple separation between fast and slow rotators is oversimplistic. Fast rotators are a mixture of at least two populations, referred to here as dynamically cold disks and intermediate systems, with disks dominating in both total stellar mass and number. When considering star-forming and passive galaxies separately, the star-forming population is almost entirely made up of disks, while the passive population is mixed, implying an array of quenching mechanisms. Passive disks represent ~30% (both in number and mass) of passive galaxies, nearly a factor of two higher than that of slow rotators, reiterating that these are an important population for understanding galaxy quenching. These results paint a picture of a local universe dominated by disky galaxies, most of which become somewhat less rotation-supported upon or after quenching. While spheroids are present to a degree, they are certainly not the evolutionary end point for the majority of galaxies.
Over the last two decades, around 300 quasars have been discovered at z ≳ 6, yet only one has been identified as being strongly gravitationally lensed. We explore a new approach—enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion—which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) the preselection of the candidates, based on their spectral energy distributions (SEDs), using catalog-level photometry; and (ii) relative probability calculations of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy–quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of θ E ≤ 1''. Visual inspection is then performed for sources with CNN scores of P lens > 0.1, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs, which can overcome the veto limitations of primarily dropout-based SED selection approaches.