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Novel isotopic labelling and imaging techniques are used to directly observe the autocatalytic reaction and facet-selective adsorption of Fe( ii ) onto individual hematite (Fe 2 O 3 ) crystallites.
Grain boundaries in noble metal catalysts have been identified as critical sites for enhancing catalytic activity in electrochemical reactions such as the oxygen reduction reaction. However, conventional methods to modify grain boundary density often alter particle size, shape, and morphology, obscuring the specific role of grain boundaries in catalytic performance. This study addresses these challenges by employing gold nanoparticle assemblies to control grain boundary density through the manipulation of nanoparticle collision frequency during synthesis. We demonstrate a direct correlation between increased grain boundary density and enhanced two-electron oxygen reduction reaction activity, achieving a significant improvement in both specific and mass activity. Additionally, the gold nanoparticle assemblies with high grain boundary density exhibit remarkable electrochemical stability, attributed to boron segregation at the grain boundaries, which prevents structural degradation. This work provides a promising strategy for optimizing the activity, selectivity, and stability of noble metal catalysts through precise grain boundary engineering.
The following conventional force limiting methods currently practiced in deriving force limiting specifications assume one-dimensional translation source and load apparent masses: Simple TDOF model; Semi-empirical force limits; Apparent mass, etc.; Impedance method. Uncorrelated motion of the mounting points for components mounted on panels and correlated, but out-of-phase, motions of the support structures are important and should be considered in deriving force limiting specifications. In this presentation "rock-n-roll" motions of the components supported by panels, which leads to a more realistic force limiting specifications are discussed.
The adopted level densities (LD) for the nuclei produced through different reaction mechanisms significantly impact the accurate calculation of cross sections for the different reaction channels. Many common LD models make simplified assumptions regarding the overall behavior of the total LD and the intrinsic spin and parity distributions of the excited states. However, very few experimental constraints are taken into account in these models: LD at neutron separation energy coming from average spacings of s- and p-wave resonances ( D 0 and D 1 , respectively) whenever they have been previously measured, and the sometimes subjective extrapolation of discrete levels. These, however, constrain the LD only in very specific regions of excitation energy, and for specific spins and parities. This work aims to establish additional experimental constraints on LD through quantitative correlations between cross sections and LD. This allows for fitting and the determination of detailed structures in LD. For this we use the microscopic Hartree-Fock-Bogoliubov (HFB) LD as a starting point as the HFB LD provide a more realistic spin and parity distributions than phenomenological models such as Gilbert-Cameron (GC). We then associate variations predicted by the HFB model with the structure observed in double-differential cross sections at low outgoing neutron energy, a region that is dominated by the LD input. We also use (n, p) on 56 Fe , as an example case where angle-integrated cross sections are extremely sensitive to LD. For comparison purposes we also perform calculations with the GC model. With this approach we are able to perform fits of the LD based on actual experimental data, constraining the model and ensuring its consistency. This approach can be particularly useful in extrapolating the LD to nuclei for which high-excited discrete levels and/or values of D 0 or D 1 are unknown. Finally, it also predicts neutron-induced inelastic γ cross sections that in some cases can differ significantly from more standard phenomenological LD models such as GC.
This paper presents experimental results pertaining to spherical glass bead and angular crushed glass particle impingement. A concept of energy absorption to explain the failure of material is proposed and is correlated with the erosion characteristics of several pure metals. Analyses of extensive erosion data indicate that the properties - surface energy, specific melting energy, strain energy, melting point, bulk modulus, hardness, atomic volume - and the product of the parameters - linear coefficient of thermal expansion x bulk modulus x temperature rise required for melting, and ultimate resilience x hardness - exhibit the best correlations. The properties of surface energy and atomic volume are suggested for the first time for correlation purposes and are found to correlate well with erosion rates at different angles of impingement. It further appears that both energy and thermal properties contribute to the total erosion.
Microseismicity induced by CO 2 injection at Decatur, Illinois, occurs in distinct clusters and shows no obvious correlation with the proceeding pressure front. Here, we analyze some of these clusters in more depth by using a waveform cross-correlation approach. With this approach we can associate about 1400 events from two clusters, with moment magnitudes between 1.1 and -1.7, with specific formations of much smaller vertical dimensions (tens of meters) than the depth resolution of traveltime-based event locations. The differentiation of reservoir and basement events, and the definition of subclusters by waveform correlation, rather than by location, helps to better analyze the spatiotemporal evolution of the events within a cluster. In the Decatur case, this is characterized by event migration from the reservoir into the adjacent basement. The spatial variation of Brune stress drop and Gutenberg b value exhibits signs of a fluid-driven triggering mechanism at the cluster level, revealing a punctual hydraulic connection between reservoir and basement, most likely associated with basement faults cutting into the reservoir. The observed clustering of microseismicity can thus be explained by the lateral heterogeneity of permeability and crustal strength and is overall consistent with a pressure-induced triggering mechanism. Hence, proper long-term risk mitigation for large-scale fluid injection close to the basement requires prior mapping of small subseismic basement-connected faults.
This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T(sub B)): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated T(sub B) at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (D(sub max)) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D(sub max)), assuming non-sticky spheres for the DMRT models. Simulated T(sub B) sensitivity analysis using the same inputs shows relatively consistent T(sub B) behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T(sub B). Results using in-situ grain size measurements (SSA or D(sub max), depending on the model) give a mean T(sub B) RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than D(sub max) measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.
Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events.
Rainfall rate estimates from space-borne k&ents are generally accepted as reliable by a majority of the atmospheric science commu&y. One-of the Tropical Rainfall Measuring Mission (TRh4M) facility rain rate algorithms is based upon passive microwave observations fiom the TRMM Microwave Imager (TMI). Part I of this study describes improvements in the TMI algorithm that are required to introduce cloud latent heating and drying as additional algorithm products. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, OP5resolution estimates of surface rain rate over ocean fiom the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over forerunning algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm, and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly, 2.5 deg. -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data are limited, TMI estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with: (a) additional contextual information brought to the estimation problem, and/or; (b) physically-consistent and representative databases supporting the algorithm. A model of the random error in instantaneous, 0.5 deg-resolution rain rate estimates appears to be consistent with the levels of error determined from TMI comparisons to collocated radar. Error model modifications for non-raining situations will be required, however. Sampling error appears to represent only a fraction of the total error in monthly, 2S0-resolution TMI estimates; the remaining error is attributed to physical inconsistency or non-representativeness of cloud-resolving model simulated profiles supporting the algorithm.
Multipoint correlations in randomly heterogeneous composite media are determined by the probability that a set of points belong to specific phases. They determine a wide range of macroscopic transport properties such as conductivity, dielectric constant, diffusion coefficient, and transmittance. The Poisson model—a random tesselation of space by hyperplanes—provides realistic descriptions of heterogeneous media in, e.g., radiation-transport applications; yet, until now, it has lacked closed-form expressions for its multipoint correlations. We resolve this problem by presenting an exact solution for the multipoint correlations in the Poisson model. By comparing it to Monte Carlo simulations of four-point correlations in three dimensions, we demonstrate the accuracy of our solution. In conclusion, we visualize the multipoint correlations and discuss their features.
We report photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500–2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R 2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
This study provides a systematic investigation into the size and geometry-dependent microstructural evolution and cracking susceptibility of LPBF-fabricated Rene 41. By coupling experimental microstructural analysis with thermal modeling, this research uniquely identifies the relationship between geometry-specific thermal histories, carbide coarsening, and liquation cracking. Four different geometries with varying thickness were fabricated with the identical process parameters. It was found that the grain size and morphology are not affected by the part size. However, the thinner parts exhibited coarser sub-grain structures compared to the thicker ones. The crack formation was observed for the parts with cross-sections smaller than 1 mm, whereas thicker parts had high density without any defects. Further, the cracks were observed in the interdendritic regions, suggesting that liquation cracking was the active micro-crack formation mechanism. The detailed microstructural analysis combined with a thermal finite element analysis proved that the heat extraction efficiency was lower for thinner parts causing a lower cooling rate and coarser carbides, making them more susceptible to constitutional liquation. Microhardness measurements were conducted for each geometry and correlated with the observed microstructural variations. The findings highlight the critical need for geometry-specific optimization of LPBF process parameters to mitigate cracking and achieve microstructural uniformity, offering valuable insights into the fabrication of complex, high-performance aerospace components.
Developing strategies to prevent corrosion at the interface of dissimilar metal alloys is challenging because of the presence of heterogenous distribution of galvanic couples and microstructural features that significantly change the corrosion rate. Devising strategies to mitigate this interfacial corrosion requires quantitative and correlative understanding of its surface electrochemical reaction. In this work, scanning electrochemical cell impedance microscopy (SECCIM) was employed to study location-specific corrosion in the interfacial region of dissimilar alloys, such as AZ31 (magnesium alloy) and DP590 (steel) welded using the Friction-stir Assisted Scribe Technique (FAST) processes. Herein, SECCM and SECCIM were used to perform correlative mapping of the local electrochemical impedance spectroscopic and potentiodynamic polarization to measure the effect of electronic and microstructural changes in the welded interfacial region on corrosion kinetics. Microstructural characterization including scanning electron microscopy and electron backscatter diffraction was performed to correlate changes in microstructural features and chemistry with the corresponding electronic properties that affect corrosion behavior. The variations in corrosion potential, corrosion current density, and electrochemical impedance spectroscopy behavior across the interface provide deeper insights on the interfacial region—which is chemically and microstructurally distinct from both bare AZ31 and DP590 that can help prevent corrosion in dissimilar metal structures.
In this work, we benchmark calculated interlayer spacings, average topotactic voltages, thermodynamic stabilities, and band gaps in layered lithium transition-metal oxides (TMOs) and their de-lithiated counterparts, which are used in lithium-ion batteries as positive electrode materials, against available experimental data. Specifically, we examine the accuracy of properties calculated within density functional theory (DFT) using eight different treatments of electron exchange-correlation: the strongly constrained and appropriately normed (SCAN) and Perdew–Burke–Ernzerhof (PBE) density functionals, Hubbard-U-corrected SCAN and PBE (i.e., SCAN+U and PBE+U), and SCAN(+U) and PBE(+U) with added long-range dispersion (D) interactions (i.e., DFT(+U)+D). van der Waals interactions are included respectively via the revised Vydrov-Van Voorhis (rVV10) for SCAN(+U) and the DFT-D3 for PBE(+U). We find that SCAN-based functionals predict larger voltages due to an underestimation of stability of the MO2 systems, while also predicting smaller interlayer spacings compared to their PBE-based counterparts. Furthermore, adding dispersion corrections to PBE has a greater effect on voltage predictions and interlayer spacings than with SCAN, indicating that DFT-SCAN – despite being a ground-state theory – fortuitously captures some short and medium-range dispersion interactions better than PBE. While SCAN-based and PBE-based functionals yield qualitatively similar band gap predictions, there is no significant quantitative improvement of SCAN-based functionals over the corresponding PBE-based versions. Finally, we expect SCAN-based functionals to yield more accurate property predictions than the respective PBE-based functionals for most TMOs, given SCAN's stronger theoretical underpinning and better predictions of systematic trends in interlayer spacings, intercalation voltages, and band gaps obtained in this work.
The climatic sensitivity of new terrain-aware backtracking algorithms is evaluated across 800 locations in the continental USA on a representative synthetic rolling terrain. We find that a global optimization approach to backtracking results in climate-specific annual energy gains of 2.4%–3.2% relative to a traditional backtracking algorithm baseline. We identify a strong logarithmic correlation between local diffuse fraction and yield improvement, and highlight the effect of seasonal precipitation on performance gains. We also find that a backtracking approach, which approximates the terrain as constant, does not offer significant annual energy gains over the baseline on the synthetic terrain. Our findings suggest that specific yield from backtracking in the USA can be improved by as much as 88 kWh/kW by considering terrain when selecting a backtracking algorithm.
Background: “Quantile-dependent expressivity” refers to a genetic effect that is dependent upon whether the phenotype (e.g., spirometric data) is high or low relative to its population distribution. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV 1 ), and the FEV 1 /FVC ratio are moderately heritable spirometric traits. The aim of the analyses is to test whether their heritability (h 2 ) is constant over all quantiles of their distribution. Methods: Quantile regression was applied to the mean age, sex, height and smoking-adjusted spirometric data over multiple visits in 9,993 offspring-parent pairs and 1,930 sibships from the Framingham Heart Study to obtain robust estimates of offspring-parent (β OP ), offspring-midparent (β OM ), and full-sib regression slopes (β FS ). Nonparametric significance levels were obtained from 1,000 bootstrap samples. β OP s were used as simple indicators of quantile-specific heritability (i.e.,h 2 = 2β OP /(1+r spouse ), where r spouse was the correlation between spouses). Results: β OP ± standard error (SE) decreased by 0.0009 ± 0.0003 (P = 0.003) with every one-percent increment in the population distribution of FEV 1 /FVC, i.e., β OP ± SE were: 0.182 ± 0.031, 0.152 ± 0.015; 0.136 ± 0.011; 0.121 ± 0.013; and 0.099 ± 0.013 at the 10th, 25th, 50th, 75th, and 90th percentiles of the FEV 1 /FVC distribution, respectively. These correspond to h 2 ± SEs of 0.350 ± 0.060 at the 10th, 0.292 ± 0.029 at the 25th, 0.262 ± 0.020 at the 50th, 0.234 ± 0.025 at the 75th, and 0.191 ± 0.025 at the 90th percentiles of the FEV 1 /FVC ratio. Maximum mid-expiratory flow (MMEF) h 2 ± SEs increased 0.0025 ± 0.0007 (P = 0.0004) with every one-percent increment in its distribution, i.e.: 0.467 ± 0.046, 0.467 ± 0.033, 0.554 ± 0.038, 0.615 ± 0.042, and 0.675 ± 0.060 at the 10th, 25th, 50th, 75th, and 90th percentiles of its distribution. This was due to forced expiratory flow at 75% of FVC (FEF75%), whose quantile-specific h 2 increased an average of 0.0042 ± 0.0008 for every one-percent increment in its distribution. It is speculated that previously reported gene-environment interactions may be partially attributable to quantile-specific h 2 , i.e., greater heritability in individuals with lower FEV 1 /FVC due to smoking or airborne particles exposure vs. nonsmoking, unexposed individuals. Conclusion: Heritabilities of FEV 1 /FVC, MMEF, and FEF75% from quantile-regression of offspring-parent and sibling spirometric data suggest their quantile-dependent expressivity.
Under typical solar chromospheric conditions for hydrogen radiators, strong collisions due to both electrons and ions are well separated in time, so that a binary collision theory for collisional redistribution is applicable. However, a simple impact approximation may not be used, but rather a unified type theory is required in which frequency dependent line shape parameters are used to describe both impact and quasi-static regions of the spectrum. In addition, correlated terms which describe absorption and emission during a collision are important, and, in fact, without correlated terms describing both transfer of excitation and emission during the same collision unphysical predictions (such as negative intensities) would be obtained. In this paper theory is specifically developed for the coupled Lyman-alpha, Lyman-beta, Hydrogen-alpha system, and equations of statistical equilibrium and absorption and emission coefficients are given. All correlated events are examined and emission during a collision is found to be important in the line wings. Stimulated emission and absorption is also included within a broadband approximation. The major approximation is to ignore lower state interaction. It is found that for Lyman-beta Raman-coupling with Hydrogen-alpha occurs and the overall scattering of radiation in the line wings is mostly coherent. In contrast, for Hydrogen-alpha, incoherent redistribution due to lower state radiative decay (which occurs even in the absence of collisions) is found to dominate the coherent scattering. Finally, in the Lyman series the dominant incoherent contribution is associated with cascade transitions and inelastic collisions between different principal quantum states.
This research is aimed at producing a fundamental new research tool for characterizing the source strength of the most important compound controlling the hemispheric and global scale distribution of tropospheric ozone. Specifically, this effort seeks to demonstrate the proof-of-concept of a new general purpose laser-induced fluorescence based spectrometer for making airborne eddy-correlation flux measurements of nitric oxide (NO) and other reactive nitrogen compounds. The new all solid-state laser technology being used in this advanced sensor will produce a forerunner of the type of sensor technology that should eventually result in highly compact operational systems. The proof-of-concept sensor being developed will have over two orders-of-magnitude greater sensitivity than present-day instruments. In addition, this sensor will offer the possibility of eventual extension to airborne eddy-correlation flux measurements of nitrogen dioxide (NO2) and possibly other compounds, such as ammonia (NH3), peroxyradicals (HO2), nitrateradicals (NO3) and several iodine compounds (e.g., I and IO). Demonstration of the new sensor's ability to measure NO fluxes will occur through a series of laboratory and field tests. This proof-of-concept demonstration will show that not only can airborne fluxes of important ultra-trace compounds be made at the few parts-per-trillion level, but that the high accuracy/precision measurements currently needed for predictive models can also. These measurement capabilities will greatly enhance our current ability to quantify the fluxes of reactive nitrogen into the troposphere and significantly impact upon the accuracy of predictive capabilities to model O3's distribution within the remote troposphere. This development effort also offers a timely approach for producing the reactive nitrogen flux measurement capabilities that will be needed by future research programs such as NASA's planned 1999 Amazon Biogeochemistry and Atmospheric Chemistry Experimental portion of LBA.