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At least 343 records · Page 19

Mechanistic Insights into Aldehyde Production from Electrochemical CO 2 Reduction on CuAg Alloy via Operando X-ray Measurements

CO 2 electrolysis converts the greenhouse gas CO 2 into valuable fuels and chemicals, such as carbon monoxide, ethylene, ethanol, etc. Currently, Cu is the only known monometallic catalyst capable of producing multicarbon products from electrochemical CO 2 reduction reaction (eCO2RR), while the poor selectivity limits its further use. It has been found that introducing Ag atoms into the Cu lattice can modulate product preference. However, the synergistic effects between Cu and Ag, and thus, the catalytic performance, are strongly influenced by catalyst morphology, electrolyzer configuration, reaction conditions, etc. Operando measurements can provide explicit information on the catalyst dynamic variation during the reaction, but their operation and analysis are challenging. Herein, we prepared CuAg multiphase alloy catalysts by magnetron sputtering, which allowed for investigating the intrinsic interaction between Cu and Ag. eCO2RR performance exhibited an improved selectivity toward carbonyls at the expense of hydrogen and hydrocarbons. The partially alloyed Cu and Ag phases were confirmed by operando X-ray diffraction. By means of combining operando X-ray measurements and density functional theory (DFT) calculations, the preferred carbonyl production is attributed to the reduced electron density and compressive strain of Cu due to Ag incorporation, which leads to a deeper d-band center and therefore weakened intermediate adsorption and oxophilicity. In conclusion, this work provides evidence of the intrinsic structural and electronic interaction between Cu and Ag during eCO2RR. The obtained information will facilitate the design of bi/multi-phase metallic or alloy electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Two-Dimensional 2M-WS 2 Nanolayers for Superconductivity

Recently, a newly discovered VIB group transition metal dichalcogenide (TMD) material, 2M-WS 2 , has attracted extensive attention due to its interesting physical properties such as topological superconductivity, nodeless superconductivity, and anisotropic Majorana bound states. However, the techniques to grow high-quality 2M-WS 2 bulk crystals and the study of their physical properties at the nanometer scale are still limited. In this work, we report a new route to grow high-quality 2M-WS 2 single crystals and the observation of superconductivity in its thin layers. The crystal structure of the as-grown 2M-WS 2 crystals was determined by X-ray diffraction (XRD) and scanning tunneling microscopy (STM). The chemical composition of the 2M-WS 2 crystals was determined by energy dispersive X-ray spectroscopy (EDS) analysis. At 77 K, we observed the spatial variation of the local tunneling conductance (dI/dV) of the 2M-WS2 thin flakes by scanning tunneling spectroscopy (STS). Our low temperature transport measurements demonstrate clear signatures of superconductivity of a 25 nm-thick 2M-WS 2 flake with a critical temperature (T C ) of ~8.5 K and an upper critical field of ~2.5 T at T = 1.5 K. Our work may pave new opportunities in studying the topological superconductivity at the atomic scale in simple 2D TMD materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach ( R 2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database ( R 2 = 0.23) and SoilGrids 2.0 ( R 2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.

58 GEOSCIENCES↗

Single-particle detection of enhanced polarizability in Au-decorated semiconducting nanorods via scanning dielectric microscopy

Hybrid nanostructures that combine semiconducting and metallic components offer great potential for photothermal therapy, optoelectronics, and sensing, by integrating tunable optical properties with enhanced light absorption and charge transport. Boosting the integrated performance of these hybrid systems demands techniques capable of probing local variations of the physical properties inaccessible to bulk analysis. Here, we report the single-particle dielectric characterization of hybrid, semiconducting bismuth sulfide (Bi 2 S 3 ) nanorods (NR) decorated with metallic Au nanoparticles (NP), employing scanning dielectric microscopy, which uses electrostatic force microscopy in combination with finite-element numerical simulations. We reveal a pronounced enhancement in the local dielectric response of Bi2S3 upon Au decoration, attributed to interfacial polarization and electron transfer from Au to the Bi 2 S 3 matrix, thus suggesting a enhanced metallic-like polarizability at the single-particle level. Numerical simulations show that the response is dominated by the vertical component of the permittivity and that the decorating metallic Au NP produce only moderate shielding of the semiconductor Bi 2 S 3 NR core, indicating that the large increase in the dielectric response originates primarily from intrinsic modifications within the NR. Overall, these findings provide direct insight into structure–property relationships at the single-particle level, supporting the rational design of advanced hybrid nanostructures with tailored electronic functionalities.

36 MATERIALS SCIENCE↗

Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structure–function coupling

At the biosphere–atmosphere interface, nonlinear interdependencies among components of an ecohydrological complex system can be inferred using multivariate high frequency time series observations. Information flow among these interacting variables allows us to represent the causal dependencies in the form of a directed acyclic graph (DAG). Here, we use high frequency multivariate data at 10 Hz from an eddy covariance instrument located at 25 m above agricultural land in the Midwestern US to quantify the evolutionary dynamics of this complex system using a sequence of DAGs by examining the structural dependency of information flow and the associated functional response. We investigate whether functional differences correspond to structural differences or if there are no functional variations despite the structural differences. We base our analysis on the hypothesis that causal dependencies are instigated through information flow, and the resulting interactions sustain the dynamics and its functionality. To test our hypothesis, we build upon causal structure analysis in the companion paper to characterize the information flow in similarly clustered DAGs from 3-min non-overlapping contiguous windows in the observational data. We characterize functionality as the nature of interactions as discerned through redundant, unique, and synergistic components of information flow. Through this analysis, we find that in turbulence at the biosphere–atmosphere interface, the variables that control the dynamic character of the atmosphere as well as the thermodynamics are driven by non-local conditions, while the scalar transport associated with CO and H 2 O is mainly driven by short-term local conditions.

58 GEOSCIENCES↗

Distributed Hierarchical Contour Trees

Contour trees are a significant tool for data analysis as they capture both local and global variation. However, their utility has been limited by scalability, in particular for distributed computation and storage. We report a distributed data structure for storing the contour tree of a data set distributed on a cluster, based on a fan-in hierarchy, and an algorithm for computing it based on the boundary tree that represents only the superarcs of a contour tree that involve contours that cross boundaries between blocks. This allows us to limit the communication cost for contour tree computation to the complexity of the block boundaries rather than of the entire data set.

Carr, Hamish A↗

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

Micro- and nanomanufacturing capabilities have rapidly expanded over the past decade to include complex three-dimensional (3D) structure fabrication; however, the metrology required to accurately assess these processes via part inspection and characterization has struggled to keep pace. X-ray computed tomography (CT) is considered an ideal candidate for providing the critically needed metrology on the smallest scales, especially internal features, or inaccessible regions. X-ray CT supporting micro- and nanomanufacturing often push against the poorly understood resolution and variation limits inherent to the machines, which can distort or hide fine structures. In this study, we have developed and experimentally verify a comprehensive analytical uncertainty propagation signal variation flow graph (SVFG) model for X-ray radiography in this work to better understand resolution and image variability limits on the small scale. The SVFG approach captures, quantifies, and predicts variations occurring in the system that limit metrology capabilities, particularly in the micro/nanodomain. This work is the first step to achieving full uncertainty modeling of CT reconstructions and provides insight into improving X-ray attenuation imaging systems. The SVFG methodology framework is applied to generate a complete basis set of functions describing the major sources of variation in radiographs. Five models are identified, covering variation in energy, intensity, length, blur, and position. Radiographic system experiments are defined to measure the parameters required by the SVFGs. Best practices are identified for these measurements. The SVFG models are confirmed via direct measurement of variation to predict variation within 30% on average.

47 OTHER INSTRUMENTATION↗

Compressed Natural Gas Component Leak Frequency Estimation

The frequency of unintended releases in a compressed natural gas system is an important aspect of the system quantitative risk assessment. The frequencies for possible release scenarios, along with engineering models, are utilized to quantify the risks for compressed natural gas facilities. This report documents component leakage frequencies representative of compressed natural gas components that were estimated as a function of the normalized leak size. A Bayesian statistical method was used which results in leak frequency distributions for each component which represent variation and uncertainty in the leak frequency. The analysis shows that there is high uncertainty in the estimated leak frequencies due to sparsity in compressed natural gas data. These leak frequencies may still be useful in compressed natural gas system risk assessments, as long as this high uncertainty is acknowledged and considered appropriately.

03 NATURAL GAS↗

Examination of Factors Affecting the Cost and Performance of a Natural Gas Combined Cycle Equipped with Carbon Dioxide Capture

The purpose of this Technical Note is to report the findings of an examination of the effect of plausible deviations in select study assumptions on the reported cost and performance estimates for a power plant case drawn from NETL’s “Cost and Performance Baseline for Fossil Energy Plants Volume 1: Bituminous Coal and Natural Gas to Electricity” (known as the Fossil Energy Baseline). An F-Class NGCC power plant equipped with state-of-the-art, solvent-based, post-combustion carbon dioxide (CO2) capture (95 percent carbon capture rate)—designated as Case B31B.95—was selected for this work. This sensitivity analysis provides insight into the effects of parameter variations within and across selected categories—ambient conditions, construction cost, natural gas (NG) price, capacity factor, and finance—on the plant performance and capital and operating and maintenance (O&M) costs, and the subsequent impact on common figures of merit.

20 FOSSIL-FUELED POWER PLANTS↗

An EvoDevo Study of Salmonid Visual Opsin Dynamics and Photopigment Spectral Sensitivity

Salmonids are ideal models as many species follow a distinct developmental program from demersal eggs and a large yolk sac to hatching at an advanced developmental stage. Further, these economically important teleosts inhabit both marine- and freshwaters and experience diverse light environments during their life histories. At a genome level, salmonids have undergone a salmonid-specific fourth whole genome duplication event (Ss4R) compared to other teleosts that are already more genetically diverse compared to many non-teleost vertebrates. Thus, salmonids display phenotypically plastic visual systems that appear to be closely related to their anadromous migration patterns. This is most likely due to a complex interplay between their larger, more gene-rich genomes and broad spectrally enriched habitats; however, the molecular basis and functional consequences for such diversity is not fully understood. This study used advances in genome sequencing to identify the repertoire and genome organization of visual opsin genes (those primarily expressed in retinal photoreceptors) from six different salmonids [Atlantic salmon ( Salmo salar ), brown trout ( Salmo trutta ), Chinook salmon ( Oncorhynchus tshawytcha ), coho salmon ( Oncorhynchus kisutch ), rainbow trout ( Oncorhynchus mykiss ), and sockeye salmon ( Oncorhynchus nerka )] compared to the northern pike ( Esox lucius ), a closely related non-salmonid species. Results identified multiple orthologues for all five visual opsin classes, except for presence of a single short-wavelength-sensitive-2 opsin gene. Several visual opsin genes were not retained after the Ss4R duplication event, which is consistent with the concept of salmonid rediploidization. Developmentally, transcriptomic analyzes of Atlantic salmon revealed differential expression within each opsin class, with two of the long-wavelength-sensitive opsins not being expressed before first feeding. Also, early opsin expression in the retina was located centrally, expanding dorsally and ventrally as eye development progressed, with rod opsin being the dominant visual opsin post-hatching. Modeling by spectral tuning analysis and atomistic molecular simulation, predicted the greatest variation in the spectral peak of absorbance to be within the Rh2 class, with a ∼40 nm difference in λ max values between the four medium-wavelength-sensitive photopigments. Overall, it appears that opsin duplication and expression, and their respective spectral tuning profiles, evolved to maximize specialist color vision throughout an anadromous lifecycle, with some visual opsin genes being lost to tailor marine-based vision.

59 BASIC BIOLOGICAL SCIENCES↗

Energy transport analysis of NSTX plasmas with the TGLF turbulent and NEO neoclassical transport models

This work presents a study of plasma transport at low aspect ratio on the National Spherical Torus Experiment tokamak, where the turbulent and neoclassical energy fluxes calculated by the quasilinear Trapped Gyro Landau Fluid (TGLF) model and the multi species drift-kinetic Neoclassical solver (NEO) are validated against experimental data. The turbulent energy transport of two plasma discharges, one in the L-mode confinement regime and another in the H-mode regime, is dominated by electrostatic drift-wave instabilities, while the ion heat transport has a significant neoclassical contribution. The data analysis workflow is described in detail to understand how the variations of mapping and fitting of experimental data affect the power balance solution and subsequent flux-matching plasma profile predictions with the TGYRO solver. On average, the predicted plasma profiles are consistent with experimental data. However, the solutions are sensitive to various input parameters, including boundary conditions, and the electron-ion coupling. Linear gyrokinetic stability analysis demonstrates close agreement of the real frequencies of unstable modes between TGLF and CGYRO gyrokinetic simulations, but higher growth rates are predicted by TGLF, especially for the H-mode case. Estimates of the low-k modes' contributions to the total flux are consistent with linear stability analysis and the E × B suppression of turbulence in TGLF simulations with the SAT1 saturation model, while the SAT2 saturation model over-predicts the low-k modes' contribution in the H-mode case. Moreover, the results with SAT1 model are consistent with power balance analysis, which indicates only neoclassical ion energy fluxes inside ρ < 0.4 in the L-mode case and $\rho \unicode{x2A7D} 0.7$ in the H-mode case. The presence of multi-scale turbulence and ion-scale driven zonal flow mixing effects are also observed in TGLF scans of the electron turbulent heat flux over a range of temperature gradients and the electron-ion temperature ratio, which could explain the strong model sensitivity to variations of input parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Three-dimensional imaging of grain boundaries via quantitative fluorescence X-ray tomography analysis

Abstract Three-dimensional visualization of material composition within multiple grains and across complex networks of grain boundaries at nanoscales can provide new insight into the structure evolution and emerging functional properties of the material for diverse applications. Here, using nanoscale scanning X-ray fluorescence tomography, coupled with an advanced self-absorption correction algorithm developed in this work, we analyze the three-dimensional gain distributions and compositions in a Ce 0.8 Gd 0.2 O 2-δ -CoFe 2 O 4 mixed ionic-electronic conductor system with high accuracy and statistical significance. Our systematic investigation reveals an additional emergent phase and uncovers highly intriguing composition stability ranges for the multiple material phases within this system. The presented visualization of composition variations across complex interfaces, supported by our quantitative composition analysis, discloses mechanistic pathways of the diverse phase transformations occurring in the material synthesis, providing insights for the optimization of transport properties in the mixed ionic-electronic conductor system.

36 MATERIALS SCIENCE↗

Impacts of uncertain feedstock quality on the economic feasibility of fast pyrolysis biorefineries with blended feedstocks and decentralized preprocessing sites in the Southeastern United States

Abstract This study performs techno‐economic analysis and Monte Carlo simulations (MCS) to explore the effects that variations in biomass feedstock quality have on the economic feasibility of fast pyrolysis biorefineries using decentralized preprocessing sites (i.e., depots that produce pellets). Two biomass resources in the Southeastern United States, that is, pine residues and switchgrass, were examined as feedstocks. A scenario analysis was conducted for an array of different combinations, including different pellet ash control levels, feedstock blending ratios, different biorefinery capacities, and different biorefinery on‐stream capacities, followed by a comparison with the traditional centralized system. MCS results show that, with depot preprocessing, variations in the feedstock moisture and feedstock ash content can be significantly reduced compared with a traditional centralized system. For a biorefinery operating at 100% of its designed capacity, the minimum fuel selling price (MFSP) of the decentralized system is $3.97–$4.39 per gallon gasoline equivalent (GGE) based on the mean value across all scenarios, whereas the mean MFSP for the traditional centralized system was $3.79–$4.12/GGE. To understand the potential benefits of highly flowable pellets in decreasing biorefinery downtime due to feedstock handling and plugging problems, this study also compares the MFSP of the decentralized system at 90% of its designed capacity with a traditional system at 80%. The analysis illustrates that using low ash pellets mixed with switchgrass and pine residues generates a more competitive MFSP. Specifically, for a biorefinery designed for 2,000 oven dry metric ton per day, running a blended pellet made from 75% switchgrass and 25% pine residues with 2% ash level, and operating at 90% of designed capacity could make an MFSP between $4.49 and $4.71/GGE. In contrast, a traditional centralized biorefinery operating at 80% of designed capacity marks an MFSP between $4.72 and $5.28.

09 BIOMASS FUELS↗

Accuracy limitations for composition analysis by XPS using relative peak intensities: LiF as an example

Although precision in XPS can be excellent, allowing small changes to be easily observed, obtaining accurate absolute elemental composition of a solid material from relative peak intensities is generally much more problematical, involving many factors: background removal; differing analysis depths at different photoelectron kinetic energies; possible angular distribution effects; calibration of the instrument transmission function, and variations of the distribution of the photoelectron intensity between “main” peaks (those usually used for analysis) and associated substructure following the main peak, as a function of the chemical bonding of the elements concerned. The last item, coupled with the use of photoionization cross-sections and/or relative sensitivity factors, is the major subject of this paper, though it is necessary to consider the other items also, using LiF as a test case. The results show that the above issues, which are relevant to differing degrees in most XPS analyses, present significant challenges to highly accurate XPS quantification.

Brundle, Christopher R.↗

Enhanced beam-beam modeling to include longitudinal variation during weak-strong simulation

Beam-beam interactions pose substantial challenges in the design and operation of circular colliders, significantly affecting their performance. In particular, the weak-strong simulation approach is pivotal for investigating single-particle dynamics during the collider design phase. This paper evaluates the limitations of existing models in weak-strong simulations, noting that while they accurately account for energy changes due to slingshot effects, they fail to incorporate longitudinal coordinate changes ( z variation). To address this gap, we introduce two novel transformations that enhance Hirata’s original framework by including both z variation and slingshot effect-induced energy changes. Through rigorous mathematical analysis and extensive weak-strong simulation studies, we validate the efficacy of these enhancements in achieving a more precise simulation of beam-beam interactions. Our results reveal that although z variation constitutes a higher-order effect and does not substantially affect the emittance growth rate within the specific design parameters of the Electron-Ion Collider, the refined model offers improved accuracy, particularly in scenarios involving the interaction between beam-beam effects and other random diffusion processes, as well as in simulations incorporating realistic lattice models. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Electromagnetic Analysis of ITER Electron Cyclotron Emission Model

The ITER electron cyclotron emission (ECE) diagnostic system is located at diagnostics shielding module 2 (DSM2), equatorial port 9 (EP9), to measure electron temperature profile and electron temperature fluctuations, and also assess nonthermal electron distributions via the oblique view. Therefore, ECE has both radial and oblique views with two hot sources for calibration and two couples of mirrors for two different optical views. It shall receive that the large electromagnetic (EM) loads up to 100-MN/m3 force density due to the eddy currents on the copper mirrors generated by the short transient plasma disruption. The simplified EM analysis model using magnetic field ( B ) data and flux variations (dB/dt) method based on the worst case of plasma disruption, MD_DW_EXP16MS_CATIII, has been developed to calculate the force and moments for each ECE component and support structure for EP9 DSM2 Bay2 and Bay3. The B and dB/dt methods use the constant B and dB/dt assumed for each local bay space, but the real B and dB/dt are basically varied along the radial direction of each bay. The complex global model with the local ECE components and support structure using Maxwell transient has been developed to do EM analysis to compare the results of the B and dB/dt models. The volumetric EM force density of the whole ECE components and support structure can be used for the subsequent structural analysis. In conclusion, combined with the thermal and nuclear loads and seismic and initial loads, the ECE integration analysis can be finalized for the operation case.

Fang, J. [Princeton Plasma Physics Laboratory (PPP↗

Multiscale investigation of thermomechanical and compositional developments in Ni alloy 718 under laser processing

Laser processing has been widely employed in various applications due to its exceptional spatial resolution. However, the rapid temperature gradients generated in localized areas present significant challenges for experimental characterization using conventional instruments. To characterize Ni alloy 718 during laser processing, we employed in-situ synchrotron X-ray diffraction with a high-speed detector, a method particularly well-suited for probing processes with high temporal and spatial resolution. Through a series of in-situ experiments, we investigated the local variations in the evolution of microstructures and thermomechanical behaviors within a keyhole mode melt pool. The in situ macroscopic thermomechanical behaviors were quantified using an empirical model derived from diffraction patterns, with experimental results showing reasonable agreement with finite element analysis. Various laser parameters were tested to assess their influences on the residual strains in the melt pools. The results revealed that the residual strain in the keyhole mode melt pool is relatively insensitive to variations in the parameters and is smaller than that in the melt pool created under conduction mode laser scanning. Additionally, we analyzed the shapes of individual diffraction spots, providing insights into the plastic behaviors and compositional developments in the resolidified alloy. The analysis confirmed that compositional variations in a dendritic microstructure manifest as asymmetric broadening of the diffraction spots.

Ni alloy 718↗