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At least 937 records · Page 52

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NASA NTRS2020

Untangling causality in midlatitude aerosol–cloud adjustments

Aerosol–cloud interactions represent the leading uncertainty in our ability to infer climate sensitivity from the observational record. The forcing from changes in cloud albedo driven by increases in cloud droplet number (Nd) (the first indirect effect) is confidently negative and has narrowed its probable range in the last decade, but the sign and strength of forcing associated with changes in cloud macrophysics in response to aerosol (aerosol–cloud adjustments) remain uncertain. This uncertainty reflects our inability to accurately quantify variability not associated with a causal link flowing from the cloud microphysical state to the cloud macrophysical state. Once variability associated with meteorology has been removed, covariance between the liquid water path (LWP) averaged across cloudy and clear regions (here characterizing the macrophysical state) and Nd (characterizing the microphysical) is the sum of two causal pathways linking Nd to LWP: Nd altering LWP (adjustments) and precipitation scavenging aerosol and thus depleting Nd. Only the former term is relevant to constraining adjustments, but disentangling these terms in observations is challenging. We hypothesize that the diversity of constraints on aerosol–cloud adjustments in the literature may be partly due to not explicitly characterizing covariance flowing from cloud to aerosol and aerosol to cloud. Here, we restrict our analysis to the regime of extratropical clouds outside of low-pressure centers associated with cyclonic activity. Observations from MAC-LWP (Multisensor Advanced Climatology of Liquid Water Path) and MODIS are compared to simulations in the Met Office Unified Model (UM) GA7.1 (the atmosphere model of HadGEM3-GC3.1 and UKESM1). The meteorological predictors of LWP are found to be similar between the model and observations. There is also agreement with previous literature on cloud-controlling factors finding that increasing stability, moisture, and sensible heat flux enhance LWP, while increasing subsidence and sea surface temperature decrease it. A simulation where cloud microphysics are insensitive to changes in Nd is used to characterize covariance between Nd and LWP that is induced by factors other than aerosol–cloud adjustments. By removing variability associated with meteorology and scavenging, we infer the sensitivity of LWP to changes in Nd. Application of this technique to UM GA7.1 simulations reproduces the true model adjustment strength. Observational constraints developed using simulated covariability not induced by adjustments and observed covariability between Nd and LWP predict a 25 %–30 % overestimate by the UM GA7.1 in LWP change and a 30 %–35 % overestimate in associated radiative forcing.

Daniel T. McCoy↗

DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗

DOE OSTI2021

Operando bulk and interfacial characterization for electrochemical energy storage: Case study employing isothermal microcalorimetry and X-ray absorption spectroscopy

Abstract The global shift to electricity as the main energy carrier will require innovation in electrochemical energy storage (EES). EES systems are the key to the “electron energy economy,” minimizing losses and increasing reliability between energy supply and demand. However, steep challenges such as cost, cycle/calendar life, energy density, material availability, and safety limit widespread adoption of batteries for large-scale grid and vehicle applications. Battery innovation that meets today’s challenges will require new chemistries, which can originate from understanding charge transport phenomena at multiple time and length scales. The advancement of operando characterization can expedite this progress as changes can be observed during battery function. This article highlights progress in bulk and interfacial operando characterization of batteries. Specifically, a case study involving Fe 3 O 4 is provided demonstrating that combining X-ray absorption spectroscopy and isothermal microcalorimetry can provide real-time characterization of productive faradaic redox processes and parasitic interfacial reactions during (de)lithiation. Graphic abstract

36 MATERIALS SCIENCE↗

DOE OSTI2023

Geophysical Constraints on Decarbonized Systems—Building Spatio-Temporal Uncertainties into Future Electricity Grid Planning

Future electricity grids will be characterized by the high penetration of renewables to support the decarbonization process. Yet, this transition will further expose grids to a broad spectrum of geophysical forces, such as weather and climate, land and space, minerals and materials. Understanding their role is thus important to support the grid decarbonization process. Here, we synthesize the current body of knowledge on the relationship between geophysical constraints and electricity grid planning. Additionally, we show that there have been promising advances in the data, methods, and modelling tools needed to incorporate the effect of geophysical constraints on demand, resource availability, and grid operations. There are, however, multiple research avenues that deserve further attention. More system-specific and finer-scale analyses are necessary to better understand how spatio-temporal variability---and associated uncertainty---in geophysical forces affect grid planning. Moreover, we need a broader focus on the multi-sectoral implications of grid decarbonization efforts, so as to limit the risk of unintended consequences. Importantly, all these efforts are challenged by the computational requirements of existing power system models, which often limit our ability to characterize uncertainty, scale analyses across larger domains, or study the multi-sector implications of grid expansion decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Online Rapid Analysis of Laser Heterodyne Radiometer (LHR) Data Using the Planetary Spectrum Generator (PSG)

One of the biggest challenges in developing scientific instruments is not just the build and testing of an instrument, but the method for processing the data and producing a consistent, well characterized data product that can be confidently used by the public and scientific community. Raw data products are frequently an array of numbers that are a read-out of voltages. The challenge is to convert these arrays into meaningful information as well as remove noise and interferences. Because this can be a tedious and time-consuming, the goal is to automate the process so that data ca be processed rapidly and be available in real-time for event monitoring. Here we present a rapid analysis method for Laser Heterodyne Radiometer (LHR) data that can be used to analyze data taken from a range of LHR observation modes (column, limb, etc.). This online tool uses the versatile Planetary Spectrum Generator (PSG) and allows LHR users to quickly analyze their own data using a NASA Goddard Space Flight Center (GSFC) monitored capability to ensure quality and reproducibility in the data products. Background: Development of a miniaturized LHR (mini-LHR) as a ground instrument for measuring carbon dioxide (CO 2 ) and methane (CH 4 ) in the atmospheric column started in 2009 with the commercial availability of distributive feedback lasers (DFB) in wavelengths that could measure absorption of gases in the near-IR. As new DFB lasers, detectors and hardware emerged at different wavelengths, additional gases were added to the capability. The mini-LHR was adapted into an occultation-viewing CubeSat with Lawrence Livermore National Laboratory (LLNL) for observing CO 2 , CH 4 , and H 2 O in the limb and was launched in 2019. Other versions of the LHR technology have been designed for observation of water vapor in the lunar exosphere as well as observations of trace gases in planetary atmospheres and plumes from icy moons. The PSG is an online tool developed at GSFC that can be used for synthesizing Earth and planetary spectra (atmospheres and surfaces) over a broad range of wavelengths (0.1 μm to 100 mm) for any observatory, orbiter or lander. Spectra are simulated by combining several state-of-the-art radiative transfer models, spectroscopic databases, planetary databases, as well as modern-era retrospective analysis for research and applications, version 2 (MERRA-2) data set which provides meteorological inputs such as modeled surface pressure for calculating dry-air columns.

Emily Wilson↗

DOE OSTI2023

In situ characterization of material extrusion printing by near-infrared spectroscopy

Material extrusion printing of reactive resins and inks present a unique challenge due to the time-dependent nature of the rheological and chemical properties they possess. As a result, careful print optimization or process control is important to obtain consistent, high quality prints via additive manufacturing. Here, we present the design and use of a near-infrared (NIR) flow through cell for in situ chemical monitoring of reactive resins during printing. Differences between in situ and off-line benchtop measurements are presented and highlight the need for in-line monitoring capability. Additionally, in-line extrusion force monitoring and off-line post inspection using machine vision is demonstrated. By combining NIR and extrusion force monitoring, it is possible to follow cure reaction kinetics and viscosity changes during printing. When combined with machine vision, the ability to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, artificial intelligence-assisted quality control of both process and product. Together, these techniques form the building blocks of an optimized closed-loop process control strategy when complex reactive inks must be used to produce printed hardware.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Characterizing Sub-Cohorts via Data Normalization and Representation Learning

The process of identifying a cohort of interest is a very challenging task. It requires manually inspecting many patient records of complex structure that might include medical coding errors and missing data. This paper presents a computational pipeline for refining the process of cohort selection based on medical concepts recorded in the electronic health records (EHRs). The pipeline extracts EHR data for a given cohort and normalizes this data using standard vocabularies. Then a stacked denoising autoencoder is used to embed the normalized patient vectors in a low dimensional space, where the patients are subsequently clustered into sub-cohorts. The goal is to represent the cohort in a standard format and abstract variants of sub-populations. As a use-case, we applied the pipeline to 1.8 million Veterans diagnosed with major depressive disorder (MDD), and identified four meaningful sub-cohorts using the features learned by the autoencoder. Then, each sub-cohort was explored using a set of keywords for interpretation.

Rush III, Everett↗

DOE OSTI2021

Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes

Focal Area: Advanced methods to glean insights from complex data (Focal Area 3). Science Challenge: Fusing and interpreting the vast amount of data from disjoint sources for the purpose of elevating our understanding of aerosol-cloud interaction presents an enormous challenge and opportunity and is necessary to improve uncertainty quantification and predictions, especially for extreme events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Synthesis and Morphological Characterization of Electroless-Deposited Ni-P Coatings on Diamond Abrasives

Deposition of a coating on rough surfaces faces unique challenges due to the complexity of substrate morphology. In the present research, electroless deposition of a Ni-P coating was successfully deposited on diamond particles. Microtomography was conducted to study the deposition mechanisms. It revealed that the coating coverage rate on diamond particles was affected by the synergistic action of the deposition time, substrate morphology, and hypophosphite concentration. The best coverage was achieved in a solution with 0.2 mol/L hypophosphite. Two major morphological features of the coating: nodular and smooth, were influenced by the deposition parameters, coating integrity, and substrate morphology. The failure was seen in fractured and peeled off coatings. It was due to residual stress produced by the coalescing of crystallites during the deposition. This failure mechanism explains the tendency of coating fracture at three morphological features of the substrate. This work is beneficial to semiconductor manufacturing where effective cutting in chip fabrication is essential.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Novel materials irradiation and pre-characterization plans for high-power targetry applications

The multi-megawatt proton beams that will be employed at future accelerator complexes introduce many new challenges for next-generation targetry systems, primarily due to the increased levels of beam-induced radiation damage combined with thermal shock effects during a beam pulse. Novel material classes, such as high-entropy alloys and electro-spun nanofibers are currently being investigated as materials that will be more tolerant of these effects in high beam power applications. Low-energy ion irradiation will be used for pre-screening and down selecting candidate novel materials prior to prototypic high-energy irradiation studies. Specific nano and micro-scale characterization techniques will be used to evaluate the radiation damage and thermal shock resistance of the new materials. Irradiation induced lattice spacing alteration and defect concentrations, as well as mechanical and thermal properties of the novel materials will be measured and compared to those of conventional targetry materials. This poster will detail plans for initial low-energy ion irradiation tests, and the post-irradiation examination techniques that will be used to develop and screen these novel target materials.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Synthesis of Anhydrous Acetates for the Components of Nuclear Fuel Recycling in Dialkylimidazolium Acetate Ionic Liquids [plus supplemental information]

A series of anhydrous acetate salts with uranium {[C 2 C 1 im][UO 2 (OAc) 3 ] (1), [C 2 C 2 im][UO 2 (OAc) 3 ] (2), and [C 4 C 1 im][UO 2 (OAc) 3 ] (3)}, lanthanides {[C 2 C 2 im] 2 [La(OAc) 5 ] (4) and [C 2 C 1 im] 2 [Nd(OAc) 5 ] (5)}, and strontium {[C 2 C 1 im] n [Sr- (OAc) 3 ] n (6)} (where C 2 C 1 im = 1-ethyl-3-methylimidazolium, C 2 C 2 im = 1,3-diethylimidazolium, C 4 C 1 im = 1-butyl-3-methylimi-dazolium, and OAc = acetate) have been prepared and structurally characterized. Both lanthanides and strontium are common components of the nuclear fuel waste, and their separation from uranium is an important but still challenging task. A new synthetic method with dialkylimidazolium acetate ionic liquids (ILs) as the solvent has been developed for the direct synthesis of homoleptic acetates from the corresponding hydrates and, unexpectedly, hardly soluble f-element oxides. Although the group of characterized compounds shows perfect structural variability, all actinide and lanthanide metal ions form monomeric complex anions where the metal cation coordinates to five ligands including two oxygen atoms in the case of uranium, as is commonly observed for uranyl compounds. Crystallographic analyses revealed that the complex [UO 2 (OAc) 3 ] - anions possess rather standard D 3h symmetry featuring a hexagonal-bipyramidal coordination environment, while the lanthanide anions [Ln(OAc) 5 ] 2- are fully asymmetric and the Ln 3+ cations are 10-coordinated in the form of a distorted bicapped tetragonal antiprism. This is the first report of lanthanide ions coordinated in this fashion. For Sr 2+ , 9-fold coordination through oxygen atoms in the form of a strongly distorted tricapped trigonal prism is observed. Furthermore, the crystallization of anhydrous, homoleptic, anionic acetate complexes from such a large variety of different metal salts appears to be due to the properties of dialkylimidazolium acetate ILs themselves, including enhanced basicity from the high concentration of free anions and their greater affinity for hydrogen-bonding solutes relative to metal cations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Spatial Correlations of the Poisson Model for Radiation Transport

Characterizing the relationship between bulk physical properties and mixing in randomly heterogeneous media is a central challenge across many areas of science and engineering. A benchmark model for such studies is the Poisson model, a random tessellation of space by a Poisson process of hyperplanes. In radiation transport studies, the lack of exact expressions for the Poisson model’s spatial multipoint functions has led to approximate methods being used, introducing unquantified sources of error. Here, we recently introduced an exact solution for the Poisson model’s multipoint functions and closely related conditional probability functions (CPFs), providing a new opportunity to understand and reduce these sources of error. In this paper, we enable a more rigorous investigation of radiation transport in stochastic media by applying the recently introduced exact solution for the Poisson model’s CPFs. This paper consists of three main contributions. First, we introduce a unified framework for CPFs of the Poisson model, encompassing the recently introduced exact CPFs as well as the previously introduced atomic mix, nearest-neighbor, and combination CPFs. This framework also includes existing pruning techniques for the approximate CPFs, such as angular exclusion, as well as a novel form of angular exclusion suitable for the exact CPFs. Second, we use the exact CPFs to characterize the spatial regions where each approximate three-point CPF is most accurate, thereby explaining the observed hierarchy of accuracy among the approximate models. Finally, we evaluate material transmittance, reflectance, and flux in a three-dimensional test problem using conditional point sampling, demonstrating the relationship between CPF accuracy and transport simulation accuracy.

Poisson model↗

DOE OSTI2020

Self-Assembly of Metallo-Supramolecules under Kinetic or Thermodynamic Control: Characterization of Positional Isomers Using Scanning Tunneling Spectroscopy

Coordination-driven self-assembly has been extensively employed to construct a variety of discrete structures as a bottom-up strategy. However, mechanistic understanding regarding whether self-assembly is under kinetic or thermodynamic control is less explored. To date, such mechanistic investigation has been limited to distinct, assembled structures. It still remains a formidable challenge to study the kinetic and thermodynamic behavior of self-assembly systems with multiple assembled isomers due to the lack of characterization methods. Herein, we use a stepwise strategy which combined self-recognition and self-assembly processes to construct giant metallo-supramolecules with 8 positional isomers in solution. With the help of ultrahigh-vacuum, low-temperature scanning tunneling microscopy and scanning tunneling spectroscopy, we were able to unambiguously differentiate 14 isomers on the substrate which correspond to 8 isomers in solution. Through measurement of 162 structures, the experimental probability of each isomer was obtained and compared with the theoretical probability. Finally, such a comparison along with density functional theory (DFT) calculation suggested that although both kinetic and thermodynamic control existed in this self-assembly, the increased experimental probabilities of isomers compared to theoretical probabilities should be attributed to thermodynamic control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Initial Temperature Testing of Advanced Manufactured Melt Wire Package

As a part of the In-Pile Instrumentation Program, The Idaho National Laboratory (INL) has recently established in-house capabilities to fabricate and test new advanced manufactured sensors for measuring the peak irradiation temperature inside a nuclear test reactor. Although nuclear test reactors can use standard methods of real-time temperature monitoring, such as thermocouples, the complexity of feedthroughs and attachment complications to collect real-time measurements greatly increases the cost of the experiment. Passive monitoring techniques may be utilized that exploit the melting point of well characterized material (standard melt wires), however, the limited space available for instrumentation during experiments is also a challenge. To accommodate the challenge, INL has expanded its temperature detection instrumentation capabilities paired with the advancement of in-house advanced manufacturing options to include printed melt wires for peak irradiation temperature measurement. The advanced manufactured melt wire package can determine peak temperatures in experiments that can fit the challenging space available during irradiation. Recent results and knowledge from this ongoing work have made it possible to develop unique temperature sensors that can accomplish user specific temperature ranges inside the harsh environments experienced during radiation testing. This report summarizes the initial temperature testing using fabricated melt wires with two different melting temperatures, silver (melting temperature of ~960 °C) and solder (melting temperature of ~160 °C), that were initially tested for their relative melting points using non-standard method of furnace and hot plate testing under normal air conditions. Future work will include more controlled way of testing printed melt wires under inert gas with multiple temperature measurements followed by comparison of standard melt wire package performance for baseline characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

An instrument for in situ characterization of powder spreading dynamics in powder-bed-based additive manufacturing processes

In powder-bed-based metal additive manufacturing (AM), the visualization and analysis of the powder spreading process are critical for understanding the powder spreading dynamics and mechanisms. Unfortunately, the high spreading speeds, the small size of the powder, and the opacity of the materials present a great challenge for directly observing the powder spreading behavior. In this study, we report a compact and flexible powder spreading system for in situ characterization of the dynamics of the powders during the spreading process by high-speed x-ray imaging. The system enables the tracing of individual powder movement within the narrow gap between the recoater and the substrate at variable spreading speeds from 17 to 322 mm/s. The instrument and method reported here provide a powerful tool for studying powder spreading physics in AM processes and for investigating the physics of granular material flow behavior in a confined environment.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Importance of Standardizing Analytical Characterization Methodology for Improved Reliability of the Nanomedicine Literature

Understanding the interaction between biological structures and nanoscale technologies, dubbed the nano-bio interface, is required for successful development of safe and efficient nanomedicine products. The lack of a universal reporting system and decentralized methodologies for nanomaterial characterization have resulted in a low degree of reliability and reproducibility in the nanomedicine literature. As such, there is a strong need to establish a characterization system to support the reproducibility of nanoscience data particularly for studies seeking clinical translation. Here, we discuss the existing key standards for addressing robust characterization of nanomaterials based on their intended use in medical devices or as pharmaceuticals. We also discuss the challenges surrounding implementation of such standard protocols and their implication for translation of nanotechnology into clinical practice. We, however, emphasize that practical implementation of standard protocols in experimental laboratories requires long-term planning through integration of stakeholders including institutions and funding agencies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Crossflow membrane filtration system for operando fouling characterization using transmission x-ray scattering

Membrane-based separations are widely used for wastewater treatment due to their low cost and efficiency. However, membrane fouling, which is the unwanted deposition or attachment of contaminants on membrane surfaces and/or within membrane pores, remains a major challenge as it increases the mass transfer resistance and reduces membrane productivity. Membrane fouling is typically probed by macroscopic performance metrics, such as flux decline, and ex situ characterization. However, this does not capture the membrane and fouling layer evolution under operating conditions, potentially masking important mechanisms and nonequilibrium pathways that impact fouling. Here, we present a remotely controlled crossflow membrane system and a custom membrane cell for operando fouling characterization using transmission small/wide angle x-ray scattering (SAXS/WAXS). This approach allows direct observation of the nanoscale changes occurring at the membrane surface during pressurized water treatment processes, enabling a new way to understand the connections between dynamic fouling behaviors and membrane performance. Nanoparticle fouling of porous membranes during ultrafiltration was investigated using operando SAXS, and mineral scaling of reverse osmosis membranes was investigated using operando WAXS. Furthermore, this system allows for tracking membrane fouling in real time and under realistic conditions, providing fundamental physical insights into how water chemistry and operating conditions affect macroscopic membrane performance. Moreover, this system opens the door for future in situ and operando studies, and it serves as a testbed for evaluating novel materials/processes for membrane-based separations.

36 MATERIALS SCIENCE↗