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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

X-ray fluorescence microscopy methods for biological tissues

Abstract Synchrotron-based X-ray fluorescence microscopy is a flexible tool for identifying the distribution of trace elements in biological specimens across a broad range of sample sizes. The technique is not particularly limited by sample type and can be performed on ancient fossils, fixed or fresh tissue specimens, and in some cases even live tissue and live cells can be studied. The technique can also be expanded to provide chemical specificity to elemental maps, either at individual points of interest in a map or across a large field of view. While virtually any sample type can be characterized with X-ray fluorescence microscopy, common biological sample preparation methods (often borrowed from other fields, such as histology) can lead to unforeseen pitfalls, resulting in altered element distributions and concentrations. A general overview of sample preparation and data-acquisition methods for X-ray fluorescence microscopy is presented, along with outlining the general approach for applying this technique to a new field of investigation for prospective new users. Considerations for improving data acquisition and quality are reviewed as well as the effects of sample preparation, with a particular focus on soft tissues. The effects of common sample pretreatment steps as well as the underlying factors that govern which, and to what extent, specific elements are likely to be altered are reviewed along with common artifacts observed in X-ray fluorescence microscopy data.

Pushie, M. Jake (ORCID:0000000174945427)↗
DOE OSTI2024

On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them

Abstract Before we attempt to (approximately) learn a function between two sets of observables of a physical process, we must first decide what the inputs and outputs of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of first deciding “the right quantities” to relate through such a function, and then proceeding to learn it. This is accomplished by first processing simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: records of multiple observation processes of the system. We determine (i) what subsets of observables are common between the observation processes (and therefore observable from each other, relatable through a function); and (ii) what information is unrelated to these common observables, therefore particular to each observation process, and not contributing to the desired function. Any data-driven technique can subsequently be used to learn the input–output relation—from k-nearest neighbors and Geometric Harmonics to Gaussian Processes and Neural Networks. Two particular “twists” of the approach are discussed. The first has to do with the identifiability of particular quantities of interest from the measurements. We now construct mappings from a single set of observations from one process to entire level sets of measurements of the second process, consistent with this single set. The second attempts to relate our framework to a form of causality: if one of the observation processes measures “now,” while the second observation process measures “in the future,” the function to be learned among what is common across observation processes constitutes a dynamical model for the system evolution.

Sroczynski, David W.↗
DOE OSTI2013

Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species

Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli . It is clear that new applications such as chemical sensing and event logging in complex environments will benefit from new host chassis. This study quantitatively compared how the same chemical event logger performed across four strains and three different microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads— Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two benchmark E. coli chassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was observed and quantified. These results were obtained via fluorescence from reporter proteins that were transcriptionally fused to the integrase and downstream recombinant region and via data-driven kinetic models. The Pseudomonads proved to be acceptable chassis for the operation of this event logger, which outperformed the common E. coli DH5α in many ways. This study advances an emerging frontier in synthetic biology that aims to build broad-host-range devices and understand the context by which different species can execute programmable genetic operations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Standardized protocols and procedures can precisely and accurately quantify non-structural carbohydrates

Non-structural carbohydrates (NSCs), the stored products of photosynthesis, building blocks for growth, and fuel for respiration, are central to plant metabolism, but their measurement is challenging. Differences in methods and procedures among laboratories can cause results to vary widely, limiting our ability to integrate and generalize patterns in plant C balance among studies. A recent assessment found that NSC concentrations measured for a common set of samples can vary by an order of magnitude, but sources for this variability were unclear. We measured a common set of nine plant material types, and two synthetic samples with known NSC concentrations, using a common protocol for sugar extraction and starch digestion, and three different sugar quantification methods (ion chromatography, enzyme, acid) in six laboratories. We also tested how sample handling, extraction solvent, and centralizing parts of the procedure in one laboratory affected results. NSC concentrations measured for synthetic samples were within about 11.5% of known values for all three methods. However, differences among quantification methods were the largest source of variation in NSC measurements for natural plant samples because the three methods quantify different NSCs. The enzyme method quantified only glucose, fructose, and sucrose, with ion chromatography we additionally quantified galactose, while the acid method quantified a large range of mono- and oligosaccharides. For some natural samples, acid method quantification doubled sugar relative to the other methods, demonstrating that trees allocate C to a range of sugar molecules. Sample handling had little effect on measurements, while ethanol sugar extraction improved accuracy over water extraction. Our results demonstrate that reasonable accuracy of NSC measurements can be achieved when different methods are used, as long as protocols are robust and standardized. Thus, we provide detailed protocols for the extraction, digestion and quantification of NSCs in plant samples, which should improve the comparability of NSC measurements among laboratories.

Landhäusser, Simon M.↗
DOE OSTI2022

Revisiting the dark matter interpretation of excess rates in semiconductors

In light of recent results from low-threshold dark matter detectors, we revisit the possibility of a common dark matter origin for multiple excesses across numerous direct detection experiments, with a focus on the excess rates in semiconductor detectors. We explore the interpretation of the low-threshold calorimetric excess rates above 40 eV in the silicon SuperCDMS Cryogenic Phonon Detector and above 100 eV in the germanium EDELWEISS Surface detector as arising from a common but unknown origin, and demonstrate a compatible fit for the observed energy spectra in both experiments, which follow a power law of index α = 3.4 3 - 0.06 + 0.11 . Despite the intriguing scaling of the normalization of these two excess rates with approximately the square of the mass number A 2 , we argue that the possibility of common origin by dark matter scattering via nuclear recoils is strongly disfavored, even allowing for exotic condensed matter effects in an as-yet unmeasured kinematic regime, due to the unphysically large dark matter velocity required to give comparable rates in the different energy ranges of the silicon and germanium excesses. We also investigate the possibility of inelastic nuclear scattering by cosmic ray neutrons, solar neutrinos, and photons as the origin, and quantitatively disfavor all three based on known fluxes of particles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

A high-transparency, micro-patternable chip for X-ray diffraction analysis of microcrystals under native growth conditions

A highly X-ray-transparent, silicon nitride-based device has been designed and fabricated to harvest protein microcrystals for high-resolution X-ray diffraction data collection using microfocus beamlines and XFELs. Microcrystals present a significant impediment to the determination of macromolecular structures by X-ray diffraction methods. Although microfocus synchrotron beamlines and X-ray free-electron lasers (XFELs) can enable the collection of interpretable diffraction data from microcrystals, there is a need for efficient methods of harvesting small volumes (<2 µl) of microcrystals grown under common laboratory formats and delivering them to an X-ray beam source under native growth conditions. One approach that shows promise in overcoming the challenges intrinsic to microcrystal analysis is to pair so-called ‘fixed-target’ sample-delivery devices with microbeam-based X-ray diffraction methods. However, to record weak diffraction patterns it is necessary to fabricate devices from X-ray-transparent materials that minimize background scattering. Presented here is the design of a new micro-diffraction device consisting of three layers fabricated from silicon nitride, photoresist and polyimide film. The chip features low X-ray scattering and X-ray absorption properties, and uses a customizable blend of hydrophobic and hydrophilic surface patterns to help localize microcrystals to defined regions. Microcrystals in their native growth conditions can be loaded into the chips with a standard pipette, allowing data collection at room temperature. Diffraction data collected from hen egg-white lysozyme microcrystals (10–15 µm) loaded into the chips yielded a complete, high-resolution (<1.6 Å) data set sufficient to determine a high-quality structure by molecular replacement. The features of the chip allow the rapid and user-friendly analysis of microcrystals grown under virtually any laboratory format at microfocus synchrotron beamlines and XFELs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

OFDM inspired waveforms for 5G

As the standardization activities are being formed to lay the foundation of 5G wireless networks, there is a common consensus on the need to replace the celebrated OFDM by a more effective air interface that better serves the challenging needs of 5G. The main reason that has made OFDM popular in the past is related to the fact that information symbols are carried over a number of pure tones/sinusoidal signals. Moreover, with the use of cyclic prefix (CP), it is assured that the information carrying tones are only affected by the channel (complex) gains at the respective frequencies. Accordingly, the channel effect can be trivially compensated for (equalized) in the frequency domain through a single complex tap per subcarrier. However, as network air interfaces become more complex and the demand for multiuser services grows, OFDM is found to be incapable of handling the inevitable loss of synchronization among users. In the recent past, two novel waveforms (namely, GFDM and C-FBMC) have been discussed in the literature to overcome this and other drawbacks of OFDM. Interestingly, and at the same time not surprising, these methods share a common fundamental property with OFDM: each data packet is made up of a number of tones that are modulated by information symbols. In this tutorial article, we build a common framework based on the said OFDM principle and derive GFDM and C-FBMC waveforms from this point of view. This derivation provides a new prospective that facilitates straightforward understanding of channel equalization and the application of these new waveforms to MIMO channels. As a result, it also facilitates derivation of new structures for more efficient synthesis/analysis of these waveforms than those that have been reported in the literature.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Systematic Characterization of Power Hardware-in-the-Loop Evaluation Platform Stability: Preprint

This paper presents a systematic approach to characterize the stability of a power-hardware-in-the-loop (PHIL) platform, an important step in PHIL tests. Many existing works focus the stability assessments on the PHIL interface algorithm; however, this work considers all software and hardware subsystems that form the closed loop of the PHIL experiment and develops a complete closed-loop stability assessment. This assessment is developed in the context of a common framework that can be readily applied to other PHIL platforms. This paper presents methods for characterizing key PHIL subsystems toward obtaining transfer functions to be used for analysis. The systematic stability assessment approach is demonstrated for a case study involving PHIL testing of a solar inverter and validated using experimental data. assessment. This assessment is developed in the context of a common framework that can be readily applied to other PHIL platforms. This paper presents methods for characterizing key PHIL subsystems toward obtaining transfer functions to be used for analysis. The systematic stability assessment approach is demonstrated for a case study involving PHIL testing of a solar inverter and validated using experimental data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Case Study: Resilience Benefits of Distributed Wind Against Fuel and Weather Hazards in Alaska

In this case study of St. Mary’s Village, Alaska, we present a resilience evaluation exercise. A resilience framework is employed to identify system characteristics, relevant metrics, and resilience hazards and to assess the performance against the hazards with and without a distributed wind system installed. The results show the resilience benefits provided by the distributed wind installation against fuel shortage hazards and cold weather hazards. The resilience benefits can be assigned monetary values, which will be highly dependent on actual circumstances of the hazard, but provide insight into value streams of distributed wind that are not usually considered. For example, the single 900 kW turbine was found to prevent an average of 14,643 kWh of load from being dropped during a two-day diesel fuel shortage event, which saved the community $447,592 from the prevented outages. This case study serves as an example for novel power system resilience analysis and builds understanding of resilience hazards that are common across many power systems.

Culler, Megan J.↗
DOE OSTI2020

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

“Understanding Robustness Lottery”: A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches

Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks are very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but our understanding of the pruning process remains limited due to the black-box nature of a neural network model. Empirical studies show that some heuristics improve performance whereas others can make models more brittle. Here, this work aims to shed light on how different pruning methods alter the network’s internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We evaluated a set of critical geometric concepts decomposed from the commonly adopted classification loss and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for an in-depth comparison of pruning methods and a comprehensive understanding of how the model responds to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundancy in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption.

Li, Zhimin [Univ. of Utah, Salt Lake City, UT (Uni↗
DOE OSTI2013

Why Do Global Long-term Scenarios for Agriculture Differ? An overview of the AgMIP Global Economic Model Intercomparison

Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. Ten global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socio-economic, climate change and bioenergy scenarios using a common set of key drivers. Results suggest that, once general assumptions are harmonized, the variability in general trends across models declines, and that several common conclusions are possible. Nonetheless, differences in basic model parameters, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. This holds for both the common reference scenario and for the various shocks applied. We conclude that agro-economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross-fertilize analyses at different scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Design of an Ara h 2 hypoallergen from conformational epitopes

Abstract Introduction Adverse reactions are relatively common during peanut oral immunotherapy. To reduce the risk to the patient, some researchers have proposed modifying the allergen to reduce IgE reactivity, creating a putative hypoallergen. Analysis of recently cloned human IgG from patients treated with peanut immunotherapy suggested that there are three common conformational epitopes for the major peanut allergen Ara h 2. We sought to test if structural information on these epitopes could indicate mutagenesis targets for designing a hypoallergen and evaluated the reduction in IgE binding via immunochemistry and a mouse model of passive cutaneous anaphylaxis (PCA). Methods X‐ray crystallography characterized the conformational epitopes in detail, followed by mutational analysis of key residues to modify monoclonal antibody (mAb) and serum IgE binding, assessed by ELISA and biolayer interferometry. A designed Ara h 2 hypoallergen was tested for reduced vascularization in mouse PCA experiments using pooled peanut allergic patient serum. Results A ternary crystal structure of Ara h 2 in complex with patient antibodies 13T1 and 13T5 was determined. Site‐specific mutants were designed that reduced 13T1, 13T5, and 22S1 mAbs binding by orders of magnitude. By combining designed mutations from the three major conformational bins, a hexamutant (Ara h 2 E46R, E89R, E97R, E114R, Q146A, R147E) was created that reduced IgE binding in serum from allergic patients. Further, in the PCA model where mice were primed with peanut allergic patient serum, reactivity upon allergen challenge was significantly decreased using the hexamutant. Conclusion These studies demonstrate that prior knowledge of common conformational epitopes can be used to engineer reduced IgE reactivity, an important first step in hypoallergen design.

Allergy↗
DOE OSTI2024

Tracking animal movements via collaborative acoustic telemetry networks: Multiscale habitat use, phenology, and management insights

Abstract Estuaries support diverse fish and invertebrate communities, including resident species that rely on estuarine habitats year‐round and transient migratory species. The unique movement patterns of these animals connect habitats within and far beyond the estuary and are integrally linked to fisheries management objectives. With a focus on Chesapeake Bay, this study leveraged data from collaborative acoustic telemetry networks in the northwest Atlantic to assess habitat use and phenology of movements for seven species of fish (cownose rays, dusky sharks, smooth dogfish, alewife, striped bass, common carp, and blue catfish) and one invertebrate (horseshoe crabs). A total of 288 acoustically tagged individuals were detected >3.2 million times (6,743 to 2,095,717 detections per species) on receivers across ~20.5 degrees of latitude spanning the North American Atlantic seaboard from Florida, USA, to New Brunswick, Canada. Common metrics of movement and phenology grouped these species as resident (common carp, blue catfish, horseshoe crabs), primarily resident in estuaries (juvenile striped bass), and coastal migrant (cownose rays, dusky sharks, smooth dogfish, alewife); maximum distance traveled varied by three orders of magnitude among these species. Further analysis of phenology for coastal migrants elucidated the timing and duration of these species' use of Chesapeake Bay. Collectively, movements linked habitats within Chesapeake Bay and connected the estuary to coastal ecosystems both to the north (e.g., alewife) and south (e.g., cownose rays), creating networks of fisheries management jurisdictions that varied in complexity and identified opportunities for enhancement to current management or co‐management of some species. Our results elucidate the importance of estuaries to species with diverse movement behaviors, identify scales and pathways of habitat connectivity via animal movements, and highlight the utility of collaborative acoustic telemetry networks for quantifying movements relevant to both ecological research and fisheries management.

Livernois, Mariah C.↗
DOE OSTI2016

Genome-wide association study reveals putative regulators of bioenergy traits in Populus deltoides

Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. Lastly, these polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.

09 BIOMASS FUELS↗
DOE OSTI2022

Accumulator Sizing for the Hybrid Hydraulic Electric Architecture (HHEA) Using Dynamic Programming

Abstract The Hybrid Hydraulic Electric Architecture (HHEA) is an approach to electrifying and improving that does not require very large electric machines. The majority power is provided hydraulically via a set of common pressure rails while small electric machines are used to modulate that power to meet exact demand. Each common pressure rail has an accumulator, which keeps the pressure of each rail near constant as long as the volume in the accumulator does not change much. A main pump adds or removes fluid from each accumulator as needed to reduce volume changes in the accumulators. It is the operation of this main pump which is investigated here. In previous analysis it was assumed that the size of the accumulators was large enough to account for any difference in fluid volume without significantly changing the pressure. In this paper, the required accumulator sizes are studied using a dynamic programming approach. At each time step the inlet and outlet of the main pump could be connected to any of the common pressure rails. These decisions for the main pump were optimized to minimize the required accumulator size. Case studies are conducted using drive cycles from a 22-ton excavator, a 20-ton wheel loader and a 5-ton excavator. The machines could be operated with total accumulator sizes of less than 20, 10, and 5 liters respectively. Required accumulator size was found to vary with pump flow rate, the frequency at which the pump/motor was able to change operating conditions, the tolerance level of pressure deviation and the accumulator’s polytropic index.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

X-ray phase contrast imaging of the breast: Analysis of tissue simulating materials

Purpose: Phase contrast imaging, particularly of the breast, is being actively investigated. The purpose of this work is to investigate the x-ray phase contrast properties of breast tissues and commonly used breast tissue substitutes or phantom materials with an aim of determining the phantom materials best representative of breast tissues. Methods: Elemental compositions of breast tissues including adipose, fibroglandular, and skin were used to determine the refractive index, n= 1 -{delta}+i {beta}. The real part of the refractive index, specifically the refractive index decrement ({delta}), over the energy range of 5-50 keV were determined using XOP software (version 2.3, European Synchrotron Radiation Facility, France). Calcium oxalate and calcium hydroxyapatite were considered to represent the material compositions of microcalcifications in vivo. Nineteen tissue substitutes were considered as possible candidates to represent adipose tissue, fibroglandular tissue and skin, and four phantom materials were considered as possible candidates to represent microcalcifications. For each material, either the molecular formula, if available, or the elemental composition based on weight fraction, was used to determine {delta}. At each x-ray photon energy, the absolute percent difference in {delta} between the breast tissue and the substitute material was determined, from which three candidates were selected. From these candidate tissue substitutes, the material that minimized the absolute percent difference in linear attenuation coefficient {mu}, and hence {beta}, was considered to be best representative of that breast tissue. Results: Over the energy range of 5-50 keV, while the {delta} of CB3 and fibroglandular tissue-equivalent material were within 1% of that of fibroglandular tissue, the {mu} of fibroglandular tissue-equivalent material better approximated the fibroglandular tissue. While the {delta} of BR10 and adipose tissue-equivalent material were within 1% of that of adipose tissue, the tissue-equivalent material better approximated the adipose tissue in terms of {mu}. Polymethyl methacrylate, a commonly used tissue substitute, exhibited {delta} greater than fibroglandular tissue by {approx}12%. The A-150 plastic closely approximated the skin. Several materials exhibited {delta} between that of adipose and fibroglandular tissue. However, there was an energy-dependent mismatch in terms of equivalent fibroglandular weight fraction between {delta} and {mu} for these materials. For microcalcifications, aluminum and calcium carbonate were observed to straddle the {delta} and {mu} of calcium oxalate and calcium hydroxyapatite. Aluminum oxide, commonly used to represent microcalcifications in the American College of Radiology recommended phantoms for accreditation exhibited {delta} greater than calcium hydroxyapatite by {approx}23%. Conclusions: A breast phantom comprising A-150 plastic to represent the skin, commercially available adipose and fibroglandular tissue-equivalent formulations to represent adipose and fibroglandular tissue, respectively, was found to be best suited for x-ray phase-sensitive imaging of the breast. Calcium carbonate or aluminum can be used to represent microcalcifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗