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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Linking cases of illegal shootings of the endangered California condor using stable lead isotope analysis

Lead poisoning is preventing the recovery of the critically endangered California condor (Gymnogyps californianus) and lead isotope analyses have demonstrated that ingestion of spent lead ammunition is the principal source of lead poisoning in condors. Over an 8 month period in 2009, three lead-poisoned condors were independently presented with birdshot embedded in their tissues, evidencing they had been shot. No information connecting these illegal shooting events existed and the timing of the shooting(s) was unknown. Using lead concentration and stable lead isotope analyses of feathers, blood, and recovered birdshot, we observed that: i) lead isotope ratios of embedded shot from all three birds were measurably indistinguishable from each other, suggesting a common source; ii) lead exposure histories re-constructed from feather analysis suggested that the shooting(s) occurred within the same timeframe; and iii) two of the three condors were lead poisoned from a lead source isotopically indistinguishable from the embedded birdshot, implicating ingestion of this type of birdshot as the source of poisoning. One of the condors was subsequently lead poisoned the following year from ingestion of a lead buckshot (blood lead 556 µg/dL), illustrating that ingested shot possess a substantially greater lead poisoning risk compared to embedded shot retained in tissue (blood lead ∼20 µg/dL). To our knowledge, this is the first study to use lead isotopes as a tool to retrospectively link wildlife shooting events. - Highlights: • We conducted a case-based analysis of illegal shootings of California condors. • Blood and feather Pb isotopes were used to reconstruct the illegal shooting events. • Embedded birdshot from the three condors had the same Pb isotope ratios. • Feather and blood Pb isotopes indicated that the condors were shot in a common event. • Ingested shot causes substantially greater lead exposure compared to embedded shot.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Single Atomic Iron Catalysts for Oxygen Reduction in Acidic Media: Particle Size Control and Thermal Activation

It remains a grand challenge to replace platinum group metal (PGM) catalysts with earth-abundant materials for the oxygen reduction reaction (ORR) in acidic media, which is crucial for large-scale deployment of proton exchange membrane fuel cells (PEMFCs). Here, we report a high-performance atomic Fe catalyst derived from chemically Fe-doped zeolitic imidazolate frameworks (ZIFs) by directly bonding Fe ions to imidazolate ligands within 3D frameworks. Although the ZIF was identified as a promising precursor, the new synthetic chemistry enables the creation of well-dispersed atomic Fe sites embedded into porous carbon without the formation of aggregates. The size of catalyst particles is tunable through synthesizing Fe-doped ZIF nanocrystal precursors in a wide range from 20 to 1000 nm followed by one-step thermal activation. Similar to Pt nanoparticles, the unique size control without altering chemical properties afforded by this approach is able to increase the number of PGM-free active sites. The best ORR activity is measured with the catalyst at a size of 50 nm. Further size reduction to 20 nm leads to significant particle agglomeration, thus decreasing the activity. Using the homogeneous atomic Fe model catalysts, we elucidated the active site formation process through correlating measured ORR activity with the change of chemical bonds in precursors during thermal activation up to 1100 °C. The critical temperature to form active sites is 800 °C, which is associated with a new Fe species with a reduced oxidation number (from Fe3+ to Fe2+) likely bonded with pyridinic N (FeN4) embedded into the carbon planes. Further increasing the temperature leads to continuously enhanced activity, linked to the rise of graphitic N and Fe–N species. The new atomic Fe catalyst has achieved respectable ORR activity in challenging acidic media (0.5 M H2SO4), showing a half-wave potential of 0.85 V vs RHE and leaving only a 30 mV gap with Pt/C (60 μgPt/cm2). Enhanced stability is attained with the same catalyst, which loses only 20 mV after 10 000 potential cycles (0.6–1.0 V) in O2 saturated acid. The high-performance atomic Fe PGM-free catalyst holds great promise as a replacement for Pt in future PEMFCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

MACAW v1.0

The ability to embed molecules in a numeric space is essential in order to build mathematical and machine-learning models describing molecular properties or other processes affected by molecules. MACAW is a cheminformatic tool that allows embedding small molecules into a multidimensional numeric space. In the embedding, each molecule is assigned a numeric vector that captures information of the molecule in relation to other molecules, and that vector can be used as input to mathematical models. Molecules that are more similar to each other are embedded closer in this numeric space, whereas molecules that are more different are embedded further away. One advantage of the MACAW embedding technology compared to established alterantives is that it is fast and does not require extensive computational resources or expertise. In particular, MACAW embeddings can be used as input to mathematical models without the need for variable cleaning or feature selection, saving time and simplifying their use. On the other hand, MACAW also contains methods to generate new molecules and to recommend new molecules satisfying a desired molecular property. The generation of new molecules can be biased based on an input set of molecules, effectively generating molecular diversity around it. In its turn, MACAW's molecular recommendation tool is a novel method for evolving molecules in silico towards a desired molecular specification. In this method, the biased molecular generator tool is applied iteratively in combination with a molecular selection step. As a result, in each iteration the molecules selected by the software are increasingly closer to the desired specification. Both the molecular generation and the molecular recommendation tools are very fast, efficient, and intuitive to use.

Roger, VincentBlay↗
DOE OSTI2022

Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope

From neutron user principal investigator: We kindly request the public release of three neutron imaging datasets through ONCat. All datasets were collected from two forensic specimens, B12W and M8N, sectioned from zinc-filled steel-wire sockets recovered from the collapsed Arecibo Telescope. The dataset titled “Neutron radiographs of B12W and M8N socket sections of the Arecibo telescope” contains normalized two-dimensional (2D) neutron radiographs of the specimens, showing the geometry and spatial distribution of the steel wires embedded within the zinc matrix, as well as internal features such as voids and cracks. The dataset titled “Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope” contains normalized 2D neutron projection images acquired over a range of specimen rotation angles for one selected region of each specimen. These projection images were used to reconstruct three-dimensional (3D) tomographic volumes that reveal the embedded-wire geometry and internal defects. The dataset titled “Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope” contains six time-of-flight (TOF) neutron imaging datasets, three from each specimen, acquired at regions of interest selected based on the radiographs. The spatially resolved 2D TOF images show the zinc matrix and embedded steel wires, and the wavelength-dependent neutron transmission data were used to characterize crystallographic texture within the zinc. All components and their condition are in the public domain as they are the property of the National Science Foundation (NSF). The neutron imaging data, part geometries, and detailed forensic information have been widely published in the Arecibo Telescope Collapse Forensic Report by Thornton Tomasetti Engineers and others (NASA report and NASEM report).

Bilheux, Hassina↗
DOE OSTI2022

Teaching AI when to care about gender

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) concerned with solving language tasks by modeling large amounts of textual data. Some NLP techniques use word embeddings which are semantic models where machine learning (ML) is used to learn to cluster semantically related words by learning about word co-occurrences in the original training text. Unfortunately, these models tend to reflect or even exaggerate biases that are present in the training corpus. Here we describe the Word Embedding Navigator (WEN), which is a tool for exploring word embedding models. We examine a specific potential use case for this tool: interactive discovery and neutralization of gender bias in word embedding models, and compare this human-in-the-loop approach to reducing bias in word embeddings with a debiasing post-processing technique.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Large-scale additive manufacturing of self-heating molds

Large-scale material extrusion additive manufacturing technology is becoming the new mainstream technology for scaled-up composite mold and die applications. This paradigm shift in composite processing technology is primarily driven by out-of-autoclave tooling applications, in which fiber reinforced composite molds with scaled-up sizes and embedded heating elements are attractive. The present research describes the design, manufacturing, and testing of self-heating composite molds fabricated via a large-scale pellet extrusion 3D printing machine with an integrated wire co-extrusion tool. Polycarbonate (PC) composites reinforced with carbon fiber (PC/CF; 20 wt.%) and glass fiber (PC/GF; 20 wt.%) were used to fabricate mold parts. Joule heating thermal test results showed that uniform temperatures (~100 °C) were achieved for both PC/CF and PC/GF mold surfaces, using a custom-made feedback control power supply and infrared thermography. Mechanical characterizations, including tensile and flexural testing were performed on the wire-embedded and un-wired PC/CF and PC/GF base specimens to investigate the impact of the fiber reinforcement as well as the embedded wires. In the direction of extrusion, the ultimate tensile stress of PC/CF was 105 MPa, and that of PC/GF was 73 MPa, while the neat PC value was 64 MPa. Inner-bead voids and interfacial gaps were observed and characterized via optical and scanning electron microscopy. The embedded wires and inner bead impacted the mechanical properties of the composites. Furthermore, the stiffness of the wire-embedded mold was still satisfactory, proving that the technology can be used to fabricate additively manufactured out-of-oven/autoclave molds.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Efficient and flexible multirate temporal adaptivity

In this work we present two new families of multirate time step adaptivity controllers, that are designed to work with embedded multirate infinitesimal (MRI) time integration methods for adapting time steps when solving problems with multiple time scales. We compare these controllers against competing approaches on two benchmark problems, showing that the proposed methods offer dramatically improved performance and flexibility. The combination of embedded MRI methods and the proposed controllers enable adaptive simulations of problems with a potentially arbitrary number of time scales, achieving high accuracy while maintaining low computational cost. Additionally, we introduce a new set of embeddings for the family of explicit multirate exponential Runge–Kutta (MERK) methods of orders 2 through 5, resulting in the first-ever fifth-order embedded MRI method. Finally, we compare the performance of a wide range of embedded MRI methods on our benchmark problems to provide guidance on how to select an appropriate MRI method and multirate controller.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

QMMM 2023: A program for combined quantum mechanical and molecular mechanical modeling and simulations

Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. As a result, the adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Electronic and magnetic properties of substituted BN sheets: A density functional theory study

Using density functional calculations, we investigate the geometries, electronic structures and magnetic properties of hexagonal BN sheets with 3d transition metal (TM) and nonmetal atoms embedded in three types of vacancies: VB, VN, and VB+N. We show that some embedded configurations, except TM atoms in VN vacancy, are stable in BN sheet and yield interesting phenomena. For instance, the band gaps and magnetic moments of BN sheet can be tuned depending on the embedded dopant species and vacancy type. In particular, embedment such as Cr in VB+N, Co in VB, and Ni in VB leads to half-metallic BN sheets interesting for spin filter applications. From the investigation of Mn-chain (CMn) embedments, a regular 1D structure can be formed in BN sheet as an electron waveguide, a metal nanometer wire with a single atom thickness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Making many-body interactions nearly pairwise additive: The polarized many-body expansion approach

The Many-Body Expansion (MBE) is a useful tool to simulate condensed phase chemical systems, often avoiding the steep computational cost of usual electronic structure methods. However, it often requires higher than 2-body terms to achieve quantitative accuracy. In this work, we propose the Polarized MBE (PolBE) method where each MBE energy contribution is treated as an embedding problem. In each energy term, a smaller fragment is embedded into a larger, polarized environment and only a small region is treated at the high-level of theory using embedded mean-field theory. The role of polarized environment was found to be crucial in providing quantitative accuracy at the 2-body level. PolBE accurately predicts noncovalent interaction energies for a number of systems, including CO 2 , water, and hydrated ion clusters, with a variety of interaction mechanisms, from weak dispersion to strong electrostatics considered in this work. We further demonstrate that the PolBE interaction energy is predominantly pairwise unlike the usual vacuum MBE that requires higher-order terms to achieve similar accuracy. We numerically show that PolBE often performs better than other widely used embedded MBE methods such as the electrostatically embedded MBE. Finally,owing to the lack of expensive diagonalization of Fock matrices and its embarrassingly parallel nature, PolBE is a promising way to access condensed phase systems with hybrid density functionals that are difficult to treat with currently available methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

THE SHAPE OF LONG OUTBURSTS IN U GEM TYPE DWARF NOVAE FROM AAVSO DATA

We search the American Association of Variable Star Observers (AAVSO) archives of the two best-studied dwarf novae in an attempt to find light curves for long outbursts that are extremely well characterized. The systems are U Gem and SS Cyg. Our goal is to search for embedded precursors such as those that have been found recently in the high-fidelity Kepler data for superoutbursts (SOs) of some members of the SU UMa subclass of dwarf novae. For the vast majority of AAVSO data, the combination of low data cadence and large errors associated with individual measurements precludes one from making any strong statement about the shape of the long outbursts. However, for a small number of outbursts, extensive long-term monitoring with digital photometry yields high-fidelity light curves. We report the discovery of embedded precursors in two of three candidate long outbursts. This is the first time that such embedded precursors have been found in dwarf novae above the period gap in other than kepler data, and reinforces van Paradijs' finding that long outbursts in dwarf novae above the period gap and SOs in systems below the period gap constitute a unified class. The thermal-tidal instability to account for SOs in the SU UMa stars predicts embedded precursors only for short orbital period dwarf novae, therefore the presence of embedded precursors in long orbital period systems-U Gem and SS Cyg-argues for a more general mechanism to explain long outbursts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Electron-electron interactions in low-dimensional Si:P delta layers

Scientists have long studied the physics of highly disordered conducting systems, seeking to understand the multitude of quantum phenomena that govern how electrons move through material systems. Recently, research into silicon-based quantum computing has made disordered conducting systems, such as Si:P monolayers embedded in isotopically pure Si, technically relevant. Consequently, applying and advancing the theoretical frameworks developed to describe electron behavior in disordered systems is a necessary objective in this field of research. This study investigates key components of dopant-based Si quantum computing devices: embedded regions of highly doped delta layers (δ layers). We examine the transport behavior and the electron-electron interaction (EEI) physics in embedded Si:P δ layers by means of self-consistent magnetotransport measurements. Parameters associated with the electronic transport offer a meaningful quantitative characterization of δ-layer quality and dopant diffusion. In addition, by examining EEI behaviors in a set of samples with embedded Si:P δ layers produced with different PH3 exposure procedures prior to Si encapsulation, we show how details of material synthesis affect the dimensionality of charge carrier interactions in embedded Si:P δ layers. Furthermore, the relationship between δ-layer confinement and EEI screening lengths is established here. This understanding will help validate important models used for device simulation and design and lead to improvements in the control of electrostatic gating of and tunneling transport through Si:P single atom transistors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Fusion Model for Metagenomics

This work highlights the use of an embeddings approach that can encode multiple features and create efficient contextualization of profiled metagenomes derived from microbiome samples using computer vision models and image representations of the abundance profiles. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples.

Valdes, CamiloA [Lawrence Livermore National Labor↗
DOE OSTI2026

Mitigating Algorithmic Bias in Cancer Site Classification Models

Purpose Integrating artificial intelligence in cancer diagnostics has improved tumor classification beyond rule-based systems. Despite these advancements, these models may still encode demographic biases. We conducted a large-scale, applied bias-probing study of a deep learning–based cancer site classifier to quantify race information encoded in document embeddings. We then evaluated how performance changes when race-correlated embedding dimensions are removed in a post-training sensitivity analysis. Methods The cancer site classifier was trained using 3.5 million electronic cancer pathology reports from six of the National Cancer Institute's SEER registries. We trained a hierarchical self-attention network to generate 400-dimensional document embeddings. These embeddings were used to train two downstream, gradient-boosted decision tree classifiers: one to classify the cancer sites and another to predict racial categories. We identified overlapping features by intersecting the top 50 feature-importance rankings from the site and race models and computed their cumulative feature importance in each model. As a post hoc sensitivity analysis, we progressively pruned these overlapping dimensions, retrained the site model, and compared overall macro-F1 and accuracy, race-stratified macro-F1, and group fairness metrics on the basis of demographic parity and equalized odds before and after pruning. Results The analysis revealed minimal feature overlap between the cancer site and race prediction models, and the cumulative importance scores indicated a negligible influence of racial information on clinical predictions. Post-training pruning of overlapping features did not compromise the models' diagnostic accuracy, with a 0.07% loss in accuracy. Conclusion Our findings demonstrate that HiSAN-generated embeddings from SEER data can be used effectively in cancer site classification without significant demographic bias influencing the outcomes. Post-training pruning therefore functions as a practical audit and sensitivity check.

Shivanna, Abhishek [ORNL] (ORCID:0009000665228593)↗
DOE OSTI2023

Oxide Semiconductor Sensors for Monitoring Advanced Energy Conversion and Manufacturing Processes at High Temperature

To optimize the operation and functionality of high temperature systems such as slagging gasifiers, coal boilers and glass/steel smelters, it is important to monitor temperature, corrosion and erosion of the refractory used in such systems. Both thermocouple and failure sensors (and arrays of these) would be useful to monitor the health of any refractory or coatings in these systems. Many of such type of sensors are installed into the systems through open access ports within the refractory; however, there are some disadvantages of this approach where corrosive/erosive gas and molten materials can penetrate and compromise the system. The current work presents the development and performance of refractory with embedded high temperature sensors such as thermocouples, thermistors, and various spallation/crack monitoring sensors, which may be used within a variety of refractory brick in different high temperature processes and applications. The objective of our work is to develop high-temperature sensors composed of electroceramic materials that are chemically stable at high temperatures (750°-1500°C) and high pressures (up to 1000 psi) that can be used in monitoring temperature, corrosion and erosion process in refractory used in high energy systems. The high-temperature sensors investigated in this work were composed of various oxide composites directly embedded into the refractory oxides. The composites used for this work were synthesized by a mixed-oxide route. Metal oxides were inserted within a matrix material composed of refractory oxides (Al2O3, ZrO2, etc.). The physical and electrical properties were specifically manipulated by altering the level of percolation of the conductive species (metal oxides) within the refractory constituent (refractory oxide). An example of one of these embedded sensors consisted of an electroceramic-based thermocouple fabricated with two separate oxide composite compositions which were patterned to produce a couple within the interior of a refractory matrix. The thermocouple successfully displayed thermoelectric voltage trend (as a function of temperature), and the voltage was 220.0 mV near 1400 °C. In addition, corrosion tests on the refractory embedded sensors were performed. To evaluate corrosion in the refractory was exposed to high-temperature glass compositions over a 90 h period. The penetration of the glass through the refractory could be monitored by both an amperometric and voltametric based sensor. With this experiment, it was demonstrated that the embedded sensor could dynamically monitor the corrosion process, and thus, provide real-time feedback on both temperature and the refractory health.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Systems and methods for customizing kernel machines with deep neural networks

A method including receiving an input data set. The input data set can include one of a feature domain set or a kernel matrix. The method also can include constructing dense embeddings using: (i) Nyström approximations on the input data set when the input data set comprises the kernel matrix, and (ii) clustered Nyström approximations on the input data set when the input data set comprises the feature domain set. The method additionally can include performing representation learning on each of the dense embeddings using a multi-layer fully-connected network for each of the dense embeddings to generate latent representations corresponding to each of the dense embeddings. The method further can include applying a fusion layer to the latent representations corresponding to the dense embeddings to generate a combined representation. The method additionally can include performing classification on the combined representation. Other embodiments of related systems and methods are also disclosed.

Song, Huan↗
DOE OSTI2015

Encapsulation of ILW metals by melting technology - 15360

This paper outlines how encapsulation using melting technology can be performed and describe the positive effects in a handling and repository perspective. Based on a feasibility study of a conditioning method for reactor core components and other ILW metals with a high content of long-lived radionuclides, the paper gives an overview of the concept and how the method can be facilitated. The paper covers identification and analysis of alternative techniques that supports the aim from a repository perspective, which is to strengthen the defense in depth for long-lived waste to be disposed. Embedding of radioactive metallic ILW components can isolate the activated component from corrosion and leakage of radionuclides into the repository barrier systems. By implementing a system that delays the mobility of nuclides the leakage to the biosphere can be delayed and by then decreased significantly. Conditioning by embedding has potential to strengthen a repository concept significantly. It may in certain cases also reduce the requirements on the repository barriers. In addition to the above, embedding also serves as an efficient radiation protection, which simplifies post treatment handling of the waste. The concept can be applied on a wide spectrum of metallic components. Positive effects in a handling and repository perspective can be achieved if low level metal not subject to free release is used as embedding material, since conditioning of metallic ILW waste can be carried out at significant lower dose rates and without increasing the total amount to be disposed. The paper discuss why there are embedding alternatives for ILW metals that are considered to be both technically feasible and reasonable in cost both with regards to investment and operational cost. A cost-benefit analysis as well as a risk analysis is discussed in order to support the feasibility assessment of a facility for encapsulation of ILW metals by melting technology. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗