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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Characterization of elastic mechanical properties of Tuscaloosa Marine Shale from well logs using the vertical transversely isotropic model

To avoid steep declines in the Tuscaloosa Marine Shale (TMS) production, wells are fracture-stimulated to release the hydrocarbons trapped in the matrix of the formation. An accurate estimation of Young’s modulus and Poisson’s ratio is essential for hydraulic fracture propagation. In addition, ignoring the highly heterogeneous and anisotropic character of TMS can lead to erroneous stress values, which subsequently affect hydraulic fracture width estimates and the overall hydraulic fracturing process. We have developed an empirical 1D geomechanical model that takes into account VTI anisotropy, and it is used to characterize the elastic mechanical properties of TMS in two wells. In the analyzed formation, the vertical Poisson’s ratio is less than the horizontal Poisson’s ratio, which suggests the necessity of an alternative to the ANNIE equations. The stiffness coefficients [Formula: see text] and [Formula: see text] were estimated using the relationships developed from the ultrasonic core data available for the two TMS. Further, correlations between the static and dynamic properties from laboratory tests were used to improve the minimum horizontal stress calculation. We compare VTI Young’s moduli, Poisson’s ratios, and minimum horizontal stress with the isotropic solution. VTI modeling improves the estimation of the elastic mechanical properties. The isotropic solution underestimates the minimum horizontal stress in the formation. Moreover, it was shown that the 20 ft shale interval below the TMS base is characterized by a low Young’s modulus (the vertical Young’s modulus is equal to 20 GPa, whereas the horizontal Young’s modulus is equal to 40 GPa) and may be a frac barrier.

Geochemistry & Geophysics↗
DOE OSTI2026

Evaluating alternative steam generation pathways in the food industry

Steam generation in the food sector requires substantial energy and cost expenditures, requiring nearly half of its energy intake. Here, we used life cycle assessment and life cycle cost assessment to investigate the cost and energy impacts of steam generating alternatives: NG, biomass and hydrogen boilers and grid-supplied and self-generated electric steam generation systems (electric boilers, renewable thermal energy storage and industrial heat pumps). The analysis starts with a set of average U.S. conditions, where biomass boilers are the most cost-competitive alternative to NG. In a series of scenarios beyond average conditions, the analysis shows energy procurement costs dominate the total life cycle cost for all technologies and, unfortunately, are highly variable geographically and temporally, significantly affecting the viability of the alternatives. Results show that site-energy consumption ranges from 0.3 MMBtu/klb for industrial heat pumps to 1.6 MMBtu/klb for biomass boilers, with heat pumps achieving up to 78% lower energy use compared to natural gas systems. For the steam costs, the results show a range between $\$$8 and $\$$50/klb for NG, with biomass following closely ($\$$11 – $\$$44/klb) and grey hydrogen and IHP next ($\$$13 – $\$$33/klb and $\$$6 - $\$$78/klb), with cost reductions if IHP's cooling is utilized. Factors like operating hours, the need for cooling, and the ability to negotiate utility rates complicate the decision, making site-specific analyses critical. Therefore, we present a decision-making matrix to help manufacturers identify which steam generating technology is the best business decision for their situation. Overall, these results highlight the importance of steam generation for the facility's organizational goals, as well as the criticality of conducting individual site analyses.

Biomass boilers↗
DOE OSTI2021

Structural insight into host plasma membrane association and assembly of HIV-1 matrix protein

Oligomerization of Pr55 Gag is a critical step of the late stage of the HIV life cycle. It has been known that the binding of IP6, an abundant endogenous cyclitol molecule at the MA domain, has been linked to the oligomerization of Pr55 Gag . However, the exact binding site of IP6 on MA remains unknown and the structural details of this interaction are missing. Here, we present three high-resolution crystal structures of the MA domain in complex with IP6 molecules to reveal its binding mode. Additionally, extensive Differential Scanning Fluorimetry analysis combined with cryo- and ambient-temperature X-ray crystallography and GNM-based transfer entropy calculations identify the key residues that participate in IP6 binding. Our data provide novel insights about the multilayered HIV-1 virion assembly process that involves the interplay of IP6 with PIP2, a phosphoinositide essential for the binding of Pr55 Gag to membrane. IP6 and PIP2 have neighboring alternate binding sites within the same highly basic region (residues 18–33). This indicates that IP6 and PIP2 bindings are not mutually exclusive and may play a key role in coordinating virion particles’ membrane localization. Based on our three different IP6-MA complex crystal structures, we propose a new model that involves IP6 coordination of the oligomerization of outer MA and inner CA domain’s 2D layers during assembly and budding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Tensor Decompositions for Count Data that Leverage Stochastic and Deterministic Optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the global maximum likelihood estimator from local minima. Simultaneously, a recent trend in theoretical computer science and numerical linear algebra leverages randomization to solve very large, hard problems. The typical approach is to use randomization for a fast approximation and determinism for refinement to yield effective algorithms with theoretical guarantees. Two popular algorithms for Poisson CPD reflect that emergent dichotomy: CP Alternating Poisson Regression is a deterministic algorithm and Generalized Canonical Polyadic decomposition makes use of stochastic algorithms in several variants. This work extends recent work to develop two new methods that leverage randomized and deterministic algorithms for improved accuracy and performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Designing Imidazolium Poly(amide-amide) and Poly(amide-imide) Ionenes and Their Interactions with Mono- and Tris(imidazolium) Ionic Liquids

Here we introduce the synthesis and thermal properties of a series of sophisticated imidazolium ionenes with alternating amide-amide or amide-imide backbone functionality, and investigate the structural effects of mono(imidazolium) and unprecedented tris(imidazolium) ionic liquids (ILs) in these ionenes. The new set of poly(amide-amide) (PAA) and poly(amide-imide) (PAI) ionenes represent the intersection of conventional high-performance polymers with the ionene archetype–presenting polymers with alternating functional and ionic elements precisely sequenced along the backbone. The effects of polymer composition on the thermal properties and morphology were analyzed. Five distinct polymer backbones were synthesized and combined with a stoichiometric equivalent of the IL 1-benzyl-3-methylimidazolium bistriflimide ([Bnmim][Tf 2 N]), which were studied to probe the self-assembly, structuring, and contributions of intermolecular forces when IL is added. Furthermore, three polyamide (PA) or polyimide (PI) ionenes with simpler xylyl linkages were interfaced with [Bnmim][Tf 2 N] as well as a novel amide-linked tris(imidazolium) IL, to demonstrate the structural changes imparted by the inclusion of functional, ionic additives dispersed within the ionene matrix. This work highlights the possibilities for utilizing concepts from small molecules which exhibit supramolecular self-assembly to guide creative design and manipulate the structuring of ionenes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Adhesion in thermomechanically processed seaweed-lignocellulosic composite materials

The increasing concerns associated with petroleum-derived polymers motivate the development of sustainable, renewably sourced alternatives. In ubiquitous applications such as structural materials for infrastructure, the built environment as well as packaging, where natural materials such as wood are used, we rely on nonrenewable and nondegradable polymers to serve as adhesives. In wood panels, such as medium density fiberboards (MDFs), formaldehyde-based resins are predominantly used to bond wood fibers and to provide strength to the materials. To further mitigate the environmental impact of construction materials, more sustainable adhesives need to be investigated. Here, in this article, we introduce Ulva seaweed as an adhesive to enable cohesion and strength in hot-pressed wood panels. Upon hot-pressing, powdered Ulva flows in between the wood particles, generating a matrix, which provides strong binding. We show that the flexural strength of Ulva-bonded wood biocomposites increases with increasing Ulva concentrations. At an Ulva concentration of 40 wt%, our composites reach an average elastic modulus of 6.1 GPa, and flexural strength of 38.2 MPa (compared to 4.7 GPa and 22.6 MPa, respectively, for pure wood compressed at the same pressing conditions). To highlight the bonding mechanisms, we performed infrared and x-ray photoelectron spectroscopy and identified indications of fatty acid mobility during hot-pressing. In addition, we demonstrate that the presence of Ulva improves other properties of the composites such as water resistance and fame retardancy. Ulva is also shown to behave as an excellent adhesive agent between two prepressed beams. Finally, we perform an in-depth analysis of the environmental impact of wood-Ulva biocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Annual report for DOE VTO

Carbon fiber (CF)/polymer composites are a transformative class of high-performance, lightweight material, where high aspect-ratio CFs reinforce a polymer matrix and exceed the strength of steel alloys at a fraction of the density. Despite the advantages of such a class of material, the broader implementation of CF composites in a range of automotive, aerospace, and energy applications is hindered by limitations of current manufacturing methods. These current techniques (e.g., hand lay-up, wet filament winding) are costly and impose severe limitations on fiber placement, orientation, and angle, and thus a composite’s ultimate properties. Today’s CF composites are expensive to manufacture, limited in form factor, and utilize costly and sub-optimal continuous filament CF. Advanced additive manufacturing (AM) processes, combined with computational design optimization and new approaches to resin development, offer alternative design and manufacturing paradigms that have the realistic potential to lift these constraints. Such integrated AM approaches could thus help to realize the full potential of CF composite materials. One relevant application of CF composite materials where manufacturing constraints limit the cost-benefit ratio is in the manufacture of high-performance composite pressure vessels for onboard compressed natural gas (CNG) storage. Current CNG storage vessels (Types 3–5) are made from load-bearing filament-wound carbon-fiber composite and are ~3.5 times as expensive as an all-metallic Type-1 vessel. This cost is invariably tied to the complex and labor-intensive nature of conventional filament winding processes and the large volumes of expensive high tensile-strength CF tow feedstock required in manufacture. Our proposed approach to CNG storage vessel manufacture is based on a combination of AM technologies for CF composite printing and design optimization tools that were pioneered at Lawrence Livermore National Laboratory (LLNL), with advances in resin/composite formulation enabled by chemical and nano-material modification. Through the successful development of this technology, LLNL seeks to demonstrate the capability for advanced CNG storage vessel manufacture at reduced cost with no reduction in performance versus the most advanced, extant Type-5 designs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Time-discretization of a plasma-neutral MHD model with a semi-implicit leapfrog algorithm

The semi-implicit leapfrog time-discretization is a workhorse algorithm for initial-value MHD codes to bridge between vastly separated time scales. Inclusion of atomic interactions with neutrals breaks the functional structure of the MHD equations that exploited by the leapfrog. In this work, we address how to best integrate atomic physics into the semi-implicit leapfrog. Following the Crank-Nicolson method, one approach is to time-center the atomic interactions in the linear solver and use a Newton method to include the nonlinear contributions. Alternatively, another family of methods are based on operator-splitting the terms associated with the atomic interactions using a Strang-splitting technique. These methods naturally break equations into constituent ODE and PDE parts and preserve the structure exploited by the semi-implicit leapfrog. We study the accuracy and efficiency of these methods through a battery of 0D and 1D cases and show that a second-order-in-time Douglas-Rachford inspired coupling between the ODE and PDE advances is effective in reducing the time-discretization error to be comparable to that of Crank-Nicolson with Newton iteration of the nonlinear terms. Splitting ODE and PDE parts results in independent matrix solves for each field which reduces the computational cost considerably and provides parallelization over species relative to Crank-Nicolson.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Soft, malleable double diamond twin

A twin boundary (TB) is a common low energy planar defect in crystals including those with the atomic diamond structure (C, Si, Ge, etc.). We study twins in a self-assembled soft matter block copolymer (BCP) supramolecular crystal having the double diamond (DD) structure, consisting of two translationally shifted, interpenetrating diamond networks of the minority polydimethyl siloxane block embedded in a polystyrene block matrix. The coherent, low energy, mirror-symmetric double tubular network twin has one minority block network with its nodes offset from the (222) TB plane, while nodes of the second network lie in the plane of the boundary. The offset network, although at a scale about a factor of 10 3 larger, has precisely the same geometry and symmetry as a (111) twin in atomic single diamond where the tetrahedral units spanning the TB retain nearly the same strut (bond) lengths and strut (bond) angles as in the normal unit cell. In DD, the second network undergoes a dramatic restructuring—the tetrahedral nodes transform into two new types of mirror-symmetric nodes (pentahedral and trihedral) which alternate and link to form a hexagonal mesh in the plane of the TB. The collective reorganization of the supramolecular packing highlights the hierarchical structure of ordered BCP phases and emphasizes the remarkable malleability of soft matter.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2024

Simplified Universal Equations for Ionic Conductivity and Transference Number

Nernst-Einstein equation can provide a reasonable estimate of the ionic conductivity of dilute solutions. For concentrated solutions, alternate methods such as Green–Kubo relations and Einstein relations are more suitable to account for ion-ion interactions. Such computations can be expensive for multicomponent systems. Simplified mathematical expressions like the Nernst-Einstein equation do not exist for concentrated multicomponent mixtures. Newman’s treatment of multicomponent concentrated solutions yields a conductivity relation in terms of species concentration and Onsager phenomenological coefficients. However, the estimation of these phenomenological coefficients is not straightforward. Here, mathematical formulations that relate the phenomenological coefficients with the friction coefficients are developed, leading to simplified, ready-to-use expressions of conductivity and transference numbers that can be used for a wide range of ionic mixtures. This approach involves spectral decomposition of the matrix of Onsager phenomenological coefficients. The general analytical expressions for conductivity and transference number are simplified for binary electrolytes, and numerical solutions are provided for ternary and quaternary mixtures with ion dissociation.

Electrochemistry↗
DOE OSTI2026

Enzymatic Biocontrol of Fire Blight ( Erwinia amylovora ) Using an Engineered Glycosyl Hydrolase

Current management of fire blight, caused by Erwinia amylovora , relies heavily on streptomycin a practice that has contributed to the emergence of antibiotic-resistant strains and raised environmental and regulatory concerns. Enzyme-based biocontrol agents offer a promising antibiotic-free approach that combines target specificity with environmental compatibility. This study evaluates CAase, a bacteriophage-derived glycosyl hydrolase, for its ability to disrupt E. amylovora biofilms and reduce disease severity. Biochemical assays and scanning electron microscopy confirmed that CAase efficiently degraded the extracellular polysaccharide (EPS) matrix, releasing cells from biofilms. Gas chromatography–mass spectrometry (GC–MS) linkage analysis of EPS isolated from two Erwinia amylovora strains demonstrated that CAase preferentially cleaves galactan-rich amylovoran produced by strain EA273, while exhibiting only limited activity toward the levan-rich EPS from strain EA1430. Functional assays revealed that CAase reduced bacterial viability by nearly 2 orders of magnitude at higher enzyme concentrations, strongly suppressed surface motility, and induced ultrastructural damage visible by transmission electron microscopy. Importantly, field trials showed that CAase significantly lowered blossom and shoot blight incidence under orchard conditions. These results highlight CAase as a potent enzyme-based strategy for reducing the virulence of E. amylovora and demonstrate its potential as a sustainable alternative to antibiotics in fire blight management.

antibiotic resistance↗
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 OSTI2020

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs), key components of crucial nutrients, soaps, industrial chemicals, and fuels. Yet, our ability to control the specificity of microbially synthesized FFAs is still limited for medium-chain fatty acids (MCFAs), which is mainly due to lack of high-throughput approaches for FFA analysis. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. Specially, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and the m/z peak ratio at 730 and 758 was used as an index for improved MCFA production. This yeast colony-based method can be performed at a rate of ~2 second per sample, which is much faster than the gold standard gas chromatography (GC)-MS (typically > 30 min per sample). To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified 9 mutants that resulted in improved MCFA production relative to the wild-type strain. This colony-based MALDI-ToF MS screening method offers an alternative way for engineering microbial fatty acid compositions in a high-throughput manner.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

On the discretization error of the discrete generalized quantum master equation

The transfer tensor method (TTM) [Cerrillo and Cao, Phys. Rev. Lett. 112 , 110401 (2014)] can be considered a discrete-time formulation of the Nakajima–Zwanzig quantum master equation (NZ-QME) for modeling non-Markovian quantum dynamics. A recent paper [Makri, J. Chem. Theory Comput. 21 , 5037 (2025)] raised concerns regarding the consistency of the TTM discretization, particularly a spurious term at the initial time t = 0. Here, this work presents a detailed analysis of the discretization structure of the TTM, clarifying the origin of the initial-time correction and establishing a consistent relationship between the TTM discrete-time memory kernel K N and the continuous-time NZ-QME kernel $\mathscr{K}$( N Δ t ). This relationship is validated numerically using the spin-boson model, demonstrating convergence of reconstructed memory kernels and accurate dynamical evolution as Δ t → 0. While the TTM provides a consistent discretization, we note that alternative schemes are also viable, such as the midpoint derivative/midpoint integral scheme proposed in Makri’s work. The relative performance of various schemes for either computing accurate $\mathscr{K}$( N Δ t ) from exact dynamics or obtaining accurate dynamics from exact $\mathscr{K}$( N Δ t ) warrants further investigation.

Density-matrix↗
DOE OSTI2021

Time-Lapse Integration at FWU: Fluids, Rock Physics, Numerical Model Integration, and Field Data Comparison

We present the current status of time-lapse seismic integration at the Farnsworth (FWU) CO2 WAG (water-alternating-gas) EOR (Enhanced Oil Recovery) project at Ochiltree County, northwest Texas. As a potential carbon sequestration mechanism, CO2 WAG projects will be subject to some degree of monitoring and verification, either as a regulatory requirement or to qualify for economic incentives. In order to evaluate the viability of time-lapse seismic as a monitoring method the Southwest Partnership (SWP) has conducted time-lapse seismic monitoring at FWU using the 3D Vertical Seismic Profiling (VSP) method. The efficacy of seismic time-lapse depends on a number of key factors, which vary widely from one application to another. Most important among these are the thermophysical properties of the original fluid in place and the displacing fluid, followed by the petrophysical properties of the rock matrix, which together determine the effective elastic properties of the rock fluid system. We present systematic analysis of fluid thermodynamics and resulting thermophysical properties, petrophysics and rock frame elastic properties, and elastic property modeling through fluid substitution using data collected at FWU. These analyses will be framed in realistic scenarios presented by the FWU CO2 WAG development. The resulting fluid/rock physics models will be applied to output from the calibrated FWU compositional reservoir simulation model to forward model the time-lapse seismic response. Modeled results are compared with field time-lapse seismic measurements and strategies for numerical model feedback/update are discussed. While mechanical effects are neglected in the work presented here, complementary parallel studies are underway in which laboratory measurements are introduced to introduce stress dependence of matrix elastic moduli.

4D↗
DOE OSTI2020

Investigation into Reductive Separation of Tc-99 from Low Activity Waste Off-Gas Condensate by Zero Valent Iron Materials - 20311

This study is investigating the potential for reductive separation of technetium-99 (Tc) from off-gas condensate generated from vitrification of legacy tank waste at the Hanford Tank Waste Treatment and Immobilization Plant. Management of Tc is a high-priority activity for the U.S. Department of Energy (DOE) Office of Environmental Management (EM) complex, which must be addressed based on knowledge gaps and technology development needs outlined in the DOE-EM Technetium Management Program Plan [1]. Technetium is one of the main risk drivers in the permanent disposal of low activity waste (LAW) by vitrification at the Hanford Site due to its high volatility and only fractional incorporation into borosilicate glass. Volatilized Tc will be captured by an off-gas treatment system, and the current plan is to continuously recycle generated condensate back to the LAW glass melter feed. Off-gas recycling is effective at increasing Tc loading in the LAW glass, but it also disproportionately increases concentrations of sulfate, halides, and other volatile compounds impeding overall LAW processing leading to significantly greater waste glass volume requirements. Removal of Tc from secondary liquid waste offers an alternative disposal route to continuous off-gas recycling which may result in significant reduction of the volumes of LAW waste glass and substantial cost savings. The objective of this research funded by the DOE-EM Minority Serving Institutions Partnership Program (MSIPP) is to determine the suitability of zero valent iron (ZVI) as a separation technology to remove Tc from the off-gas condensate stream to minimize its recycle. While ZVI has been proposed for various remediation applications with Tc, it has not been studied for its suitability for removing Tc from the LAW off-gas condensate. The goal is to identify the optimal conditions for which Tc{sup (VII)} will be reduced to Tc{sup (IV)} species and incorporated into an iron matrix. This paper presents a programmatic overview of our evaluation of commercial ZVI materials for their efficiency towards Tc{sup (VII)} reductive removal from simplified off-gas simulant solutions under aerobic conditions. Tc concentrations in solutions were monitored for up to 30 days to determine efficiency and kinetics of reductive removal of Tc. The best performing candidate materials were selected for further experiments evaluating the effects of (1) variable ionic strength, (2) the presence of chromium and corrosive anions, and (3) ZVI aging on Tc{sup (VII)} reductive removal in batch experiments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗