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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Analyzing machine learning models to accelerate generation of fundamental materials insights

Abstract Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Regulatory Design for Transport Security Regulations of Nuclear and Other Radioactive Material

A cornerstone within a nuclear security regime of a country is a legislative and regulatory framework that both regulates the individuals using radioactive and nuclear materials, but also regulates the regulators for their commitments under International Law. When drafting nuclear law and regulations, both technical and legal expertise is needed to ensure the regulation meets the technical requirements for security, but also is legally enforceable within a country’s jurisdiction. While the Primary Nuclear Law, or Enabling Act provides establishes the Competent Authority and responsibilities for nuclear security, regulatory development is where the proverbial, rubber meets the road for requirements. The regulations provide instruction for inspections, authorizations, and managed activities, such as use storage, and transport. Transport Security regulatory development for radioactive and nuclear material is complex because the regulation maybe multi-jurisdictional, multi-modal, and involve multiple stakeholders. Development of transport security regulations requires coordination with other ministerial stakeholders, transport operators, and alignment to international treaties and conventions outside the nuclear domain. This paper provides a brief discussion of the challenges associated with regulatory development for transport security regulations for nuclear and other radioactive materials in transport. The paper will briefly identify key stakeholders, international law involved, and current efforts by the International Atomic Energy Agency (IAEA) in supporting Member States in regulatory development. While active in that regulations are necessary for a robust nuclear security regime, regulatory development is more a slow-burn than an active activity in nuclear security.

Fialkoff, Marc↗
DOE OSTI2020

Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes

Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.

42 ENGINEERING↗
DOE OSTI2020

Theoretical insight into thermodynamics of particle-based crystallization

Crystallization process often does not follow the monomer-by-monomer addition pathway, as described by classical nucleation theory. Instead, initial step may involve the formation of larger building blocks, such as crystalline or amorphous nanoparticles, droplets, clusters, complexes or oligomers, which subsequently undergo assembly into a crystal. These multistep crystallization processes are ubiquitous in mineral formation and biomineralization. In materials synthesis utilizing non-classical crystallization pathways is particularly attractive as the restriction on which crystalline faces can grow is lifted in a multistep crystallization process adding to the versatility of materials architectures that can be obtained. Predicting the outcomes of non-classical crystallization through the understanding of the control parameters influencing crystallization pathways is an important and challenging task essential for utilizing the full potential of non-classical crystallization in the design of functional materials. Here, the current status of the development of a predictive theoretical framework for modeling non-classical crystallization is reviewed.

Sushko, Maria L.↗
DOE OSTI2016

A three dimensional field formulation, and isogeometric solutions to point and line defects using Toupin's theory of gradient elasticity at finite strains

In this study, we present a field formulation for defects that draws from the classical representation of the cores as force dipoles. We write these dipoles as singular distributions. Exploiting the key insight that the variational setting is the only appropriate one for the theory of distributions, we arrive at universally applicable weak forms for defects in nonlinear elasticity. Remarkably, the standard, Galerkin finite element method yields numerical solutions for the elastic fields of defects that, when parameterized suitably, match very well with classical, linearized elasticity solutions. The true potential of our approach, however, lies in its easy extension to generate solutions to elastic fields of defects in the regime of nonlinear elasticity, and even more notably for Toupin's theory of gradient elasticity at finite strains (Toupin Arch. Ration. Mech. Anal., 11 (1962) 385). In computing these solutions we adopt recent numerical work on an isogeometric analytic framework that enabled the first three-dimensional solutions to general boundary value problems of Toupin's theory (Rudraraju et al. Comput. Methods Appl. Mech. Eng., 278 (2014) 705). We first present exhaustive solutions to point defects, edge and screw dislocations, and a study on the energetics of interacting dislocations. Then, to demonstrate the generality and potential of our treatment, we apply it to other complex dislocation configurations, including loops and low-angle grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A review of interdependence of sustainable building

ABSTRACT: Despite the worldwide promotion of the sustainable building (SB) approach, its associated interdependence has seldom been explored. This knowledge gap is significant given the paradigm shift of regarding SBs as complex socio-technical systems embedded with multifaceted interdependence. The aim of this paper is to examine the interdependence of SB through a literature review. The literature review was guided by a framework comprising three dimensions of SB systems, i.e., building performance, methodology and stakeholders, on their theoretical grounds ranged from reductionism to holism. In order to articulate the integration of the three dimensions, this paper examined zero carbon building as a specific case of SB. The findings contribute an innovative approach to examining the interdependence of SB, and should guide the development of strategies for managing the trade-offs in delivering SBs. - Highlights: • Performance scope of sustainable building triggers interdependence. • Material flow from cradle to cradle causes interdependence. • Interdependence occurs between the building and their context. • Interdependence exists in stakeholders' participation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Experimental and DFT Calculated IR Spectra of Guests in Zeolites: Acyclic Olefins and Host-Guest Interactions

We performed experimental and periodic density functional theory (DFT) IR spectroscopy to investigate the adsorption of acyclic olefins over both acidic and non-acidic zeolites. Two conjugated polyenes, 2,4-dimethyl-1,3-pentadiene (I) and 2,6-dimethyl-2,4,6-octatriene (II) were studied to probe organic intermediates that can form during methanol conversion, and that can lead to deactivating species known collectively as “coke.” We computed vibrational spectra using zeolite-adsorbed and gas-phase models for both neutral and protonated forms of I and II, and compared these DFT results to diffuse reflectance IR Fourier transform (DRIFT) spectra of zeolite-guest systems. Our experimental and computational results are precise enough to pinpoint that the gauche s-cis conformation of species I predominates during adsorption over de-aluminated zeolite beta. Computed zeolite-adsorbed spectra of the protonated species I and II best represent the DRIFT spectra obtained after the adsorption of the olefins on HMOR at 20 ?, with computed bands at 1543 and 1562 cm–1 for molecules I+ and II+, respectively, attributed to the allylic stretching mode, ?(C=C–C+). These computed band frequencies are within 6 cm–1 of experimental data and confirm that the interaction between neutral acyclic olefins and acidic zeolites leads to protonation of the olefin. Comparison of computed spectra of the protonated species in the gas phase to those in the zeolite indicates that the electrostatic interaction between alkenyl and alkadienyl cations and negative zeolite framework does not significantly impact the position of the allylic stretching bands. These results highlight that computed spectroscopy and thermodynamics coupled with experimental spectra can be used for elucidating complex mixtures in zeolites, and certain spectral features of adsorbed olefins can be accurately modeled by gas-phase calculations. ACKNOWLEDGMENTS BM and SMA were supported by the National Science Foundation under Award # CBET-1512442. Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for support (or partial support) of this research (#589978-ND5, EDH and FCJ). MDB was supported by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES), Division of Materials Science and Engineering. CJM was supported by the DOE, Office of Science, BES, Division of Chemical Sciences, Geosciences, and Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated by Battelle for the U.S. DOE. Computing resources were generously provided through PNNL’s institutional computing (PIC).

Manookian, Babgen↗
DOE OSTI2024

Simulating charged defects at database scale

Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is, therefore, crucial for understanding the growth and operation of materials, especially for optoelectronics applications. In this work, we present a general-purpose Python framework for the analysis of point defects in crystalline materials as well as a generalized workflow for their treatment with high-throughput simulations. The distinguishing feature of our approach is an emphasis on a unique, unit cell, structure-only, definition of point defects which decouples the defect definition, and the specific supercell representation used to simulate the defect. This allows the results of first-principles calculations to be aggregated into a database without extensive provenance information and is a crucial step in building a persistent database of point defects that can grow over time, a key component toward realizing the idea of a “defect genome” that can yield more complex relationships governing the behavior of defects in materials. We demonstrate several examples of the approach for three technologically relevant materials and highlight current pitfalls that must be considered when employing these methodologies as well as their potential solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Adaptive simulations enable computational design of electron beam processing of nanomaterials with supersonic micro-jet precursor

Focused Electron Beam Induced Processing (FEBIP) is a powerful tool for the “direct-write” of nanomaterials with the possibility of atomistic control on suspended 2D material substrates. FEBIP capabilities have been significantly expanded by using a localized jet-based delivery of precursors, and especially when a thermally energized supersonic micro-jet enhances the delivery of mass flux along with controlling the far-from-equilibrium thermodynamic state of adsorbed adatoms. The possibilities of growing nanomaterials with “dialed-in” composition with ultra-high growth rates and aspect ratios up to 100:1 have been demonstrated using the supersonic micro-jet-FEBIP. Bringing this scientific discovery to the level of maturity required for practical applications in additive nanomanufacturing requires simulation tools that are capable of capturing the complex flow physics of micro-jet-substrate interactions that bridge a wide range of flow regimes from the high-density gas micro-jet expanding into the vacuum environment of FEBIP. To address this significant computational challenge, a new approach has been developed and described in this work for a multiscale adaptive DSMC (Direct Simulation Monte Carlo) algorithm to predict the micro-jet gas dynamics in an FEBIP environment, spanning the full range of flow regimes from low Knudsen (Kn) number O(0.01) continuum flow to high Kn of O(10) for the molecular flow within a unified computational framework. Here, the fundamental principles of the adaptive DSMC algorithm are described, its viability as a DSMC technique is demonstrated, and computational improvements for the benchmark cases are discussed in the context of advancing 3D nanofabrication with FEBIP. Ultimately, combining the first principle simulations via adaptive DSMC with the complementary experimental data will enable the creation of the powerful CAD tools for in silico design and optimal operation of micro-jet-FEBIP.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Revealing the unusual rate-dependent mechanical behaviors of nematic liquid crystal elastomers

Liquid crystal elastomers (LCEs) exhibit unique mechanical properties of soft elasticity and enhanced energy dissipation with rate dependency. They are potentially transformative materials for applications in mechanical impact mitigation and vibration isolation. However, previous studies have primarily focused on the mechanics of LCEs under equilibrium and quasistatic loading conditions. Critical knowledge gaps exist in understanding their rate-dependent behaviors, which are a complex mixture of traditional network viscoelasticity and the soft elastic behaviors with changes in the mesogen orientation and order parameter. Together, these inelastic mechanisms lead to unusual rate-dependent energy absorption responses of LCEs. In this work, we developed a viscoelastic constitutive theory for monodomain nematic LCEs to investigate how multiple underlying sources of inelasticity manifest in the rate-dependent and dissipative behaviors of monodomain LCEs. Here, the theoretical modeling framework combines the neo-classical network theory with evolution rules for the mesogen orientation and order parameter with conventional viscoelasticity. The model is calibrated with uniaxial tension and compression data spanning six decades of strain rates. The established 3D constitutive model enables general loading predictions taking the initial mesogen orientation and order parameter as inputs. Additionally, parametric studies were performed to further understand the rate dependence of monodomain LCEs in relation to their energy absorption characteristics. Based on the parametric studies, particularly loading scenarios are identified as conditions where LCEs outperform conventional elastomers regarding energy absorption.

42 ENGINEERING↗
DOE OSTI2025

Theory of terahertz pulse transmission through ferroelectric nanomembranes

An analytical model is developed to predict the temporal evolution of the lattice polarization in ferroelectric nanomembranes upon the excitation by a terahertz (THz) electromagnetic pulse of an arbitrary waveform and the concurrent transmission of the THz pulse in both linear and nonlinear regimes. It involves the use of the perturbation method to solve the equation of motion for the lattice polarization in both unclamped and strained ferroelectric nanomembranes within the framework of Landau-Ginzburg-Devonshire theory. The model is applicable to perovskite oxides such as BaTiO 3 and SrTiO 3 , wurtzite Al 1−𝑥 ⁢Sc 𝑥 ⁢N, and trigonal LiNbO 3 . Our analytical model provides a theoretical basis for determining the thermodynamic and kinetic parameters of ferroelectric materials through a THz transmission experiment. The calculation results also suggest an approach to reversing the chirality of a circularly polarized THz pulse by harnessing the resonant polarization-photon coupling in ferroelectrics. This capability of chirality reversal, along with the high tunability from a strain applied along any arbitrarily oriented in-plane axis, provides new opportunities for THz wave modulation without relying on complex metasurface designs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications

Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Prediction of residual stresses in additively manufactured parts using lumped capacitance and classical lamination theory

Several industries are interested in Laser Powder Bed Fusion (L-PBF) Additively Manufactured (AM) metal parts because their designs can be made arbitrarily complex while retaining bulk-type material properties. However, the residual stresses (RS) and distortions caused by the heat gradients inherent to L-PBF processes are detrimental to the structural integrity of the parts and must be taken into consideration during the part design cycle. Predicting the state of stresses in as-built 3D printed parts is a difficult problem that is typically approached with the use of transient thermomechanical Finite Element Models (FEMs). However, the nonlinearities associated with AM processes are difficult to capture in these FEMs without increasing the computational cost of the simulation, limiting their ability to be incorporated into practical design cycles. This work presents a novel analytical framework that combines lumped capacitance nonlinear heat transfer with time dependent classical lamination theory to efficiently and accurately predict RS in as-built L-PBF parts without the need of FEMs. The simulation was compared to Neutron Diffraction (ND) residual strain measurements taken at Oak Ridge National Laboratories (ORNL) as well as Synchrotron X-ray Diffraction (XRD) strain data published by the National Institute of Standards and Technology (NIST). The simulation predictions and the experimental data showed excellent agreement for the in-plane strain directions, and general agreement for the out of plane strain component, highlighting an area where further development can be implemented.

42 ENGINEERING↗
DOE OSTI2018

Stability of M 3 S 3 complexes on fcc M(111) surfaces: M = Au, Ag, Cu, and Ni

Density Functional Theory is utilized to assess the stability of metal (M)-sulfur (S) complexes adsorbed on fcc M(111) surfaces, specifically considering S-decorated planar M trimers, M 3 S 3 . Scanning Tunneling Microscopy studies have identified structures proposed to be Ni 3 S 3 on Ni(111), and Au 3 S 3 on Au(111). In addition, Cu 3 S 3 on Cu(111) has been suggested to facilitate enhanced Cu surface mass transport. Our analysis considers M 3 S 3 complexes for M = Au, Ag, Cu, and Ni, assessing key measures of stability on surfaces, and also comparing behavior with trends in gas-phase stability. These surface and gas-phase analyses are systematically related within the framework of Hess’s law, which allows elucidation of various contributions to the overall energetics. In all cases, the adsorbed complex is stable relative to its separated constituents adsorbed on the terrace. However, only for Ag does one find a negative energy of formation from excess S on terraces and M extracted from kink sites along step edges, implying spontaneous complex formation for this pathway. We interpret various experimental observations in the context of our results for energetics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design and Application of Materials for Sequestration and Immobilization of 99 Tc

99 Technetium ( 99 Tc) is a hazardous radionuclide generated by the nuclear industry that poses a serious environmental threat. The wide variation and complex chemistries of nuclear waste streams containing 99 Tc often create unique, site specific challenges when sequestering and immobilizing the waste in a matrix suitable for long-term storage and disposal. Therefore, an effective management plan for 99 Tc containing radioactive waste will likely require a variety of suitable materials/matrices capable of adapting to and addressing these challenges. In this review, we discuss and highlight the key developments for effective removal and immobilization of 99 Tc in inorganic waste forms. Specifically, we review the synthesis, characterization, and application of materials for targeted removal of 99 Tc from (simulated) waste solutions under various experimental conditions. These materials include: i) layered double hydroxides (LDHs), ii) metal-organic frameworks (MOFs), iii) ion-exchange resins (IERs) as well as cationic organic polymers (COPs), and iv) surface modified natural clay materials (SMCMs). Secondly, we discuss some of the major and recent developments towards 99 Tc immobilization in i) glass, ii) cement, and iii) iron mineral waste forms. Finally, we present future challenges that need to be addressed for the design, synthesis, and selection of suitable matrices for the efficient sequestration and immobilization of 99 Tc from targeted wastes. Finally, the purpose of this review is to inspire the researches on the design and application of various suitable materials/matrices for selective removal of 99 Tc present globally in different radioactive wastes and its immobilization in stable/durable waste forms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mild-Temperature Supercritical Water Confined in Hydrophobic Metal–Organic Frameworks

Fluids under extreme confinement show characteristics significantly different from those of their bulk counterpart. This work focuses on water confined within the complex cavities of highly hydrophobic metal–organic frameworks (MOFs) at high pressures. A combination of high-pressure intrusion–extrusion experiments with molecular dynamic simulations and synchrotron data reveals that supercritical transition for MOF-confined water takes place at a much lower temperature than in bulk water, ~250 K below the reference values. This large shifting of the critical temperature (T c ) is attributed to the very large density of confined water vapor in the peculiar geometry and chemistry of the cavities of Cu 2 tebpz (tebpz = 3,3',5,5'-tetraethyl-4,4'-bipyrazolate) hydrophobic MOF. This is the first time the shift of Tc is investigated for water confined within highly hydrophobic nanoporous materials, which explains why such a large reduction of the critical temperature was never reported before, neither experimentally nor computationally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mixing ligands to enhance gas uptake in polyMOFs

Polymer-metal organic frameworks (polyMOFs) offer a pathway toward processable polymer–MOF hybrid materials; however, because polymeric ligands are incorporated throughout the MOF lattice, polyMOFs have an inherent shortcoming of reduced pore accessability and surface area compared to traditional MOFs. Herein, a strategy for altering the degree of polymer incorporation in polyMOFs by mixing a multivalent polymer ligand containing MOF-forming linkers with “free” linkers is investigated as a means to tune the properties of polyMOFs, resulting in polyMOFs with superior N 2 and CO 2 uptake. The mixed ligand approach is further extended to distinct MOF-forming polymer ligands to create multivariate (MTV)-polyMOFs, which provides a method for incorporating low-dispersity polymer ligands with complex architectures into polyMOF lattices without the addition of small molecule components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗