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At least 613 records · Page 34

Trends in Human Spaceflight: Failure Tolerance, High Reliability and Correlated Failure History

In a half century of human spaceflight, NASA has continuously refined agency safety and reliability requirements in response to mission demands, critical failures, and technology development. Early spacecraft, including Mercury, Gemini and Apollo vehicles, were highly reliant on dissimilar redundancy and demonstrated test margins. Later programs, such as the reusable Space Transportation System (STS) and International Space Station (ISS), introduced probabilistic studies and isolated two-failure tolerance to improve robustness at the expense of added complexity. More recently, the Orion Multi-Program Crew Vehicle (MPCV) program adopted universal single-failure tolerance with two categorical exceptions; Zero-Failure Tolerant (0FT) and Design for Minimum Risk (DFMR) hardware. Failure tolerance variances are defined and managed in accordance with agency human-rating requirements, and require concurrence from program Technical Authorities (TA) as well as the MPCV Safety and Mission Assurance Safety and Engineering Review Panel (MSERP). To understand and reaffirm standards applied to Apollo, Space Shuttle and Orion vehicles, Orion and Deep Space Gateway Safety and Mission Assurance (S&MA) representatives conducted accelerated research to compare unique safety and reliability criteria against ground and flight anomalies, based on information contained in post-mission reports and the Problem Reporting and Corrective Action (PRACA) database. In some cases, high-profile failures and narrow escapes have reinforced decisions to maintain or adapt safety requirements. In others, empirical trends have highlighted the need for vigilance and innovative safety guidelines. Given the inability to achieve absolute compliance with evolving safety and reliability requirements, the team conducted a targeted review of DFMR and 0FT propulsion elements within the framework of changing system design, inspection, materials and process developments to formulate conclusions on technological maturity, failure density, and net changes in safety risk. Based on the aggregate performance of high-reliability and failure-tolerant systems, the authors have attempted to establish best practices and guidelines to inform future program decisions. On a somewhat cautionary note, this study is not intended to direct a universal set of requirements for future missions based on prior lessons learned. Spacecraft safety is a multi-variable problem, and attempts to mitigate past failures will not guarantee future success. However, this assessment offers a retrospective review of policy changes, implementation and effectiveness. In the future, NASA, European Space Agency (ESA) and industry partners may benefit from a more robust correlation between requirements and performance, as space-faring nations work toward more challenging, complex and long-duration commercial and deep-space ventures.

Green, Carrie↗

A pore-expanded supramolecular organic framework and its enrichment of photosensitizers and catalysts for visible-light-induced hydrogen production

A pore-expanded three-dimensional supramolecular organic framework SOF-bpb, with a previously unattained aperture size of 3.6 nm, has been constructed in water from the co-assembly of cucurbit[8]uril (CB[8]) and a tetraphenylmethane-cored 1,4-bis(pyridin-4-yl)-benzene-appended building block M1. The periodicity of SOF-bpb in water and in the solid state has been confirmed using synchrotron X-ray scattering and diffraction experiments. SOF-bpb can adsorb anionic and neutral Ru 2+ complex photosensitizers and anionic Wells–Dawson-type and Keggin-type polyoxometalates (POMs). The adsorption leads to an important enrichment effect, which remarkably increases the catalytic efficiency of the Ru 2+ complex–POM systems for visible light-induced reduction of protons to produce H 2 . Here, the expanded aperture of SOF-bpb also facilitates light absorption of the adsorbed Ru 2+ complex photosensitizers and electron transfer between excited complexes and the POM catalysts, leading to enhanced photocatalytic activities as compared with the prototypical SOF that has an aperture size of 2.1 nm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗

The structures of ordered defects in thiocyanate analogues of Prussian Blue

We report the structures of six new divalent transition metal hexathiocyanatobismuthate frameworks with the generic formula $\Big\{ M[Bi(SCN)_6]_\frac{2+x}{3} \Big\}$ $\{guest\}^{x+}$, M = Mn, Co, Ni and Zn. These frameworks are defective analogues of the perovskite-derived trivalent transition metal hexathiocyanatobismuthates M III [Bi(SCN) 6 ]. The defects in these new thiocyanate frameworks order and produce complex superstructures due to the low symmetry of the parent structure, in contrast to the related and more well-studied cyanide Prussian Blue analogues. Despite the close similarities in the chemistries of these four transition metal cations, we find that each framework contains a different mechanism for accommodating the lowered transition metal charge, making use of some combination of Bi(SCN) 6 3- vacancies, M Bi antisite defects, water substitution for thiocyanate, adventitious extra-framework cations and reduced metal coordination number. These materials provide an unusually clear view of defects in molecular framework materials and their variety suggests that similar richness may be waiting to be uncovered in other hybrid perovskite frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Comparative Analysis via CFD Simulation on the Impact of Graphite Anode Morphologies on the Discharge of a Lithium-Ion Battery

The morphology of electrode materials plays a crucial role in determining the performance of lithium-ion batteries. Traditional computational models often simplify graphite flakes as uniformly sized spheres, which limits their predictive accuracy. In this study, we present a computational workflow that overcomes these limitations by incorporating a more realistic representation of graphite morphologies. This workflow is designed to be flexible and reproducible, enabling efficient evaluation of electrochemical performance across diverse material structures. By exploring different graphite morphologies, our approach accelerates the optimization of material preparation techniques and processing conditions. Our findings reveal that incorporating greater morphological complexity leads to significant deviations from classical model predictions. Instead, our refined model offers a more accurate representation of battery discharge behavior, closely aligning with experimental data. This improvement underscores the importance of detailed morphological descriptions in advancing battery design and performance assessments. To promote accessibility and reproducibility, we provide the developed code for seamless integration with the COMSOL API, allowing researchers to implement and adapt it easily. This computational framework serves as a valuable tool for investigating the impact of graphite morphology on battery performance, bridging the gap between theoretical modeling and experimental validation to enhance lithium-ion battery technology.

25 ENERGY STORAGE↗

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗

Multi-physics modeling of grain growth during solidification in electron beam additive manufacturing of Inconel 718

While experimental work has shown promising results regarding control of additive manufacturing metal grain structure, the effects of processing parameters on the grain structure is difficult to understand and predict from experiment alone. To this end, a modeling framework is developed which sequentially couples a macro-scale, semi-analytic thermal model, and a meso-scale, cellular automata-based microstructure model. This framework is applied to electron beam additive manufacturing of Inconel 718 using a complex spot scan pattern. The model shows that, with the same scan pattern, variations in the spot time and electron-beam current produce thermal histories with significant spatial and temporal differences, which then produce complex solidification conditions from the interplay between molten pools in the same layer and subsequent layers, resulting in vastly different grain structures. Additionally, it is noted that the framework can significantly reduce the computational expenses for coupled thermal-metallurgical problems, and has the potential to be used for component level problems.

36 MATERIALS SCIENCE↗

The superior hydrothermal stability of Pd/SSZ-39 in low temperature passive NOx adsorption (PNA) and methane combustion

We successfully synthesized uniform SSZ-39 with an average crystal size of about a micron. Pd (0.7 - 3 wt%) was supported on SSZ-39 with Si/Al ratio ~12. The as-synthesized materials were characterized by FTIR, XRD, Helium Ion Microscopy, HAADF-STEM imaging, 27 Al, 29 Si and H solid state NMR spectroscopic techniques. FTIR studies with CO and NO probe molecules reveal that the 0.7 wt% Pd/SSZ-39 material with Si/Al ~12 has the majority of Pd dispersed atomically as isolated Pd(II) and Pd(II)-OH centers, and thus can be used as a low-temperature passive NOx adsorber. Pd(II)-NO, Pd(II)(OH)(NO) and Pd(II)(CO)(NO) complexes form during PNA in this material. We compare this PNA material directly with the Pd/SSZ-13 system (with Si/Al ratio ~12) and show its superior hydrothermal stability. Remarkably, Pd/SSZ-39 with Si/Al ratio ~12 survives hydrothermal aging up to 815 ºC in 10% H 2 O/Air vapor for 16 hours without significant loss in activity. The SSZ-39 crystal structure remains intact during hydrothermal aging up to 1,000 ºC as we elucidate it with XRD and HAADF-STEM imaging/EDS mapping. However, changes to the framework during such harsh hydrothermal treatment significantly change the NOx release profiles during PNA as evidenced by high-field 27 Al NMR on fresh and aged Pd/SSZ-39 samples as well as PNA performance measurements. Besides PNA application, these hydrothermally very stable materials (3 wt% Pd on SSZ-39 with Si/Al ratio ~12) can be used as a robust methane combustion catalyst under industrially relevant conditions (GHSV~600,000hr -1 ). This catalyst shows minimal deactivation after both harsh hydrothermal aging at 750 and 800 ºC, and prolonged time on stream (105 hrs) at 425 °C. In contrast, both 3wt% Pd/alumina and 3wt% SSZ-13 supported samples lose a significant portion of their activity. Our study opens new avenues to prepare the most hydrothermally stable known Pd/zeolite materials with applications for adsorption and catalytic hydrocarbon combustion under industrially relevant conditions.

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Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.

Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗

Novel additive manufacturing for plasma facing materials ‐ creating a research pathway for minority students

This project addresses two critical and intertwined challenges in fusion energy, namely the shortage of a broadly trained scientific workforce and the lack of scalable manufacturing solutions for plasma-facing components (PFCs). Through a collaboration among Florida International University (FIU), Miami Dade College (MDC), and Purdue University, the project established structured, reproducible educational and research pathways that recruit and advance students from institutions historically outside the fusion energy enterprise, building the human capital that this field urgently needs. The project integrates the complementary research strengths of FIU and Purdue to investigate flash sintering as a transformative processing route for tungsten-based PFCs. Unlike conventional sintering approaches, flash sintering offers rapid densification at significantly reduced thermal budgets, making it a compelling candidate for fabricating complex tungsten geometries that must withstand extreme plasma-facing environments. Systematic experimental and modeling efforts will elucidate the fundamental mechanisms governing microstructure evolution, grain boundary chemistry, and thermomechanical response during flash sintering — knowledge that is presently lacking but essential for translating this technology into reliable manufacturing practice. The convergence of workforce development and cutting-edge manufacturing research positions this project to deliver measurable, durable impact: a pipeline of fusion-ready researchers cultivated through expanded institutional partnerships, and a validated materials processing framework that accelerates domestic readiness for next-generation fusion reactor construction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Development of Multi-Physics Dynamics Models for High-Frequency Large-Amplitude Structural Response Simulation

An analytic approach is demonstrated to reveal potential pyroshock-driven dynamic effects causing power losses in the Thermo-Electric (TE) module bars of the Mars Science Laboratory (MSL) Multi-Mission Radioisotope Thermoelectric Generator (MMRTG). This study utilizes high-fidelity finite element analysis with SIERRA/PRESTO codes to estimate wave propagation effects due to large-amplitude suddenly-applied pyro shock loads in the MMRTG. A high fidelity model of the TE module bar was created with approximately 30 million degrees-of-freedom (DOF). First, a quasi-static preload was applied on top of the TE module bar, then transient tri-axial acceleration inputs were simultaneously applied on the preloaded module. The applied input acceleration signals were measured during MMRTG shock qualification tests performed at the Jet Propulsion Laboratory. An explicit finite element solver in the SIERRA/PRESTO computational environment, along with a 3000 processor parallel super -computing framework at NASA AMES, was used for the simulation. The simulation results were investigated both qualitatively and quantitatively. The predicted shock wave propagation results provide detailed structural responses throughout the TE module bar, and key insights into the dynamic response (i.e., loads, displacements, accelerations) of critical internal spring/piston compression systems, TE materials, and internal component interfaces in the MMRTG TE module bar. They also provide confidence on the viability of this high-fidelity modeling scheme to accurately predict shock wave propagation patterns within complex structures. This analytic approach is envisioned for modeling shock sensitive hardware susceptible to intense shock environments positioned near shock separation devices in modern space vehicles and systems.

Shock Waves↗

Applying Technology and Experience to Ensure Safe In Situ Decommissioning - 19587

Permanent entombment of a radioactively contaminated facility as a decommissioning option (In Situ Decommissioning, ISD) has been completed for DOE facilities at the Savannah River Site (P and Reactors, HWCTR reactor), Hanford (U Canyon) and Idaho (CP-601/604),and is currently being considered for implementation at a limited number of selected DOE facilities as well as for the NPD and WR-1 reactors in Canada and various facilities in the United Kingdom. A general description of an ISD project encompasses an entombed facility which in some cases is limited to just the below-grade portion of a facility with the above-ground portion being decommissioned, packaged and disposed in a conventional manner. The entombed portions of the facility are of robust construction, generally of reinforced concrete exterior, that provides a migration barrier between internal contamination and the environment with significant internal void spaces backfilled or grouted. The scope of entombment may include ancillary equipment and structures and may contain radioactive and hazardous materials and contamination within the facility and waste imported from outside the facility. ISD is most often a permanent decommissioning end-state although in some instances (e.g. the U.K.) there may be a need to revisit the ISD facility after 300 years to effect traditional decommissioning. The defined completion (the end-state) of the decommissioned facility is therefore project-specific and in conformance with environmental approval processes and planned future activities. In all cases however, the final condition is passive, meaning that there are no requirements for ongoing operational systems or equipment within the decommissioned facility. ISD projects in the USA are presumed to be under indefinite institutional control of the U.S. Government. The regulatory framework in the U.S. is currently in place to provide assurance that the risk posed by an ISD facility is within regulatory acceptance criteria. (The framework in the U.K. and Canada is evolving towards that in the USA). Special emphasis is placed on the fact that an entombed facility is not considered a waste disposal facility; rather it is a decommissioning end-state option. ISD does not eliminate the need for effective and careful management of contaminated materials and structures, nor does it serve to abandon contaminated buildings in place. Further, ISD is safe and cost-effective for a relatively small number of facilities across the DOE Complex. This paper explores the current programmatic considerations and technical aspects of the ISD process and discuss the measures taken to ensure that an 'entombed' facility is safe for both site workers and the general population. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Strongly Anharmonic Phonons and Their Role in Superionic Diffusion and Ultralow Thermal Conductivity of Cu 7 PSe 6

We report the quest for advanced superionic materials requires understanding their complex atomic dynamics, but detailed studies of the interplay between lattice vibrations and ionic diffusion remain scarce. Here inelastic and quasielastic neutron scattering measurements in the superionic argyrodite Cu 7 PSe 6 are reported, combined with molecular dynamics (MD) based on ab initio and machine-learned potentials, providing critical insights into the atomistic mechanisms underlying fast ion conduction. The results reveal how long-range Cu diffusion is limited by intercluster hopping, controlled by selective anharmonic phonons of the crystalline framework. Further, the Green–Kubo simulations reproduce the ultralow lattice thermal conductivity and identify contributions from mobile ions, phonons, and their cross-correlations. The mode resolved analysis shows that the thermal conductivity is dominated by low-energy acoustic phonon modes of the overall crystal framework. The analysis of mode-resolved spectral functions further show that vibrational modes with significant Cu contributions are strongly damped, corresponding to the breakdown of associated phonon quasiparticles. These results highlight the importance of strongly anharmonic effects in superionic systems, in which the traditional quasiharmonic phonon picture is insufficient, and pave the way toward combining machine-learning accelerated simulations with neutron scattering experiments to rationalize the complex atomic dynamics underlying ionic and thermal transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Tale of Two Trimers from Two Different Worlds: A COF‐Inspired Synthetic Strategy for Pore‐Space Partitioning of MOFs

Abstract The introduction of a symmetry‐ and size‐matching pore‐partitioning agent in the form of either a molecular ligand, such as 2,4,6‐tri(4‐pyridinyl)‐1,3,5‐triazine ( tpt ), or a metal‐complex cluster, into the hexagonal channels of MIL‐88/MOF‐235‐type (the acs net) to create pacs ‐type (partitioned acs ) crystalline porous materials is an effective strategy to develop high‐performance gas adsorbents. We have developed an integrated COF–MOF coassembly strategy as a new method for pore‐space partitioning through the coassembly of [(M 3 (OH) 1− x (O) x (COO) 6 ] MOF‐type and [B 3 O 3 (py) 3 ] COF‐type trimers. With this strategy, the coordination‐driven assembly of the acs framework occurred concurrently and synergistically with the COF‐1‐type condensation of pyridine‐4‐boronic acid into a C 3 ‐symmetric trimeric boroxine molecule. The resulting boroxine‐based pacs materials exhibited dramatically enhanced gas‐sorption properties as compared to nonpartitioned acs ‐type materials and are among the most efficient NH 3 ‐sorption materials.

Wang, Yanxiang↗

Structural mapping and tuning of mixed halide ions in amorphous sulfides for fast Li-ion conduction and high deformability

Amorphous sulfides are among the most promising candidates for solid electrolytes (SEs) owing to their excellent deformability and acceptable Li-ion conductivity (σion) at room temperature. However, the complex atomic structure of these amorphous materials without long-range ordering results in a lack of structural understanding and difficulties in tuning material properties. Here, in this study, we performed structural mapping of glassy sulfide SEs composed of various anion clusters using a combinatorial atomic level analysis of synchrotron X-ray-based pair distribution function (PDF) and reverse Monte-Carlo (RMC) methods, demonstrating the potential to tune σ ion in glass SEs. First, we prepared a new glassy sulfide with a mixed anion framework of two halogens (Br and I) as atomic anions, with PS 4 3- molecular anions in a Li 2 S–P 2 S 5 -based glass SE. At a specific Br content, [(Li 2 S) 0.658 (LiI 0.9 LiBr 0.1 ) 0.342 ] 0.825 [P 2 S 5 ] 0.175 recorded a σ ion of 2.27 mS cm -1 , the highest value for any glass sulfides reported to date; however, its elastic modulus was still suppressed to 14.48 GPa. PDF and RMC calculations successfully provided structural mapping of anion clusters, including two halogens. Molecular dynamics simulations of each composition confirmed that flexible coordination caused by the rattling of small polarizable Br ions in the mixed halogens of glassy SEs contributed to the superior σ ion . Our results may provide new insights into the design of superior glassy SEs that play key roles in all-solid-state batteries requiring fast Li-ion conduction and high deformability.

36 MATERIALS SCIENCE↗

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

Sustained advances in the mathematics of modeling and simulation have resulted in the capability today for routine simulation of a number of large scale complex DOE-relevant systems. As remarkable as this capability for solving the so-called forward problem is, it is typically only the first step-an inner loop within an outer loop that explores the simulation model's parameter space and decision space to characterize uncertainty in the model's predictions, learn unknown model parameters from data, design the most informative experiments, determine optimal control strategies, and create optimal designs. Broadly, what unifies all of these outer loop problems is that they are, in one form or another, optimization problems over parameter/control/design space that are constrained by complex uncertain models. To fully realize the power of scientific simulation as a basis for scientific discovery, technological innovation, and rational decision-making, it is imperative to move beyond simulation to tackle the outer loop of optimization for learning from data, experimental design, and control with complex uncertain models. When the models under consideration are large-scale and complex, and when the optimization variable and uncertain parameter spaces are high (or infinite) dimensional, this constitutes a grand challenge of the highest order, and is intractable with conventional methods. To overcome these challenges, the AEOLUS Center was established to develop a unified mathematical, computational, and statistical framework for (1) Learning predictive models from complex data via Bayesian inference and optimization, and (2) Optimizing experiments, processes, and designs using the resulting uncertain models. These problems are intractable with conventional methods, for several reasons: (1) The simulation problems that govern the inner loops of the optimization problems are expensive to execute (due to severe nonlinearity, heterogeneity, multiphysics/multiscale coupling); (2) The optimization variable and uncertain parameter spaces are high dimensional, often stemming from discretizations of infinite dimensional fields such as initial conditions, sources, or material properties. We argue that the key to overcoming these challenges is to develop new mathematical, computational, and statistical methods that exploit the structure of the Bayesian inference and optimization problems mediated by their underlying complex uncertain models. This structure includes the regularity, sparsity, geometry, low intrinsic dimensionality, and multifidelity nature of the maps from uncertain parameter/optimization variable spaces to the specific objectives targeted: Bayesian inference, optimal experimental design, and optimal control design. Black box methods developed as generic tools are incapable of exploiting this structure. To be successful, we must create, integrate, and cross-fertilize ideas across multiple areas of applied math--including approximation theory, Bayesian inference, data science, experimental design, information theory, machine learning, model reduction, optimal control theory, parallel algorithms, PDE-constrained optimization, randomized algorithms, stochastic optimization, and uncertainty quantification--all while exploiting the structure of the problems at hand. With this goal in mind, we have marshaled a team of leading authorities in these areas. While the methods we develop will be broadly applicable across a wide spectrum of DOE problems in which experiments inform models and the systems those models describe must be optimized under uncertainty, we have chosen a specific area, advanced manufacturing and materials, to drive our work. AMM is characterized by complex models across multiple scales, and is a rich source of challenging problems in inference, experimental design, and optimal control, requiring multifaceted and integrated advances in applied mathematics. As such, AMM serves as an excellent vehicle to motivate and demonstrate the advances in applied mathematics developed by our center.

97 MATHEMATICS AND COMPUTING↗

Porous Microstructure Analysis (PuMA) software

The Porous Microstructure Analysis (PuMA) software was developed to provide a robust and efficient framework for computing material properties based on their microstructures. The development was motivated by advancements in X-ray microtomography, an imaging technology that can resolve the structure of a material at a sub-micron scale, in 3D and even in 4D (over time). PuMA provides the capability of computing a comprehensive spectrum of properties, from the most fundamental geometric features of a microstructure, to advanced anisotropic thermo-elastic properties. In addition, the software can generate artificial microstructures, ranging from simple analytical shapes to complex fibrous woven and non- woven geometries, which can be used in performance optimization studies. This presentation will highlight many of the capabilities of the recent open-source release.

microtomography↗

Metal–Organic-Framework-Supported and -Isolated Ceria Clusters with Mixed Oxidation States

The formation of oxygen vacancies via reversible transitions between Ce(IV) and Ce(III) plays a crucial role in the propensity of cerium oxide to act as a supporting promoter in oxidative heterogeneous catalysis. An open challenge is, however, preparation of high-porosity, supported arrays of isolated ceria(IV, III) clusters with high porosity. In this article, we report two examples of oxy-Ce(IV, III) clusters supported and spatially isolated on an oxy-zirconium MOF, NU-1000. The clusters are introduced using either of two Ce complexes (precursors): Ce IV (tmhd) 4 (tmhd = 2,2,6,6-tetramethyl-3,5-heptanedionate) or Ce III (iPrCp) 3 (iPrCp = tris(isopropyl-cyclopenta-dienyl), via SIM (solvothermal installation in MOFs). The prepared materials are named Ce-l-SIM-NU-1000 and Ce-n-SIM-NU-1000, respectively. X-ray photoelectron spectroscopy characterization shows that the ratio of Ce(III) to Ce(IV) oxidation states can be modulated. Difference envelope density analyses of X-ray scattering show that Ce x O y H z clusters in Ce-n-SIM-NU-1000 are located between pairs of Zr6 nodes, whereas in Ce-l-SIM-NU-1000, they are sited on MOF linkers throughout the micropores of NU-1000. Cluster size differences were further evaluated by pair function distribution (PDF) analyses of total X-ray scattering reveal that the node sited clusters contain of only a few cerium ions, whereas the linker-sited clusters each contain ~90 cerium ions. The observed size appears to be defined by the size of NU-1000s triangular pores, that is, cluster formation appears to be pore templated. The Ce-SIM functionalized materials are catalytically active for hydrolysis of DMNP (dimethyl 4-nitrophenyl phosphate), a nerve-agent simulant. Conversion of a small fraction of the Ce(IV) ions in which the presence of small fractions of the cerium(IV) ions in Ce-l-SIM-NU-1000 to cerium(III) significantly enhances catalytic activity-perhaps by labilizing aqua ligands and facilitating simulant binding to the clusters Lewis-basic metal ions. While not explored here, the larger clusters, when partially reduced, are, we believe, candidate catalysts for O 2 activation and subsequent selective oxidation of organic substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗