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DOE OSTI2024

A reactive electrochemomechanical theory for growth and remodeling of polyelectrolyte hydrogels and application to dynamic polymerization of $\text{DNA}$ hydrogels

Here this study develops a framework for growth and remodeling of active polyelectrolyte hydrogels that accounts for effects of compositional changes on the mechanical response. By developing a reactive electrochemomechanical theory, thermodynamical constraints upon reactive and remodeling processes are elucidated within a general framework that allows any number of chemical reactions to evolve the response of the gel and transfer mass and charge between constituents. Fully coupled, nonlinear constitutive relations are adopted for molar fluxes, allowing exploration of effects including cross-diffusion, electrophoresis, and electro-osmosis. A robust finite element implementation is developed in the open source FEBio software (febio.org) by exploiting an equivalence between electrochemomechanics and mixture theory. The implementation is verified against analytical solutions for free swelling, and a proper reduction to a prior chemomechanical theory is demonstrated for neutral gels swollen only by a solvent with no solutes. The theory and implementation are then applied to model the tunable large swelling achieved through dynamic polymerization of DNA crosslinkers seen in our recently developed experimental hydrogel system (Cangialosi et al., 2017). A novel constitutive model for reaction-driven evolution of the locking stretch λ L in a non-Gaussian mechanical free energy was developed, where the increasing concentration of DNA crosslinkers makes further swelling energetically favorable. With a single free parameter, excellent agreement was found between measured and predicted equilibrium swelling ratios. This study demonstrated the ability to extend the electrochemomechanical framework to include chemical reactions and composition-aware constitutive models, and showed that development of reactive models allows simulation of complex dynamic polymerization phenomena not treated before. The theoretical frame here can be further expanded in scope to incorporate additional non-ideal and nonlinear phenomena.

42 ENGINEERING↗

DOE OSTI2022

Emergent magnetism and exchange bias effect in iron oxide nanocubes with tunable phase and size

Abstract We report a systematic investigation of the magnetic properties including the exchange bias (EB) effect in an iron oxide nanocube system with tunable phase and average size (10, 15, 24, 34, and 43 nm). X-ray diffraction and Raman spectroscopy reveal the presence of Fe 3 O 4 , FeO, and α -Fe 2 O 3 phases in the nanocubes, in which the volume fraction of each phase varies depending upon particle size. While the Fe 3 O 4 phase is dominant in all and tends to grow with increasing particle size, the FeO phase appears to coexist with the Fe 3 O 4 phase in 10, 15, and 24 nm nanocubes but disappears in 34 and 43 nm nanocubes. The nanocubes exposed to air resulted in an α -Fe 2 O 3 oxidized surface layer whose thickness scaled with particle size resulting in a shell made of α -Fe 2 O 3 phase and a core containing Fe 3 O 4 or a mixture of both Fe 3 O 4 and FeO phases. Magnetometry indicates that the nanocubes undergo Morin (of the α -Fe 2 O 3 phase) and Verwey (of the Fe 3 O 4 phase) transitions at ∼250 K and ∼120 K, respectively. For smaller nanocubes (10, 15, and 24 nm), the EB effect is observed below 200 K, of which the 15 nm nanocubes showed the most prominent EB with optimal antiferromagnetic (AFM) FeO phase. No EB is reported for larger nanocubes (34 and 43 nm). The observed EB effect is ascribed to the strong interfacial coupling between the ferrimagnetic (FiM) Fe 3 O 4 phase and AFM FeO phase, while its absence is related to the disappearance of the FeO phase. The Fe 3 O 4 / α -Fe 2 O 3 (FiM/AFM) interfaces are found to have negligible influence on the EB. Our findings shed light on the complexity of the EB effect in mixed-phase iron oxide nanosystems and pave the way to design exchange-coupled nanomaterials with desirable magnetic properties for biomedical and spintronic applications.

Physics↗

DOE OSTI2024

Trebiskyite, the First Titanium-Decavanadate Mineral

Abstract Trebiskyite, Na3Mg2[TiV9O28]·22H2O, from the Pickett Corral mine, Bull Canyon, Montrose County, Colorado, USA, is the first mineral containing essential Ti as a component of a decavanadate polyanion. Crystals are thin prisms or needles up to ∼0.3 mm in length, occurring as individuals and in divergent sprays. Trebiskyite crystals are translucent yellow with vitreous luster and pale-yellow streak and are non-fluorescent under both longwave and shortwave ultraviolet illumination. The crystals exhibit elongation on [001] and are modified by several 0kl prism forms, possibly corresponding to {010}, {011}, and {012}. The Mohs hardness is ca. 2 based on scratch tests. Crystals have brittle tenacity with irregular fracture. No apparent cleavage, nor twinning, was evident, and the mineral is readily soluble in H2O. The density measured by flotation in methylene iodide-toluene mixture is 2.38(2) g/cm3. The calculated density is 2.352 g/cm3 for the empirical formula and 2.343 g/cm3 for the ideal formula. Electron microprobe analyses provided the empirical formula Na3.39Mg1.87V8.75Ti1.25O50H44.00, based on Ti + V = 10 apfu, O = 50 apfu, with the Na content having been determined from the refined crystal structure. Trebiskyite is monoclinic, space group P21/c, a = 9.478(4), b = 21.426(11), c = 11.267(5) Å, β = 114.572(7)°, V = 2080.8(13) Å3, Z = 2. The structure was refined from 6642 reflections to a final R1 = 0.0517 for reflections I > 2σ(I). The structural unit of trebiskyite, (TiV9O28)7−, is built from a Ti-substituted decavanadate polyanion, (V10O28)6−, found in other members of the pascoite family. Chemically, trebiskyite is closely related to lasalite, Na2Mg2[V10O28]·20H2O, and huemulite, Na4Mg[V10O28]·24H2O; however, trebiskyite contains a novel structural arrangement that is unique among the decavanadate minerals. The interstitial complex in trebiskyite has the ideal formula {[Na3Mg2(H2O)19](H2O)3} and consists of chains of distorted octahedrally coordinated Na+ and regular Mg[O(H2O)5] octahedra. The observed low incident bond valence to the V1 site (4.45 vu) and marked deviation in vanadyl and V–Oeq bond lengths indicate that this site is host to the essential Ti4+ identified during chemical analyses.

Mineralogy↗

DOE OSTI2025

Polypropylene Composites Reinforced With Recycled Waste Cellulosic Fiber/Fine Mixture: The Impact of Cellulose Sieving on Performance

This study explores how a sieving step of waste cellulosic fiber and fine (WCFF) mixture affects the performance of WCFF‐loaded polypropylene (PP) composites and whether the separation of fines from fibers offers an added benefit. The WCFF samples were downsized, and four different filler size ranges were sieved using a series of mesh sizes from 4 to 0.85 mm. The WCFF/PP composites were then compounded at 20 wt.% loading of WCFF using a twin‐screw extruder. Incorporating WCFF increased the tensile strength to 41.28 MPa and the modulus to 3207 MPa, accounting for 28% and 38% enhancements, respectively. Interestingly, the greatest improvements were associated with the nonsieved WCFF case, and the sieved WCFF fibers provided only marginal enhancements over virgin PP. The outperformance of nonsieved WCFF was attributed to the synergistic reinforcement of hybrid fibers and fines as well as the maintenance of longer fibers in the system. However, the strain at break and impact strength of PP decreased after introducing WCFF. Moreover, the complex viscosity and storage modulus increased with an increase in the filler size, due to the formation of a more effective percolative network. The PP's crystallinity exhibited a relatively strong dependency on the sieving, where WCFF samples with short‐aspect‐ratio fillers promoted the crystallinity significantly. It was also found that the WCFF degradation onset temperature increased once it was incorporated into PP. This study suggests that waste cellulosic feedstocks can be utilized as a reinforcement without additional sieving to manufacture high‐performance and cost‐effective composites.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis

Abstract Solid‐state synthesis from powder precursors is the primary processing route to advanced multicomponent ceramic materials. Designing reaction conditions and precursors for ceramic synthesis can be a laborious, trial‐and‐error process, as heterogeneous mixtures of precursors often evolve through a complicated series of reaction intermediates. Here, ab initio thermodynamics is used to model which pair of precursors has the most reactive interface, enabling the understanding and anticipation of which non‐equilibrium intermediates form in the early stages of a solid‐state reaction. In situ X‐ray diffraction and in situ electron microscopy are then used to observe how these initial intermediates influence phase evolution in the synthesis of the classic high‐temperature superconductor YBa 2 Cu 3 O 6+ x (YBCO). The model developed herein rationalizes how the replacement of the traditional BaCO 3 precursor with BaO 2 redirects phase evolution through a low‐temperature eutectic melt, facilitating the formation of YBCO in 30 min instead of 12+ h. Precursor selection plays an important role in tuning the thermodynamics of interfacial reactions and emerges as an important design parameter in planning kinetically favorable synthesis pathways to complex ceramic materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Modification of conventional peak shapes to accurately represent spectral asymmetry: High-Resolution X-ray photoelectron spectra of [C 4 C 1 Pyrr][NTf 2 ] and [C 8 C 1 Im][NTf 2 ] ionic liquids

X-ray photoelectron spectroscopy (XPS) is one of the most widely used techniques for surface characterization. Analysis of XPS data is challenging and requires the analyst to fit the data with synthetic line shapes to reach physically meaningful interpretations. Experimental spectral envelopes, however, are complex and display asymmetric features that are often ignored or attributed to additional chemical components. The high-resolution XPS spectra of [C 4 C 1 Pyrr][NTf 2 ] and [C 8 C 1 Im][NTf 2 ] all exhibit a degree of asymmetry which is systematically observed at the higher binding energy side of photoemission envelopes. Here, we present the development of a refined fitting procedure for XPS spectra of these ionic liquid-based systems which include (a) Shirley background offset necessary to account for the insulator-like region and (b) spectral asymmetry in C 1s and N 1s regions. Further, Shirley and trapezoid components are applied to compensate for inelastic scattering taking place during electron transitions as high as 7.8 eV above the start of the fitting region in C 1s high-resolution spectrum. To demonstrate the fitness of this model, we present an analysis of a 2:1 mixture of [C 4 C 1 Pyrr][NTf 2 ]: [C 8 C 1 Im][NTf 2 ].

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

KBKit: A Python Toolkit for Kirkwood–Buff Theory from Molecular Dynamics

Thermodynamic properties of liquid mixtures govern processes that range from drug delivery to energy storage, yet extracting these properties from molecular simulations remains challenging. Kirkwood–Buff (KB) theory offers a rigorous route by linking microscopic pair distribution functions to macroscopic free energies, but practical use of the theory has been hindered by two obstacles: (i) the long simulations needed to obtain well-converged Kirkwood-Buff integrals (KBIs) and (ii) the specialized corrections required to translate finite-size data to the thermodynamic limit. $\texttt{KBKit}$ is an open-source Python package that removes these barriers. It automatically computes KBIs and derived thermodynamic quantities from GROMACS input files, applies state-of-the-art finite-size corrections, and provides built-in diagnostic tools to quantify statistical uncertainty. Written with modern software-engineering practices—continuous integration, extensive unit testing, and thorough documentation—$\texttt{KBKit}$ is both reliable and easy to extend. By condensing complex KBI analysis into a few intuitive commands, $\texttt{KBKit}$ enables researchers to incorporate KB theory into routine simulation workflows and accelerate the discovery of solution-phase thermodynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Microwave-Based Synthesis of Functional Morphological Variants and Carbon Nanotube-Based Composites of VS 4 for Electrochemical Applications

A novel facile, fast, and efficient microwave-assisted method was developed to synthesize a number of diverse nanostructured motifs (ranging from nanorods to nanoflowers) of VS 4 along with its associated composite heterostructures, VS 4 /multi-walled carbon nanotube (MWNT; i.e., multi-walled carbon nanotubes). Specifically, we have probed and correlated the effects of a number of specific experimental variables, including primarily precursor, solvent, temperature, and time. We noted that nanorods formed more readily with VO(acac) 2 as the vanadium precursor and n-methyl-2-pyrrolidone (NMP) as a polar reaction solvent. By contrast, we determined that hierarchical three-dimensional (3D) nanoflower-like assemblies, ranging in size from 100 to 200 nm in average diameter, could be controllably synthesized by using Na 3 VO 4 as the vanadium precursor and an aqueous water: polar solvent mixture as the reaction medium. We also observed that VS 4 disintegrates, when in the presence of either air, solution, or a combination of these environments, and established that the extent of VS4 nanorod decomposition could be almost fully prevented by storage under nitrogen. From an application’s perspective, our VS 4 is electrochemically active and shows behavior, consistent with the literature. In particular, as compared with pristine VS 4 nanorods alone, we observed enhanced electrochemical activity with (i) 3D hierarchical flower-like motifs, (ii) unique necklace-like VS 4 nanorod–MWNT composites, and (iii) samples in which as-prepared VS4 nanorods had been annealed. Moreover, we found that the rational application of specific physical and chemical processing treatments, such as (i) thermal annealing to improve crystallinity, (ii) the addition of MWNTs to form conductive composites, and (iii) the evolution of morphology from one-dimensional (1D) nanorods to more complex 3D nanoflowers, was favorable to the resulting electrochemical performance with respect to increasing stability and reversibility.

25 ENERGY STORAGE↗

DOE OSTI2022

Chemical and structural characterization of particulate fallout isolated from air-filters

We report particulate nuclear fallout is the radioactive byproduct of a nuclear event formed by the mixture of proximate environmental materials with vaporized bomb debris. The fallout debris can be transported into the atmosphere during cloud rise, raining out locally and dispersing globally constituting a radiation hazard and contributing to the distribution of anthropogenic radionuclides in the environment. Questions remain on how entrainment of environmental material in the fireball affects fallout formation processes and radionuclide incorporation and distribution during cooling. To inform the characterization of fallout including the development of fallout size distributions and how radionuclides are incorporated into fallout debris where entrainment plays a role, we analyzed an archived historic US air-filter collected by aircraft in the aftermath of ground interacting nuclear tests. Particulate fallout collected on the filter was isolated and analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and NanoSIMS (Secondary Ion Mass Spectrometry) to determine chemistry, structure, morphology, and size of the particles. Results demonstrate that the particles isolated from the filter have spherical shape, display complex internal structures, and are mainly composed of Fe and Si oxides. In these spherical particles, Pu is preferentially associated with Fe-rich composition. The characterization of fallout particulate samples can provide information on nucleation and particle growth from the vapor phase to improve modeling and simulation of fallout hazards.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2021

Electronic structure and two-band superconductivity in unconventional high- T c cuprates Ba 2 CuO 3+δ

We report that the recently discovered cuprate superconductor Ba 2 CuO 3+δ exhibits a high T c ≃73 K at δ ≃ 0.2. The polycrystal grown under high pressure has a structure similar to La 2 CuO 4 but with dramatically different lattice parameters due to the CuO 6 octahedron compression. The crystal field in the compressed Ba 2 CuO 4 leads to an inverted Cu 3d e g complex with the d x 2 -y 2 orbital sitting below the d 3z 2 - r 2 and an electronic structure highly unusual compared to the conventional cuprates. We construct a two-orbital Hubbard model for the Cu d 9 state at hole doping x = 2δ and study the orbital-dependent strong correlation and superconductivity. For the undoped case at x = 0 , we found that strong correlation drives an orbital-polarized Mott-insulating state with the spin-1/2 moment of the localized d 3z 2 - r 2 orbital. In contrast to the single-band cuprates where superconductivity is suppressed in the overdoped regime, hole doping the two-orbital Mott insulator leads to orbital-dependent correlations and the robust spin and orbital exchange interactions produce a high-T c antiphase d-wave superconductor even in the heavily doped regime at x = 0.4 . We conjecture that Ba 2 CuO 3+δ realizes mixtures of such heavily hole-doped superconducting Ba 2 CuO 4 and disordered Ba 2 CuO 3 chains in a single-layer or predominately separated bilayer structure. Our findings suggest that unconventional cuprates with liberated orbitals as doped two-band Mott insulators can be a direction for realizing high-T c superconductivity with enhanced transition temperature T c .

36 MATERIALS SCIENCE↗

DOE OSTI2025

Measurement of partial vapor pressures of salt mixtures via combined horizontal transpiration and thermogravimetric analysis

A method combining thermogravimetric analysis (TGA) and horizontal transpiration with elemental analysis via inductively coupled plasma mass spectrometry or ion chromatography enabled calculation of partial pressures of individual salts in molten mixtures. TGA quantified total mass loss, while transpiration identified vapor-phase composition. Furthermore, two chloride (NaCl-MgCl 2 , NaCl-MgCl 2 + UCl 3 ) salts and one mixed halide (LiCl-LiF + Li 2 O) salt were analyzed at 750 °C and 550 °C, respectively. NaCl and MgCl 2 vapor pressures were 2.19–2.61 × 10 -4 atm and 2.47–2.48 × 10 -5 atm (dependent upon the identity of the invesitgated mixture); UCl 3 was 1.42 × 10 -7 atm. LiCl and LiF vapor pressures at 550 °C were 1.53 × 10 -6 and 6.32 × 10 -6 atm, respectively. Additionally, the TGA method was validated against values from the literature for unary LiCl and LiF.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Manganese Catalyzed Partial Oxidation of Light Alkanes

The catalytic partial oxidation of methane is achieved at low temperatures (<200 °C) using manganese oxides and manganese salts in mixtures of trifluoroacetic acid and trifluoroacetic anhydride. Dioxygen is used as the in situ terminal oxidant. For Mn oxides (e.g., MnO 2 , Mn 2 O 3 , and Mn 3 O 4 ), we studied stoichiometric methane partial oxidation in HTFA (TFA = trifluoroacetate). Using a Mn trifluoroacetate salt, at 180 °C and under 25 psig of methane, product selectivity for the mono-oxidized product methyl trifluoroacetate (MeTFA) is observed to be >90% at ~35% methane conversion at approximately 6 turnovers. Under our catalytic methane oxidation reaction conditions, MeTFA is stable against overoxidation, which explains the likely high selectivity at conversions >15%. Using combined experimental studies and DFT calculations, a mechanism involving soluble and molecular Mn species in the catalytic cycle is proposed. The proposed reaction pathway involves initial activation of Mn II by dioxygen, cleavage of a methane C–H bond by a Mn III hydroxo intermediate, rebound of the methyl radical to generate MeTFA, and finally regeneration of the starting Mn II complex. Also, this process is shown to be applicable to the oxidation of ethane, favoring the mono-oxidized product ethyl trifluoroacetate (EtTFA) and reaching ~46% conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning

As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing high dimensionality and complex data sets, which challenges the traditional methods used to extract the physical information they contain. Here, we present the use of a novel self-supervised machine-learning method to visualize the multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Abstract Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates,” which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from “global” chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using “global” surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using “expert-selected” surrogates ( n = 3) instead of “global” surrogates ( n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases. Graphical Abstract

Pu, Shirley (ORCID:0000000201223797)↗

DOE OSTI2024

Unifying Combinatorial and Graphical Methods in Artificial Intelligence

Recently, a new graph Laplacian, called the inner product Laplacian, was introduced which generalizes many existing Laplacians, including the normalized and combinatorial Laplacian and their weighted variants. The key observation behind the inner product Laplacian is that by defining appropriate inner product spaces on the vertices and edges, the standard Laplacians can be recovered as Hodge Laplacians over the simplicial complex formed by the edges and vertices. These inner product spaces form a natural way to incorporate non-combinatorial information into the definition of a domain-specific Laplacian. In particular, in contrast to current domain-specific weighting schemes which rely solely on edge weights, information regarding the similarity of non-adjacent vertices and arbitrary pairs of edges can be effectively incorporated into the Laplacian. In order to illustrate this approach we consider the problem of calculating the potential energy of an atomistic configuration using Graph Neural Networks. In comparison with start-of-the-art approaches, such as SchNet, our approach replaces a learned (via auto-encoder) representation of the atom types with an inner product space on atoms based on scientific knowledge (e.g., electronegativity). We will illustrate how this approach captures key chemical properties of the molecules and compare the energy calculations with state-of-the-art neural network approaches. However, to compute the resulting Laplacian involves a mixture of sparse and dense matrix computation and yields a dense matrix as the basis for the graph convolution. This dense convolutional kernel necessitates moving away from the standard message passing framework for graph neural networks and increases the computational cost of applying the kernel. In order to mitigate these costs we investigate means of leveraging the mixed sparse and dense computations to reduce the overall computational cost and how these approaches can be automatically transferred to energy efficient hardware (e.g., field programmable gate arrays (FPGAs)).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Investigating Covalent Bonding in f-elements using Gas-phase Ion Chemistry

Introduction In the reprocessing of f-elements present in used nuclear fuels, a variety of diglycolamides (DGA’s) are used as extractants for actinide partitioning. In particular, the Actinide-Lanthanide Separation (ALSEP) process typically utilizes either the N,N,N’,N’-tetraoctyl diglycolamide (TODGA) or N,N,N',N'-tetra-2-ethylhexyl diglycolamide (T2EHDGA) extractant ligands following the partitioning of uranium and plutonium from used nuclear fuel. To better understand fundamental interactions in these processes, covalent bonding of several f-elements with diglycolamides, primarily TODGA, is investigated in the gas phase using nanospray ionization and a quadrupole time-of-flight mass spectrometer. Further, analysis of the identity and relative strength of the cluster is enabled by MS2 isolation and collision induced dissociation. Methods Metal ion cluster analysis was completed using a Bruker mircOTOF-Q II quadrupole time-of-flight mass spectrometer equipped with a CaptiveSpray nanospray ion source. Metal:ligand solutions were prepared as 30 µM europium nitrate, samarium nitrate, cerium nitrate, or holmium nitrate and 3 µM DGA in acetonitrile or a 50:50 mixture of acetonitrile: isopropanol. Cluster mass spectra and collision-induced dissociation experiments were conducted in positive mode. Preliminary data To examine the patterns and relative strength of lanthanide: DGA interactions, MS2 experiments were completed with each lanthanide species listed above. Preliminary analyses of samarium and europium TODGA clusters suggest several combinations of TODGA and nitrate forming. The samarium cluster experiments yielded Sm(TODGA)x clusters with a samarium:TODGA ratio of up to 1:7 able to be isolated and evidence of greater ratios present in the mass spectrum. This is surprising, as metal clusters are not expected to have a coordination space able to accommodate this many ligands as large as TODGA. MS2 experiments show that, at higher ratios and with sufficient collision energy, entire TODGA ligands are removed instead of being fragmented. These experiments show that a lower collision energy is required to remove ligands as the number of bound TODGA’s increases, suggesting that in larger clusters, ligands are more delicately complexed to the metal. In addition to Sm(TODGA)x, several clusters were observed with nitrate ions bound to the metal in addition to TODGA. With a single nitrate ion, clusters with up to six TODGA’s were able to be isolated. In a similar pattern to the samarium clusters with only TODGA, less collision energy is required to eliminate one or more TODGA’s with increasing size. MS2 experiments suggest clusters with one nitrate appear to be of an equivalent or greater stability to clusters which replace the nitrate with a TODGA, as more collision energy is required to remove a TODGA ligand. These species with one nitrate are also in a higher abundance than the equivalent TODGA only cluster. With two nitrate ions, only clusters with a single TODGA were able to be isolated. Analogous europium experiments resulted in very similar clusters. Ratios of up to 1:7 Eu:TODGA were able to be isolated, and clusters with one nitrate and up to five TODGAs were isolated. In clusters with two nitrate ions, one or two TODGA’s could also be bound to the metal. MS2 experiments suggested, similarly to samarium, that larger clusters required less collision energy to eliminate TODGA. Europium clusters with one nitrate are in greater abundance and are stronger than the equivalent cluster which replaces the nitrate with TODGA. Similar analysis with cerium and holmium is ongoing, as well as analysis with other DGA ligands to compare relative strengths of the lanthanide metals with various extractant ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗

DOE OSTI2023

Production of carotenoids from aromatics and pretreated lignocellulosic biomass by Novosphingobium aromaticivorans

ABSTRACT Carotenoids are lipophilic compounds found in the membranes of various organisms. Individual carotenoids are also commodity chemicals, produced industrially for use as food additives, nutritional supplements, cosmetics, and pharmaceuticals. The alphaproteobacterium Novosphingobium aromaticivorans has previously been established as a potential platform microbe for converting aromatic compounds derived from lignocellulosic plant biomass into valuable extracellular products. Here, we show that N. aromaticivorans DSM 12444 cells naturally produce the carotenoid nostoxanthin, and we construct a set of gene deletion mutants that accumulate β-carotene, lycopene, or zeaxanthin, which are predicted intermediates in nostoxanthin biosynthesis as well as commodity chemicals. We also show that a mutant strain heterologously expressing a CrtW protein accumulates the carotenoid astaxanthin. When grown on vanillate as the carbon source, we find that the levels of carotenoids are not significantly affected by O 2 concentration in the tested range of 5% to 21% O 2 . We also show that these carotenoids are produced at comparable levels when strains are grown in liquor from alkaline pretreated sorghum biomass [sorghum alkaline pretreatment liquor (APL)], which contains a mixture of aromatics. Finally, we construct strains that produce zeaxanthin, β-carotene, or astaxanthin concurrently with 2-pyrone-4,6-dicarboxylic acid, a potential building block for biodegradable polymers, when grown in sorghum APL. Combined, our results show that N. aromaticivorans can simultaneously produce valuable intracellular and extracellular commodities when grown in the presence of either pure aromatics or pretreated lignocellulosic biomass. IMPORTANCE There is economic and environmental interest in generating commodity chemicals from renewable resources, such as lignocellulosic biomass, that can substitute for chemicals derived from fossil fuels. The bacterium Novosphingobium aromaticivorans is a promising microbial platform for producing commodity chemicals from lignocellulosic biomass because it can produce these from compounds in pretreated lignocellulosic biomass, which many industrial microbial catalysts cannot metabolize. Here, we show that N. aromaticivorans can be engineered to produce several valuable carotenoids. We also show that engineered N. aromaticivorans strains can produce these lipophilic chemicals concurrently with the extracellular commodity chemical 2-pyrone-4,6-dicarboxylic acid when grown in a complex liquor obtained from alkaline pretreated lignocellulosic biomass. Concurrent microbial production of valuable intra- and extracellular products can increase the economic value generated from the conversion of lignocellulosic biomass-derived compounds into commodity chemicals and facilitate the separation of water- and membrane-soluble products.

09 BIOMASS FUELS↗