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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 271 records · Page 15

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗

Selectivity of tris complexation for Ni(II), Co(II), and Fe(II) and its effect on carbonate precipitation under alkaline conditions

Simultaneous critical element recovery and ex-situ carbon mineralization of low-grade ultramafic deposits have garnered increasing interest. Understanding the selectivity of metal complexing organic ligands for various divalent metals present in ultramafic rocks during carbonate mineralization is required to optimize this process. Here we evaluate 2-amino-2-(hydroxymethyl)-1,3-propanediol (i.e., Tris) as a model for bidentate ligands that bind divalent metals with both amine and alcohol groups in alkaline conditions (pH 8–10.5) at 25 °C and 80 °C in carbonate-buffered solutions. Protonated Tris forms a stronger complex with metal ions and is selective for trace metals with Ni(II) > Co(II) > Fe(II) during carbonate precipitation, with the rates decreasing but selectivity increasing at lower temperature and lower pH. At 25 °C, metastable amorphous hydrated carbonates form, regardless of the amount of Tris present or pH values. At 80 °C and pH 8, the Co and Fe carbonates that form are a mixture of rosasite-group minerals (Co 2 CO 3 (OH) 2 (H 2 O) and Fe 2 CO 3 (OH) 2 ) and pure carbonates (sphaerocobaltite: CoCO 3 and siderite: FeCO 3 ), with the latter more stabilized with increasing Tris concentration. In mixed metal solutions without Tris at 25 °C where Fe:Ni or Fe:Co is 2:1, Fe increases the rates of Ni or Co carbonate precipitation. However, with increasing Tris concentration the presence of Ni or Co inhibits Fe carbonate precipitation. At 80 °C without Tris, Ni or Co substitute into the iron chukanovite (Fe 2 CO 3 (OH) 2 ) lattice, increasing Ni or Co carbonate precipitation rates. Increasing Tris concentration only slightly inhibits Fe and Co precipitation, but slows Ni precipitation up to 10 times, with Fe progressively partitioning into more pure carbonate phases with distinct crystalline morphologies. These findings suggest bidentate amine-bearing ligands may be effective at Ni and Co recovery during carbon mineralization of Fe-bearing ultramafic deposits at relatively low temperatures and slightly alkaline pH.

54 ENVIRONMENTAL SCIENCES↗

From high-entropy ceramics to compositionally complex ceramics and beyond

Over the past decade, the field of high-entropy ceramics (HECs) has expanded rapidly to encompass a broad range of oxides, borides, silicides, and other ceramic solid solutions. In 2020, we proposed extending HECs to compositionally complex ceramics (CCCs), where non-equimolar compositions and the presence of long- or short-range order, although reducing configurational entropy, create new opportunities to tailor and enhance properties, often surpassing those of higher-entropy counterparts. Along these lines, several fundamental scientific questions arise. Is the entropy in HECs truly high? Is maximizing entropy always desirable? In this perspective article, I revisit key concepts and terminologies and highlight emerging directions, including dual-phase CCCs, ultrahigh-entropy phases, and novel processing routes such as ultrafast reactive sintering. I propose that exploring compositional complexity across vast non-equimolar spaces, together with exploiting correlated disorder (coupled chemical and structural short-range order), represents a transformative strategy for designing ceramics with superior performance.

36 MATERIALS SCIENCE↗

From cytoplasm to lumen—mapping the free pools of protein subunits of three photosynthetic complexes using quantitative mass spectrometry

The phycobilisome (PBS) captures light energy and transfers it to photosystem I (PSI) and photosystem II (PSII). Which and how many copies of protein subunits in PBSs, PSI, and PSII remain unbound in thylakoids are unknown. Here, quantitative mass spectrometry (QMS) was used to quantify substantial pools of free extrinsic subunits of PSII and PSI. Interestingly, the membrane intrinsic PsaL is 3-fold higher than PsaA/B. This scenario complements the static structures of these complexes as revealed by X-ray crystallography and cryo-EM. Furthermore, the ratios of ApcG and photoprotective OCP over PBS indicate a pool of extra ApcG. The 2.5 ratio of CpcG-PBS over CpcL-PBS improves our understanding of these light-harvesting complexes involved in energy capture and photoprotection in cyanobacteria.

cyanobacteria↗

Complex-Concentrated Anion Doping Enables Ultra-Stable Lattice Oxygen and Structural Integrity in Lithium-Rich Layered Oxide Cathodes

Lithium- and manganese-rich layered oxides (LMR) stand out as next-generation lithium-ion cathode chemistries, which harness both transition-metal and lattice-oxygen redox processes to deliver exceptional capacity and energy density. However, their full potential is hindered by intrinsic oxygen instability and structural degradation, resulting in pronounced voltage fade and capacity decay. Here, we present a complex-concentrated anion-doping paradigm in which multiple anions, F, Br, and S, are incorporated into the oxygen sublattice to enhance oxygen-redox and structural stability. X-ray absorption spectroscopy and aberration-corrected scanning transmission electron microscopy confirm ultra-stable local oxygen coordination environments during long-term cycling, with detrimental phase transformations and oxygen-loss-induced cavitation dramatically inhibited. Notably, we show that the characteristic LiTM6 transition metal (TM) honeycomb ordering is preserved even after electrochemical cycling. Concurrently, this strategy yields an unprecedented volume change of only 0.63% upon charging to 4.8 V vs. Li+/Li, achieving the first zero-strain LMR cathode. The resulting LMR cathode delivers ultralow voltage fade (1 mV per cycle during the first 100 cycles and becomes negligible in subsequent cycles) and outstanding energy retention (93% after 200 cycles) in a pouch cell configuration. Our complex-concentrated anion-doping concept establishes a broadly applicable strategy for resolving chemo-mechanical failure mechanisms in ceramic intercalation electrodes for next-generation energy storage.

Li-ion batteries↗

Rapid particle generation from an STL file and related issues in the application of material point methods to complex objects

Abstract In this paper, we focus on three issues related to applications of material point methods (MPMs) to objects with complex geometries. They are material point generation, compatibility of material points with a mesh, and sensitivity to mesh orientation. An efficient method of generating material points from a stereolithography (STL) file is introduced. This material point generation method is independent of the mesh used in MPM calculations. The compatibility between the material points and the mesh is then studied. We also show that the original MPM and the dual domain material point (DDMP) method are sensitive to mesh orientation. These issues are related to the calculation of the internal force and are concerns of the MPMs. They become more prominent when MPMs are applied to complex geometries. Our numerical results show that the recently developed local stress difference (LSD) algorithm (Perez et al. in J Comp Phys 498:112681, 2024) can be used to effectively address them.

36 MATERIALS SCIENCE↗

Local chemical ordering of a neutron-irradiated CrFeMnNi compositionally complex alloy

While ion-irradiation studies are a critical first step in studying compositionally complex alloys (CCAs) for nuclear applications, they do not capture all the microstructural changes occurring under the low irradiation dose rates and different particles’ scattering patterns experienced in a nuclear reactor setting. To explore these phenomena in reactor-relevant conditions for the first time in CCA, the single-phase solid-solution Cr 10 Fe 30 Mn 30 Ni 30 was neutron irradiated up to 6.61 displacements per atom at 395 and 579 °C. Irradiation-enhanced local chemical ordering (LCO) well beyond the range of short range ordering was observed, and is predicted to be the precursor to the precipitation of a coherent Ni-Mn L1 0 phase and a Cr-rich α’ phase, though TEM analysis did not indicate the presence of either in any irradiation condition. The line density of faulted dislocation loops decreased from 6.47 to 1.69 ∙ 10 15 m -2 from 3.43 to 6.61 dpa at 579 °C despite no appreciable faulted loop content in the unirradiated material. LCO is expected to increase the complexity of the energy landscape within this alloy, restricting interstitial point defect mobility and creating local regions of greater stacking fault energy. These contribute to the negative correlation between irradiation dose and faulted dislocation loop density in this study, as well as the lack of void swelling observed.

36 MATERIALS SCIENCE↗

Accurate and rapid acoustic damage characterization in complex structures using sparse sensor networks and deep learning models

Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.

36 MATERIALS SCIENCE↗

Influence of americium complexation on the radiation-induced chemical reactivity of sulfophenyl bistriazinyl pyridine (SO 3 -Ph-BTP) towards the nitrate radical

Sulfophenyl bistriazinyl pyridine (SO 3 -Ph-BTP) is a hydrophilic organic ligand used to separate actinides and lanthanides. Dose accumulation and time-resolved studies have previously provided insight into the radiolytic stability and degradation pathways of SO 3 -Ph-BTP in reprocessing environments, but no study has yet addressed the impact of minor actinide complexation on the radiation chemistry of this ligand. To begin to fill this knowledge gap, a systematic, time-resolved study exploring the reactivity of the nitrate radical (NO 3 • ) with SO 3 -Ph-BTP in the presence of trivalent americium, Am(III), has been conducted, which demonstrates enhanced reactivity (an order of magnitude faster) upon metal complexation.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗

Czochralski Growth and Characterization of a Compositionally Complex Rare Earth Aluminum Garnet Scintillator: (Gd 1/4 Y 1/4 Tb 1/4 Lu 1/4 ) 3 Al 5 O 12 :Ce

Compositionally complex oxides have garnered increasing interest for their enhanced phase stability and tunable functional properties, yet their development as bulk single crystal scintillators remains limited. Herein, we report the Czochralski growth and characterization of (Gd 1/4 Y 1/4 Tb 1/4 Lu 1/4 ) 3 Al 5 O 12 :Ce (GYTLAG), a compositionally complex garnet incorporating four dodecahedrally coordinated principal rare earth elements. The garnet phase was confirmed by powder and single crystal X-ray diffraction, and macroscopic defects are described. X-ray absorption near-edge structure measurements confirm the 3+ oxidation state of all rare earths and support their occupation of the same crystallographic site; white line intensity variations correlate with the anticipated segregation behavior. Elemental segregation is quantified by SEM/EDS and ICP-OES, and a linear trend was established between the segregation coefficient and the difference between each rare earth’s ionic radius (r) and the average ionic radius (AIR) of the dodecahedral site. This trend offers a predictive framework for compositional control in future REAG crystals grown by the Czochralski method. Photoluminescence and radioluminescence measurements reveal both Ce 3+ and Tb 3+ emission. Scintillation pulses exhibit four-component decay with dominant ~230 µs and ~1.2 ms components, and the light yield is estimated to be up to 43,000 ph/MeV under 137Cs γ-ray excitation. GYTLAG also demonstrates a strong radioluminescence efficiency and 50% lower afterglow at 20 ms compared to a LuAG:Ce reference, underscoring its promise for scintillator applications.

Compositionally complex oxide, high entropy oxide,↗

Fundamental Insights into Cathode Stability: Linking Compositional Tuning and Local Coordination in Complex Metal Oxides under Aqueous Transformations

Compositional tuning of complex metal oxides in Li-ion battery materials influences their performance as well as their end-of-life behavior, in particular, the tendency to release toxic metal cations in aqueous solution. We modeled ternary variants of a parent LiCoO 2 delafossite structure by varying the metal identity and relative amounts. This yielded ten model formulations of Li(A 4/6 B 1/6 C 1/6 )O 2 , where the material is enriched with the A metal and doped with B and C, with Ni, Mn, Co, Fe, Al, V, and Ti as constituent metals. To assess their stability in aqueous conditions, metal release energetics were calculated using a combination of Density Functional Theory calculations and thermodynamics. Metal release in ternary oxides is dictated by subtle variations in the coordination environment of the leaving group. To identify governing chemical features across diverse compositions with varying local coordination environments, we leverage random forest regression and descriptor importance analysis. A key result is that metal–oxygen orbital hybridization, quantified using a projected density-of-states-derived descriptor, H d/p , provides a physically grounded measure of interaction strength that governs metal release energetics. This refined perspective goes beyond conventional oxidation state considerations and offers more robust insights for materials science. Finally, we model defect surface-bound O 2 dimer formation as a proxy for reactive oxygen species (ROS) generation. The results show that Ni-rich compositions more readily stabilize spin-polarized O 2 dimers, corroborating experimental reports of an increased ROS-driven biological response. In conclusion, our results establish a compositional and electronic basis for metal release and surface oxygen reactivity that form a rationale for complex metal oxide design principles.

36 MATERIALS SCIENCE↗

Complexing Agent-Assisted Membraneless Zinc–Iodine Aqueous Batteries

A membrane is required for conventional zinc–iodine aqueous batteries, since soluble polyiodides cross over to the anode side and react with zinc metal spontaneously. Making the battery membraneless increases ion transport and reduces its cost and overall footprint. In this paper, a membraneless Zn–I 2 aqueous battery is demonstrated, employing a complexing agent, 1-butyl-1-methylpyrrolidinium iodide (MBPI), to promote the formation of I 5 – -containing, phase-separated polyiodides upon charging, to minimize self-discharge and suppress Zn dendrite growth. With an additional 0.3 M MBPI in 4 M ZnI 2 electrolyte, the membraneless battery achieved 65 cycles with >85% Coulombic efficiency, whereas the MBPI-free control failed immediately. Additionally, a volumetric capacity of 14.3 Ah L –1 was achieved, surpassing those of most membraneless batteries reported to date regardless of redox chemistry, and underscores the potential of complexing agents in simplifying the architecture of conventional Zn–I 2 flow batteries.

25 ENERGY STORAGE↗

Compositionally Complex Spinel Oxides as Conversion Anodes for Lithium-Ion Batteries

Four different compositionally complex multicomponent M 3 O 4 spinels containing 5–8 distinct metals were prepared by a rapid combustion synthesis method or solvothermal synthesis. High resolution synchrotron X-ray diffraction patterns show that the materials consist primarily of spinel phases with small amounts of rock salt impurities, and, in several samples, a minor amount of contracted spinel phase. Materials were investigated as conversion anodes in lithium half-cells and delivered significantly higher capacities than two-component MgFe 2 O 4 made by combustion synthesis. X-ray absorption near-edge structure (XANES) was used to estimate the oxidation states of the metals in the pristine, lithiated (discharged) and delithiated (charged) materials to better understand the redox processes in half cells that led to the improvement. Co, Ni, and Zn are reduced to low oxidation states during lithiation (cell discharge) but are only partially oxidized. The presence of a conductive metallic network that forms after lithiation is thought to account for the improved electrochemical characteristics. Interestingly, in most of the samples, iron is not fully reduced during initial lithiation unlike what happens with a set of related high entropy spinel ferrites studied previously. Finally, the improved electrochemical properties of these materials illustrates both the advantages of complexity and the difficulties in predicting their behavior.

25 ENERGY STORAGE↗

Covalency of M–N Bonds in Isomorphous Lanthanide and Actinide 5-(2-Pyridyl)-1H-tetrazolate Complexes

Experimental and computational analyses of [M(pdtz) 3 (H 2 O) 3 ]·3.5H 2 O (M 3+ = Pu 3+ −Cm 3+ , La 3+ −Nd 3+ , and Sm 3+ −Ho 3+ , pdtz− = 5-(2-pyridyl)-1H-tetrazolate) were conducted to understand potential differences in bonding between lanthanide and actinide complexes with a N-donor ligand. Structural analyses show that the An−N bond distances in the Pu 3+ , Am 3+ , and Cm 3+ complexes are within error of one another. Whereas in the lanthanide series, there is a nearly linear decrease in the Ln−N bond lengths from La 3+ to Ho 3+ (excluding Pm 3+ ). The An−N bond lengths are ∼0.015 Å shorter than their similarly-sized lanthanide analogs, in agreement with computational results that suggest greater covalent character in these bonds versus those with lanthanides. QTAIM analysis indicates that the An−N orbital mixing remains essentially unchanged from Pu 3+ to Cm 3+ , consistent with the nearly identical An−N bond lengths. However, upon deconvolution of the NLMOs into orbital compositions, the metal orbital contributions to An−N bonding decreases slightly overall wherein the 6d involvement remains constant, 7s involvement slightly increases, and 5f participation decreases. The molecular orbital energy diagram indicates that energy degeneracy between the 5f metal and 2p ligand orbitals increases from Pu 3+ to Cm 3+ and counteracts the contraction of the 5f orbtials. Together with prior reports of decreasing energy degeneracy between 5f and 3p orbitals from Np 3+ to Cf 3+ , these observations provide guidance on understanding how chemical bonding evolves in the actinide series.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Interacting dust grains in complex plasmas: Ion wake formation and the electric potential

Dust grains have been used as minimally invasive probes to determine plasma parameters including the plasma density, temperature, and electric field in a plasma discharge. However, the dust grains in a plasma generate local potential disturbances due to the collection of charge and the subsequent electrostatic interactions between the dust and charged plasma particles. Dust grains in close proximity to one another exhibit interesting non-reciprocal interactions and self-organize into structures such as one-dimensional filamentary chains, two-dimensional “zigzags,” and three-dimensional helices, among others. The formation of these structures suggests that although the dust grains may be less invasive than traditional plasma probes, the disturbance to the local plasma environment introduced by dust grains is non-trivial. Commonly used analytic forms of the electric potential describing complex plasmas have failed to resolve the near-dust region, and as a result are insufficient to provide insight about the formation of complex dust structures. Here, we use an N-body simulation to compute the electric potential from ion densities near various dust grain configurations. We provide an alternative description to the standard analytic model for the electric potential of dust and ion wakes based on a Gaussian shaped cloud of ions. The electric potential obtained from simulations is used to identify minimum energy configurations for two and three dust grains. It is further demonstrated that the minimum potential region identified for N dust grains and their associated ion wakes does not predict the minimum-energy configuration of N + 1 dust grains.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Learning a general model of single phase flow in complex 3D porous media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

36 MATERIALS SCIENCE↗

Taxogenomic analysis of Pichia senei sp. nov. and new insights into hybridization events in the Pichia cactophila species complex

Three strains of a novel yeast species were isolated from necrotic cactus tissues of Cereus saddianus and Micranthocereus dolichospermaticus and from phytotelmata of Bromelia karatas. DNA sequence analysis of the Internal Transcribed Spacer (ITS) region and D1/D2 domains of the large subunit ribosomal RNA, along with whole genome phylogenomic analysis, showed that this yeast is most closely related to Pichia insulana, Pichia cactophila, and Pichia inconspicua. The new species differs by 10–13 nucleotide substitutions from these species in D1/D2 sequences and exhibits <90% genome-wide average nucleotide identity to them. The name Pichia senei sp. nov. is proposed for the novel species, which is homothallic and produces asci with one to four hat-shaped ascospores. The holotype is CBS 16311 (MycoBank MB 858723). Taxogenomic analyses of the P. cactophila species complex, including P. senei, provide new insights about the hybridizations events that shaped this group. Pichia insulana and P. inconspicua are identified as the parental lineages that originated P. cactophila, and P. senei also appears closely related to one of the progenitors of P. inconspicua. We assess phylogeny, heterozygosity, and ploidy to explore the processes shaping diversity, showing how genomic data support yeast species delimitation and reveal complex hybridization.

59 BASIC BIOLOGICAL SCIENCES↗

Reducing Operator Complexity of Galerkin Coarse-grid Operators with Machine Learning

Here, we propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in multigrid (MG) methods, addressing the well-known issue of increasing operator complexity. Guided by the MG theory on spectrally equivalent coarse-grid operators, we have developed novel machine learning algorithms that utilize neural networks combined with smooth test vectors from multigrid eigenvalue problems. The proposed method demonstrates promise in reducing the complexity of coarse-grid operators while maintaining overall MG convergence for solving parametric partial differential equation problems. Numerical experiments on anisotropic rotated Laplacian and linear elasticity problems are provided to showcase the performance and comparison with existing methods for computing non-Galerkin coarse-grid operators.

97 MATHEMATICS AND COMPUTING↗