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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tumor targeted alpha particle therapy with an actinium-225 labelled antibody for carbonic anhydrase IX

Selective antibody targeted delivery of α particle emitting actinium-225 to tumors has significant therapeutic potential. This work highlights the design and synthesis of a new bifunctional macrocyclic diazacrown ether chelator, H 2 MacropaSqOEt, that can be conjugated to antibodies and forms stable complexes with actinium-225. The macrocyclic diazacrown ether chelator incorporates a linker comprised of a short polyethylene glycol fragment and a squaramide ester that allows selective reaction with lysine residues on antibodies to form stable vinylogous amide linkages. This new H 2 MacropaSqOEt chelator was used to modify a monoclonal antibody, girentuximab (hG250), that binds to carbonic anhydrase IX, an enzyme that is overexpressed on the surface of cancers such as clear cell renal cell carcinoma. This new antibody conjugate (H 2 MacropaSq-hG250) had an average chelator to antibody ratio of 4 : 1 and retained high affinity for carbonic anhydrase IX. H 2 MacropaSq-hG250 was radiolabeled quantitatively with [ 225 Ac]Ac III within one minute at room temperature with micromolar concentrations of antibody and the radioactive complex is stable in human serum for >7 days. Evaluation of [ 225 Ac]Ac(MacropaSq-hG250) in a mouse xenograft model, that overexpresses carbonic anhydrase IX, demonstrated a highly significant therapeutic response. It is likely that H 2 MacropaSqOEt could be used to modify other antibodies providing a readily adaptable platform for other actinium-225 based therapeutics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Material Extrusion Printing of Poly(Acrylonitrile‐Styrene‐Acrylate) and Poly(Acrylonitrile‐Butadiene‐Styrene) Structures Reinforced with Poly(Phenylene Oxide) Additives for Improved Thermomechanical Properties and their Surface Analysis by Time‐of‐Flight Secondary Ion Mass Spectrometry

Fused filament fabrication offers the ability to 3D print complex geometries made from plastic filament materials; however, these parts are mechanically outperformed by parts created by traditional fabrication methods. To overcome this challenge, a high-performance polymer poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) is incorporated as an additive into two common engineering thermoplastics, poly(acrylonitrile-styrene-acrylate) (ASA) and poly(acrylonitrile-butadiene-styrene) (ABS). Structures printed from these polymer blends are more mechanically robust compared to those prepared from the parent polymers, with low loading levels (1–5 wt%) of PPO improving the elastic strength by up to ≈30% relative to the parent terpolymers. Even at higher loading levels (10 and 20 wt% PPO), there is no evidence of additive aggregation in the model thin films, which is supported by compositional analysis of the copolymers and chemical analysis via time-of-flight secondary ion mass spectrometry. The enhancements in mechanical properties of ASA and ABS blends appear to be a consequence of homogeneous incorporation of the PPO additive. In conclusion, this work explores expanding materials-property space using miscible blends of engineering thermoplastics to improve mechanical performance as a general approach to overcoming challenges with parts created by melt-based material extrusion printing.

additive manufacturing↗
DOE OSTI2022

Ligand Coverage and Exciton Delocalization Control Chiral Imprinting in Perovskite Nanoplatelets

Chiral perovskites have generated significant recent interest, yet little is known about how their chiro-optical properties arise. In this study, chiral methylammonium lead halide perovskite nanoplatelets (NPLs) with varied halide and ligand compositions are prepared using direct synthetic methods. Circular dichroism (CD) and 1 H NMR studies find a non-linear relationship between the chiroptical properties and the ratio of chiral phenylethylammonium (PEA) to achiral octylamine (OA) ligands on the NPL surface. We use density functional theory (DFT) computations and a chiral imprinted particle-in-a-box model to rationalize the experimentally observed CD spectra and find that the saturation point of the induced chirality depends on the size of the perovskite exciton relative to the size of the ligands. Temperature-dependent CD and 1 H NMR studies, combined with DFT analysis, show that both the CD intensity and sign depend strongly on the structure and orientation of the ligands. Furthermore, this work reveals the complex nature of chiral imprinting in perovskite nanostructures and establishes a simple physical model for ligand-induced chiral imprinting to guide the further development of chiral materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗
DOE OSTI2020

Forced Patterns of Sea Level Rise in the Community Earth System Model Large Ensemble From 1920 to 2100

To provide context for observed sea level rise, the forced response (FR) in dynamic sea level (DSL) during the 20th and 21st centuries is examined in the Community Earth System Model Large Ensemble (LE). After accounting for simulation drift, which in the LE is sizable, the DSL FR is found to be complex, both in space and time. Its evolving character is suggested to arise from both the diversity and transient evolution of climate forcing agents and the slow adjustment timescales of the intermediate and deep oceans. Nonetheless, various intervals of spatially coherent change simulated for the recent past and near future are identified, and their characteristics and associated driving mechanisms are identified and discussed. The mid- to late-20th century DSL FR is characterized by an hemispherically asymmetric pattern of change, with depressed rates of rise in the northern oceans. There is also a dipole of change in the Southern Ocean caused by changes in near-surface zonal winds. Through the late 20th and early 21st centuries, a different pattern of rise emerges with elevated rates in the tropics and depressed rates at high latitudes. Zonal and interbasin variations characterize both intervals and involve the pattern and depth of anomalous ocean heat content storage and spatial contrasts in the expansion coefficient tied mainly to base state temperature, with greater rates of rise in warm regions per unit warming. Finally, the relative roles of surface flux and ocean convergence anomalies are examined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Pyroelectric Heat Detection for Calibrated Measurement of Atomic Layer Deposition Reaction Heat

Atomic layer deposition (ALD) precursors and processes have been identified that enable the deposition of materials that span the periodic table. However, the detailed mechanisms, kinetics, thermodynamics, and complexities of even the most well-studied ALD half-reactions remain insufficiently resolved experimentally to test present computational hypotheses. In this work, a pyroelectric calorimeter is designed, implemented, calibrated, and electrically modeled to enable quantitative ALD reaction heat detection. The operation and response of three ALD calorimeter designs were evaluated for use as absolute ALD half-reaction heat detectors. The most effective design was calibrated in situ before being utilized to measure the heat generated by the reaction of trimethylaluminum with a hydroxylated alumina surface to produce 96 μJ/cm 2 over a reaction time of ~30 ms. With thermal and temporal resolutions down to 0.1 μJ/cm 2 and 50 ns, respectively, this sensitive and ultrafast probe is poised to improve our fundamental understanding of numerous ALD reaction mechanisms through quantitative comparison with first-principles computation predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Correlated Response of Protein Side-Chain Fluctuations and Conformational Entropy to Ligand Binding

The heterogeneous fast side-chain dynamics of proteins plays crucial roles in molecular recognition and binding. Site-specific NMR experiments quantify these motions by measuring the model-free order parameter (O axis 2 ) on a scale of 0 (most flexible) to 1 (least flexible) for each methyl-containing residue of proteins. In this work, we have examined ligand-induced variations in the fast side-chain dynamics and conformational entropy of calmodulin (CaM) using five different CaM–peptide complexes. O axis 2 of CaM in the ligand-free (O axis,U 2 ) and ligand-bound (O axis,B 2 ) states are calculated from molecular dynamics trajectories and conformational energy surfaces obtained using the adaptive biasing force (ABF) method. ΔO axis 2 = O axis,B 2 – O axis,U 2 follows a Gaussian-like unimodal distribution whose second moment is a potential indicator of the binding affinity of these complexes. The probability for the binding-induced O axis,U 2 → O axis,B 2 transition decreases with increasing magnitude of ΔO axis 2 , indicating that large flexibility changes are improbable for side chains of CaM after ligand binding. A linear correlation established between ΔOaxis2 and the conformational entropy change of the protein makes possible the determination of the conformational entropy of binding of protein–ligand complexes. The results not only underscore the functional importance of fast side-chain fluctuations but also highlight key motional and thermodynamic correlates of protein–ligand binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2020

Graphene-extracted membrane lipids facilitate the activation of integrin α v β 8

Despite the remarkable electrochemical properties of graphene, strong van der Waals attraction between graphene and biomolecules often causes cytotoxicity, which hinders its applications in the biomedical field. Unfortunately, surface passivation of graphene might stimulate undesired immune response as the nanomaterial triggers cytokine production through membrane receptor activation. In this work, we use all-atom Molecular Dynamics (MD) simulations to unravel the underlying mechanism of graphene-induced inside-out activation of integrin αvβ8, a prominent membrane receptor expressed in immune cells. We model the transmembrane (TM) domains of integrin αvβ8 in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer and observe the structural changes in the integrin–membrane complex when interacting with a graphene nanosheet across the membrane. We find that the β8 TM domain interacts with the graphene nanosheet directly or indirectly through extracted lipids, facilitating the pulling of a β8 subunit away from an αv subunit and thus leading to the disruption of the TM domain association by breaking the hydrophobic cluster in the cytoplasmic domains of the αv and β8 subunits. Alanine substitution of two conserved phenylalanine residues on the αv subunit at this hydrophobic cluster further reveals the importance of a stable T-shaped structure in retaining integrin in its inactive state. Our results agree with previous studies on the interactions between other integrin subtypes and their endogenous activators, suggesting an intriguing role that the graphene nanosheet may play in the integrin-related signal transduction during its interaction with the membrane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H–L , and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H–L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol –1 MAE) for robust VHTS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Impacts of phase state and water content on secondary organic aerosol formation and partitioning (Final Technical Report)

Secondary organic aerosols (SOA) account for a major fraction of particulate matter in the atmosphere, affecting climate, air quality, and public health. SOA formation and evolution are highly complex processes involving both chemical reactions and transport of molecules in air, at particle surfaces, and within particles. The state of the matter, or “phase state,” comprising SOA can vary from a liquid, over an amorphous semi-solid, to a glassy solid, depending on chemical composition, water content, relative humidity (RH), and temperature. The occurrence of glassy and amorphous semi-solid states can pose limitations on the rate of transport of molecules, affecting gas-particle interactions and challenging the treatment of SOA in atmospheric models. The objective of this project was to improve fundamental understanding of the interplay of the phase state of particles and water content on the evolution of SOA formation. We have developed a method to estimate the glass transition temperature (T g ) of organic compounds to predict the phase state and viscosity of SOA. This method has been applied to high resolution mass spectrometry data of various types of SOA, achieving a good agreement with viscosity measurements. In addition, the viscosity estimation method was implemented into a regional model CMAQ to simulate diel and seasonal variations of SOA phase state over the U.S. Applying kinetic multilayer modeling, we analyzed laboratory experiments and ARM field measurements (e.g., HI-SCALE) on kinetic limitations of amine uptake. We have also conducted numerical simulations to estimate equilibration timescales of SOA partitioning. Overall, we have evaluated the impacts of phase state and water content on SOA lifecycle that should contribute to reducing the uncertainty of SOA representation in regional climate and air quality predictions. The project has yielded 17 publications, in which DE-SC0018349 is explicitly acknowledged. Please see the list of publications as below, followed by summary of research activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Zentropy Theory for Transformative Functionalities of Magnetic and Superconducting Materials

The proposed research developed the zentropy theory through applications to complex magnetic materials and superconductors under the hypothesis that the emergent properties of complex magnetic materials and superconductors can be predicted by statistical mechanics of ergodic microstates with their partition functions computed from DFT-predicted free energies. The key objective is to develop approaches to systematically determine the types and number of microstates and the supercell size in DFT-based calculations through convergency of macroscopic functionalities, with the incorporation of our mixed-space approach accounting for the interactions between periodic supercells. In addition to use scientific intuitions to guide the design of important microstates, the key innovation of the proposed research is to integrate the domain knowledge and the material-property-descriptor database (MPDD) with 4 million microstates, which is supported by our deep neural network machine learning models (SIPFENN: structure-informed prediction of formation energy using neural networks) and integrated with our high throughput DFT Tool Kit (DFTTK). For complex magnetic materials, one of the objectives is to develop approaches to calculate short-range ordering from the statistical distribution of each microstate. For superconductors, the divergency of quasiparticle effective mass at a quantum critical point will be investigated, and the superconducting and non-superconducting microstates will be delineated through analysis of electronic band structure, density of states, charge density, and Fermi surface.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Neural network reconstruction of the DIII-D tokamak plasma boundary using a reduced set of diagnostics

This study investigates the feasibility of reconstructing the last closed flux surface in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared: one trained solely on coil currents and another incorporating coil currents, plasma current and loop voltage. The model trained exclusively on coil currents achieved a mean point displacement of $0.04$ m on a held-out test set, while the inclusion of plasma current and loop voltage reduced the error to $0.03$ m. This comparison highlights the trade-offs between input feature complexity and reconstruction accuracy, demonstrating the potential of machine learning algorithms to perform effectively in data-limited environments, such as those expected in fusion power plants due to diagnostic constraints imposed by the presence of blankets and shielding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Metal Independent Correlations for Site-specific Binding Energies of Relevant Catalytic Intermediates

Establishing energy correlations among different metals can accelerate the discovery of efficient and cost-effective catalysts for complex reactions. Using a recently introduced coordination-based model, we can predict site specific metal binding energies (∆EM) that can be used as a descriptor for chemical reactions. In this study, we have examined a range of metals including Ag, Au, Co, Cu, Ir, Ni, Os, Pd, Pt, Rh, and Ru and found linear correlations between predicted ∆EM and adsorption energies of CH and O (∆ECH and ∆EO) at various coordination environment for all the considered metals. Interestingly, all the metals correlate with one another under specific surface site coordination, indicating that different metals are interrelated in a particular coordination environment. Furthermore, we have tested and verified for PtPd and PtIr-based alloys that they follow a similar behavior. Moreover, we have expanded the metal space by taking some early transition metals along with few s-block metals and shown a cyclic behavior of the adsorbate binding energy (∆EA) versus ∆EM. Therefore, ∆ECH and ∆EO can be efficiently interpolated between metals, alloys and intermetallics based on information related to one metal only. This simplifies the process of screening new metal catalyst formulations and their reaction energies.

Density function theory↗
DOE OSTI2021

Analysis of the Reactive CO 2 Surface Flux in Electrocatalytic Aqueous Flow Reactors

We study how the mass transfer of a chemical species in shear flow is suppressed by the production of a second species that partially reacts away the first species. The second species is produced at the surface with the first species as a reactant. Our work is directly motivated by electrochemical CO 2 reduction in aqueous flow reactors, where OH– molecules generated by the CO evolution reaction react away CO 2 molecules, ultimately inhibiting the mass transfer of CO 2 to the cathode surface. We derive a simple approximation of the Sherwood number, a dimensionless measure of the mass flux into the surface, as a function of the Péclet number, surface Damköhler number, and bulk Damköhler number, and we compare our approximation to numerical solutions of the governing equations. We find that in the diffusion-limited regime, the Sherwood number is well described by the classical Graetz-Lévêque result reduced by a reaction factor due to the competing bulk reaction; with the stoichiometry relevant to electrochemical CO 2 reduction, this reaction factor is 1/2. While the model problem we solve provides insight into how OH– production affects CO 2 mass transfer, experimental systems often involve more complex chemistry. We thus also show how a common buffered electrolyte, KHCO 3 , affects our results.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Modified Energy Span Analysis of Catalytic Parallel Pathways and Selectivity

Mechanistic modeling provides vital insights into catalytic reactions. To analyze complex reaction networks with parallel pathways, we leverage the graph theory approach of the Energy Span Model (ESM) to develop a modified energy span analysis (MESA). A new method of cycle plots is proposed to perform reaction pathways analysis visually. We demonstrate this method on two published models: one describing carbon monoxide oxidation and the other simulating ethylene conversion to propanal via hydroformylation or ethane via hydrogenation. Fundamental insights explain kinetic observables, such as a reactant’s negative reaction order. General principles are revealed, such as rate-determining surface species being outside the primary reaction flux cycle and pathway selectivity being a purely kinetic property when reaction conditions are not near equilibrium. Lastly, we demonstrate MESA’s consistency with published microkinetic modeling results, highlighting this technique’s extension of the ESM to heterogeneous catalysts using collision theory to describe adsorption steps and concentration effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Anisotropic domains and antiferrodistortive-transition controlled magnetization in epitaxial manganite films on vicinal SrTiO 3 substrates

We studied the microstructural evolution and magnetism of ferroelastic La 0.9 Sr 0.1 MnO 3 (LSMO) epitaxial thin films grown on SrTiO 3 (001) substrates with different miscut angles. The substrate miscut angle plays a critical role in controlling the in-plane magnetic anisotropy. The microscopic origin of such magnetic anisotropy is attributed to the formation of anisotropic stripe domains along the surface step terraces. The magnetization in the LSMO films was found to be selectively modulated by the antiferrodistortive phase transition of the SrTiO 3 substrate. This phenomenon has been qualitatively explained by a strain modified Stoner–Wohlfarth model. We conclude that the magnetization modulation by the SrTiO 3 phase transition depends on h, the ratio of applied magnetic field to the saturation field. Such modulation is only visible with h < 1. The established domain microstructure–anisotropy–magnetism correlation in manganite films can be applied to a variety of complex oxide thin films on vicinal substrates.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Asymmetric drift in MaNGA: mass and radially dependent stratification rates in galaxy discs

ABSTRACT We measure the age–velocity relationship from the lag between ionized gas and stellar tangential speeds in ∼500 nearby disc galaxies from MaNGA in Sloan Digital Sky Survey IV (SDSS-IV). Selected galaxies are kinematically axisymmetric. Velocity lags are asymmetric drift, seen in the Milky Way’s (MW) solar neighbourhood and other Local Group galaxies; their amplitude correlates with stellar population age. The trend is qualitatively consistent in rate ($\dot{\sigma }$) with a simple power-law model where σ ∝ tb that explains the dynamical phase-space stratification in the solar neighbourhood. The model is generalized based on disc dynamical times to other radii and other galaxies. We find in-plane radial stratification parameters σ0,r (dispersion of the youngest populations) in the range of 10–40 km s−1 and 0.2 < br < 0.5 for MaNGA galaxies. Overall, brincreases with galaxy mass, decreases with radius for galaxies above 10.4 dex (M⊙) in stellar mass, but is ∼constant with radius at lower mass. The measurement scatter indicates the stratification model is too simple to capture the complexity seen in the data, unsurprising given the many possible astrophysical processes that may lead to stellar population dynamical stratification. None the less, the data show dynamical stratification is broadly present in the galaxy population, with systematic trends in mass and density. The amplitude of the asymmetric drift signal is larger for the MaNGA sample than the MW, and better represented in the mean by what is observed in the discs of M31 and M33. Either typical discs have higher surface-density or, more likely, are dynamically hotter (hence thicker) than the MW.

Bershady, Matthew A. (ORCID:0000000231314374)↗