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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

MLIPs↗
DOE OSTI2025

Bottom-Up Simulation, Reconstruction, and Quantification of Macromolecule Sequences from Experimental Polymerizations

Motivated by the canonical sequence–structure–function paradigm, tools to characterize chemical patterning in natural biomacromolecules, from proteins to nucleic acids, have grown exponentially in recent years. However, analogous strategies for synthetic macromolecules remain in nascent stages, complicated by sequence polydispersity and analytical limitations. To address this, we have developed a comprehensive and open-source Python package, PRISM (polymer rate insights and sequence modeling), an end-to-end workflow that provides a path from experimental kinetics measurements to quantitative and qualitative metrics for describing chemical patterning in stochastic polymers. First, a numerical integration strategy was constructed to simulate and fit experimental data from reversible addition–fragmentation chain transfer (RAFT) polymerization kinetics, enabling the facile estimation of relevant reactivity ratios. These ratios were then used in a mechanism-specific stochastic kinetic simulation strategy to simulate sequence ensembles corresponding to model systems spanning experimental copolymers, classes of statistical polymers (e.g., alternating, block, and gradient), and multiblock copolymers. Lastly, inspired by sequence homology metrics from bioinformatics, we introduce visualization strategies and quantitative metrics to facilitate comparisons of different sequence ensembles. As the sequence–structure–function paradigm becomes increasingly central in de novo design of synthetic macromolecules, this toolkit provides a first step toward accurate and representative sequence description and featurization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Experimental Verification of Ir 5d Orbital States and Atomic Structures in Highly Active Amorphous Iridium Oxide Catalysts

In iridium oxide catalysts, the electronic states whose energies are in the range of energetics and charge transfer kinetics of the oxygen evolution reaction (OER) originate from the Ir 5d orbital states. However, the understanding of the atomic structures and orbital states underlying catalytic reactivity in amorphous iridium oxide oxygen evolving catalysts (Ir-OECs) is incomplete compared to that of crystalline oxides, owing to a lack of direct experimental verification. Here, we present experimental approaches using resonant inelastic X-ray scattering (RIXS) to directly access Ir 5d orbital excitations at the Ir L 3 edge and atomic pair distribution function (PDF) measurements to characterize electronic and coordination structures at the atomic scale. The so-called iridium blue layer (IrBL) and IrO x were formed from the organometallic precursor complex [Cp*Ir(H 2 O) 3 ]SO 4 and the inorganic precursor IrCl 3 , respectively. Ex situ IrBL and IrO x films for RIXS and PDF measurements were prepared by conditioning electrodeposited films at a low voltage. The incident energy RIXS profile of IrO x exhibited extra weak resonantly enhanced excitation below 2 eV energy loss. The feature was clearly different from a single high-energy excitation above 3 eV of IrBL related to the interband transition between π- and σ-antibonding states. The atomic structure refinement based on PDF measurements revealed the atomic structure domains to have edge- and corner-shared IrO 6 octahedra with trigonal-type distortion. Furthermore, density functional theory calculations guided by the refined atomic structures shed light on the electronic structure corresponding to experimental results, including insulating and metallic phases in ex situ IrBL and IrO x films, respectively. Our study establishes different Ir 5d orbital states and atomic structures in two amorphous Ir oxide OER catalysts in their reduction states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Reducing Reaction Temperature, Steam Requirements, and Coke Formation During Methane Steam Reforming Using Electric Fields: A Microkinetic Modeling and Experimental Study

In this study, we approach several common problems with the Ni-catalyzed methane steam reformation reaction (MSR) using a two-pronged approach combining density functional theory (DFT) calculations with experimental work. Specifically, we look at the deactivation of a Ni catalyst due to coke formation, its high operating temperature requirements, and the high steam-to-methane (H 2 O/CH 4 ) ratio needed for proper MSR operation. A DFT-based microkinetic model was developed in the presence and absence of electric fields and the results were compared with experimental results. The microkinetic model shows that under various electric fields, the most favorable MSR mechanism changed slightly. It also shows that the presence of a positive electric field decreases the surface coverage of carbon, increases the water coverage, accelerates the rate-limiting step of the C-H bond cleavage in methane, and increases the desorption rates of the syngas product (CO + H 2 ) during MSR. Consequently, for a given methane conversion, a positive electric field allows for significantly lower H 2 O/CH 4 ratio and operating temperatures as compared to systems without an electric field. These findings correspond well with experimental tests under a variety of operating conditions. In addition, improvement in the catalytic activity due to the presence of a positive electric field remained significant even at industrially relevant applied pressures improving the hydrogen yield greatly. Overall, we find that an applied electric field can play a significant role in improving the catalytic activity of heterogeneous reactions. Here, this information can guide the design of heterogeneous reactions in the presence of an electric field. By utilizing the electric field generated by various renewable energy sources, electric field assisted heterogeneous reactions can open up a paradigm in future energy research.

03 NATURAL GAS↗
DOE OSTI2017

Consideration of the Aluminum Distribution in Zeolites in Theoretical and Experimental Catalysis Research

Research efforts in zeolite catalysis have become increasingly cognizant of the diversity in structure and function resulting from the distribution of framework aluminum atoms, through emerging reports of catalytic phenomena that fall outside those recognizable as the shape-selective ones emblematic of its earlier history. Molecular-level descriptions of how active-site distributions affect catalysis are an aspirational goal articulated frequently in experimental and theoretical research, yet they are limited by imprecise knowledge of the structure and behavior of the zeolite materials under interrogation. In experimental research, higher precision can result from more reliable control of structure during synthesis and from more robust and quantitative structural and kinetic characterization probes. In theoretical research, construction of models with specific aluminum locations and distributions seldom capture the heterogeneity inherent to the materials studied by experiment. In this Perspective, we discuss research findings that appropriately frame the challenges in developing more predictive synthesis-structure-function relations for zeolites, highlighting studies on ZSM-5 zeolites that are among the most structurally complex molecular sieve frameworks and the most widely studied because of their versatility in commercial applications. We discuss research directions to address these challenges and forge stronger connections between zeolite structure, composition, and active sites to catalytic function. Such connections promise to aid in bridging the findings of theoretical and experimental catalysis research, and transforming zeolite active site design from an empirical endeavor into a more predictable science founded on validated models.

09 BIOMASS FUELS↗
DOE OSTI2021

Experimental Protocols for Studying Organic Non-aqueous Redox Flow Batteries

We report that Redox flow batteries (RFBs) are promising devices for grid-scale energy storage due to the decoupling of power and energy, which can be independently scaled by the electrode area and storage tank size, respectively. To date, only aqueous RFBs, such as the vanadium RFB, have been implemented commercially. Nevertheless, the limited energy densities and high-cost materials may preclude their wider market penetration. Organic non-aqueous redox-flow batteries (O-NRFBs), which utilize redox-active organic molecules (ROMs), have been offered as an attractive alternative. Many possible advantages include the use of earth-abundant elements (C, H, N, O, S, F), wherein the ROMs can be prepared from low-cost and sustainable materials. Additionally, a large variety of electroactive moieties are accessible as building blocks, providing a synthetic platform to tune the properties of ROMs through rational design. As a consequence, the development of novel ROMs has attracted researchers with diverse backgrounds, prompting remarkable innovations in the past decade. However, consistency in experimental protocols (e.g., electrochemical methods, cycling stability, experimental conditions) has not coincided with this uptick in research, leading to sometimes ambiguous and incomparable results. This is further convoluted by the complexity innate to battery development, such as cell design, detection, and characterization of reactions and active components. The O-NRFB application imposes stringent requirements on the physical or/and chemical processes involved in electrochemical cycling. Yet such considerations are often overlooked. Further, while quantum mechanical calculations provide a convenient tool for evaluating and selecting ROM candidates, this simulation is not always performed. Thus, in this Energy Focus, we detail a means to standardize experimental protocols for studies of O-NRFBs and suggest practices to facilitate fundamental understanding and development of ROMs.

25 ENERGY STORAGE↗
DOE OSTI2025

Regional-Scale Modeling Parameterizations for Secondary Organic Aerosol Formation from Isoprene Epoxydiols: Experimentally Based Evaluation and Optimization

Isoprene is an abundant volatile organic compound emitted from broadleaf forests. Under low nitric oxide concentrations, isoprene is photochemically oxidized to form gas-phase isoprene epoxydiols (IEPOX). In the presence of acidified sulfate aerosols, IEPOX enhances the secondary organic aerosol (SOA) formation. Predictions of IEPOX-SOA in regional-scale models, e.g., the Community Multiscale Air Quality Model (CMAQ), are uncertain due to homogeneous aerosol assumptions, underpredictions of water uptake (hygroscopicity), and aerosol surface area. Here, we used experimental measurements of IEPOX-SOA tracers, 2-methyltetrols (2-MT) and 2-methyltetrol sulfates (2-MTS), formed at initial IEPOX-to-inorganic sulfate ratios ranging from 1–10.5, at ∼50% relative humidity to constrain key IEPOX-SOA parameters: phase separation, organic shell diffusivity (D org ), acidity, hygroscopic growth, mass accommodation, and kinetics. The base CMAQ parametrization overpredicted experimental IEPOX-SOA with an average normalized mean bias (NMB average ) of 1.63. CMAQ with phase separation underpredicted IEPOX-SOA (NMB average = −0.71). Using the phase-separated model, CMAQ model performance was optimized (NMB average = 0.077) with an increased D org = 2 × 10 –16 m 2 s –1 and increased rate constants (k 2-MT = 1 × 10 –3 M 2 s –1 , k 2-MTS = 8.83 × 10 –3 M 2 s –1 ). The optimized model explicitly accounted for hygroscopic growth by utilizing experimentally derived growth rates, improving aerosol surface area predictions. Our model highlights the importance of the aerosol mixing state (homogeneous versus phase-separated), aerosol size dynamics, and hygroscopic growth in modeling heterogeneous reactive uptake of IEPOX.

aerosols↗
DOE OSTI2020

Co(salen)-Catalyzed Oxidation of Lignin Models to Form Benzoquinones and Benzaldehydes: A Computational and Experimental Study

Lignin is a highly abundant polyphenolic polymer that imparts mechanical strength to plant biomass. Transition-metal complexes can catalyze lignin oxidation to produce value-added products, but low catalytic efficiency has hampered their use in industry. Identifying the chemical and structural factors that govern catalytic activity is a prerequisite to rational design of catalysts with improved activity. In this study, we combine computational and experimental approaches to investigate the mechanism of Co(salen)-catalyzed oxidation of the monomeric lignin models syringyl (S), vanillyl (G), and 4-hydroxybenzyl alcohol (H) to produce benzoquinone and benzaldehyde products. Experimentally, S oxidation to form dimethoxybenzoquinone proceeded efficiently with a Co(salen) catalyst coordinated by a pyridine ligand, but G and H did not undergo oxidation. Density functional theory calculations reveal that catalyst regeneration is energetically unfavorable in the presence of H, which prevents oxidation. In contrast, S readily facilitates catalyst regeneration. Formation of methoxybenzoquinone from G was achieved experimentally by adding bulky, noncoordinating bases. These findings provide a fundamental baseline for enhancing the activity of Co-Schiff base catalysts toward lignin-like molecules by adding sterically hindered nitrogenous bases or potentially by including a cocatalyst that promotes catalyst regeneration.

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

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals

We report that experimentally refined crystal structures for metal-organic frameworks (MOFs) often include solvent molecules and partially occupied or disordered atoms. This creates a major impediment to applying high-throughput computational screening to MOFs. To address this problem, we have constructed a database of MOF structures that are derived from experimental data but are immediately suitable for molecular simulations. The Computation-Ready, Experimental (CoRE) MOF database contains over 4,700 porous structures with publically available atomic coordinates. Important physical and chemical properties including the surface area and pore dimensions are reported for these structures. To demonstrate the utility of the database, we performed grand canonical Monte Carlo simulations of methane adsorption on all structures in the CoRE MOF database. Lastly, we investigated the structural properties of the CoRE MOFs that govern methane storage capacity and found that these relationships agree well with those derived recently from a large database of hypothetical MOFs.

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

Pd(II) and Rh(I) Catalytic Precursors for Arene Alkenylation: Comparative Evaluation of Reactivity and Mechanism Based on Experimental and Computational Studies

We combine experimental and computational investigations to compare and understand catalytic arene alkenylation using the Pd(II) and Rh(I) precursors Pd(OAc) 2 and [(η 2 -C 2 H 4 ) 2 Rh(µ-OAc)] 2 with arene, olefin and Cu(II) carboxylate at elevated temperature (> 120 °C). Under specific conditions, previous computational and experimental efforts have identified heterotrimetallic cyclic PdCu 2 (η 2 -C 2 H 4 ) 3 (μ-OPiv) 6 and [(η 2 -C 2 H 4 ) 2 Rh(µ-OPiv) 2 ] 2 (µ-Cu) (OPiv = pivalate) species as likely active catalysts for these processes. Further studies of catalyst speciation suggest a complicated equilibrium between Cu(II)-containing complexes containing one Rh or Pd atom with complexes containing two Rh or Pd atoms. At 120 °C, Rh catalysis produces styrene > 20-fold more rapidly than Pd. Also, at 120 °C, Rh is ~98% selective for styrene formation while Pd is ~82% selective. Furthermore, our studies indicate that Pd catalysis has a higher predilection toward olefin functionalization to form undesired vinyl ester, while Rh catalysis is more selective for arene/olefin coupling. However, at elevated temperatures, Pd converts vinyl ester and arene to vinyl arene, which is proposed to occur through low valent Pd(0) clusters that are formed in situ. Regardless of arene functionality, the regioselectivity for alkenylation of mono-substituted arenes with the Rh catalyst gives an approximate 2:1 meta:para ratio with minimal ortho C–H activation. In contrast, Pd selectivity is significantly influenced by arene electronics with electron-rich arenes giving an approximate 1:2:2 ortho:meta:para ratio while the electron deficient (α,α,α)-trifluorotoluene gives a 3:1 meta:para ratio with minimal ortho functionalization. Kinetic intermolecular arene ethenylation competition experiments find that Rh reacts most rapidly with benzene, and the rate of mono-substituted arene alkenylation does not correlate with arene electronics. In contrast, with Pd catalysis, electron-rich arenes react more rapidly than benzene while electron-deficient arenes react less rapidly than benzene. These experimental findings, in combination with computational results, are consistent with the arene C–H activation step for Pd catalysis involving significant η 1 -arenium character due to Pd-mediated electrophilic aromatic substitution character. In contrast, the mechanism for Rh catalysis is not sensitive to arene substituent electronics, which we propose indicates less electrophilic aromatic substitution character for the Rh-mediated arene C–H activation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Experimental Quantification of Spin–Phonon Coupling in Molecular Qubits Using Inelastic Neutron Scattering

Electronic spin superposition states enable nanoscale sensing through their sensitivity to the local environment, yet their sensitivity to vibrational motion also limits their coherence times. In molecular spin systems, chemical tunability and atomicscale resolution are accompanied by a dense, thermally accessible phonon spectrum that introduces efficient spin relaxation pathways. Despite extensive theoretical work, there is little experimental consensus on which vibrational energies dominate spin relaxation or how molecular structure controls spin−phonon coupling (SPC). We present a fully experimental method to quantify SPC coefficients by combining temperature-dependent vibrational spectra from inelastic neutron scattering with spin relaxation rates measured by electron paramagnetic resonance. We apply this framework to two model S = 1/2 systems, copper(II) phthalocyanine (CuPc) and copper(II) octaethylporphyrin (CuOEP). Two distinct relaxation regimes emerge: below 40 K, weakly coupled lattice modes below 50 cm −1 dominate, whereas above 40 K, optical phonons above ∼185 cm −1 become thermally populated and drive relaxation with SPC coefficients nearly 3 orders of magnitude larger. Structural distortions in CuOEP that break planar symmetry soften the crystal lattice and enhance anharmonic scattering but also raise the energy of stretching modes at the molecular core where the spins reside. This redistributes vibrational energy toward the molecular periphery and out of plane, ultimately reducing SPC relative to CuPc and enabling room-temperature spin coherence in CuOEP. Although our method does not provide mode-specific SPC coefficients, it quantifies contributions from distinct spectral regions and establishes a broadly applicable, fully experimental link between crystal structure, lattice dynamics, and spin relaxation.

Lohaus, Stefan H. [California Institute of Technol↗
DOE OSTI2018

Experimental Testing Scale Considerations for the Investigation of Bare‐Soil Evaporation Dynamics in the Presence of Sustained Above‐Ground Airflow

Abstract At the fundamental process level, many of the concepts that form the foundation of our understanding of bare‐soil evaporation dynamics have advanced little since their initial formation. This investigation explores experimental scaling issues that should be considered during the study of bare‐soil evaporation dynamics under conditions of sustained above‐ground airflow. Results are presented from a series of large‐scale laboratory experiments conducted for various soil conditions (i.e., heterogeneity and surface roughness). Fundamental experimental research of this nature is not feasible in the field or small laboratory columns that are limited by system control and scale. All experimentation was therefore conducted in a test facility that couples a climate‐controlled, low‐speed wind tunnel with a 7.15‐m‐long soil tank—allowing for control over soil properties and initial and boundary conditions. Measured flow phenomena supported, and agreed well with, existing wind tunnel and field study literature. These data were in turn, used to explain observed evaporative water loss and soil moisture distribution spatiotemporal trends and patterns. Results demonstrated that relatively large length scales are required for the impacts of atmospheric feedbacks on the subsurface hydrodynamics to manifest themselves and become quantifiable. Airflow had the greatest impact in the case of a flat homogeneous soil; the strength of this feedback was significantly weaker in the presence of soil heterogeneities and surface undulations that were dominated by other transport phenomena. Comparison of these results with those of past studies furthermore cautions against the use of column scale data to make generalizations about bare‐soil evaporation dynamic upscaling.

Trautz, Andrew C.↗
DOE OSTI2019

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Abstract The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗
DOE OSTI2020

Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. Here we introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, which calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R h ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

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

Theoretical and experimental quantification of Suzuki segregation enthalpy and strengthening mechanisms in a binary alloy

Solute segregation to planar defects in metallic alloys has been shown to drastically alter mechanical properties. While various works using first-principles and thermodynamic calculations have studied the fundamental driving forces for solute segregation via the Suzuki criterion, planar defect energy, or a comparison of energies of the HCP-like phase and FCC matrix, a quantitative experimental and computational comparison of equilibrium composition and segregation enthalpies has not yet been reported. In this work, we predict the equilibrium composition and segregation enthalpy to intrinsic stacking faults in a Ni-60Co (at.%) alloy and compare the results to two independent experimental methods. We observed that Co segregates to the innermost two planes of the intrinsic stacking fault, and we found that the experimental segregation enrichment, measured from transmission electron microscopy energy dispersive X-ray spectroscopy, of the faults is 6.8 at.% Co, which is 2.2 at.% less than the predicted value at the same temperature. We also find that the segregation enthalpy measured from the composition profile is −21.1 ± 6.4 meV/atom and separately from differential scanning calorimetry segregation enthalpy is −33.2 meV/atom, whereas the predicted enthalpy is −31 ± 1 meV/atom. Based on these results, we determine that segregation occurs very rapidly, within 8 min at temperatures as low as 36% of the homologous solidus temperature. Furthermore, this analysis provides an overview of the possible dislocation mechanisms responsible for strengthening effects due to solute segregation, and concludes that changes in room temperature hardness from local phase transformation is likely tied to post-segregation room temperature equilibrium partial separation distance.

Ab initio calculation↗
DOE OSTI2017

A closer look at the physical and optical properties of gold nanostars: an experimental and computational study

A combined experimental and computational study was carried out to design a semi-empirical method to determine the volume, surface area, and extinction coefficients of gold nanostars. The values obtained were confirmed by reconstructing the nanostar 3D topography through high-tilt TEM tomography and introducing the finite elements in COMSOL Multiphysics through which we have also calculated the morphology-dependent extinction coefficient. We have, for the first time, modeled the heat losses of a real, experimentally synthesized nanostar, and found the plasmon resonances to be in excellent agreement with those obtained experimentally. Furthermore, we believe that our approach could substantially improve the applicability of this remarkable nanomaterial.

36 MATERIALS SCIENCE↗