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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2021

Theoretical and experimental study on the O( 3 P) + 2,5-dimethylfuran reaction in the gas phase

In this work we report a joint experimental and computational study on the 2,5-dimethylfuran oxidation reaction in the gas phase initiated by atomic oxygen O( 3 P). The experiments have been performed by using vacuum-ultraviolet synchrotron radiation at the Advanced Light Source (ALS) of the Lawrence Berkeley National Laboratory (LBNL), at a temperature of 550 K and a pressure of 8 Torr. The experimental data were supported by quantum-chemical calculations along with a kinetic model, also taking into account the possible involvement of different magnetic states, performed in the framework of the RRKM theory. Propyne, acetaldehyde, methylglyoxal, dimethylglyoxal, 3-penten-2-one, 2,5-dimethylfuran-3(2 H )-one, and 1,2-diacetyl ethylene have been identified as the main primary products arising under the conditions of the experiment. Our computational model suggests that these species can be formed at the concentration and branching ratio experimentally observed only in the presence of a non-negligible fraction of non-thermalized intermediates.

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

Optimal experimental design using eigenvalue-based criteria with Pyomo.DoE

New developments in automated optimal experimental design within the PSE+ software ecosystem. Advancements in user experience (to reduce the time taken to perform optimal experiment design) and computational capabilities (allowing more diverse experimental design) are shown with an example relevant to critical minerals and materials. Also, a small tutorial on science-based optimal experimental design and novel contributions therein are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

WPEC SG50: Developing an Automatically Readable, Comprehensive and Curated Experimental Nuclear Reaction Database

The Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) Working Party on International Nuclear Data Evaluation Co-operation (WPEC) subgroup (SG) 50 was formed in 2020 to develop an automatically readable, comprehensive and curated experimental nuclear reaction database. This database is called MEDUSAL (Machine-readable Experimental Data User Application & Library), and will draw from EXFOR. The EXFOR database preserves experimental nuclear reaction data true to its original documentation and information from the authors of the data. MEDUSAL will deviate from EXFOR by storing additional information from users of the data for their fields of work (evaluation, model development, validation, etc.). This includes expert judgment on the data sets, identification of data points as outliers, renormalization of the data to the newest monitor reactions, and estimations of missing uncertainty sources. The format for MEDUSAL is being developed to enable easy automatic parsing of large amounts of data. Here, we will summarize the use cases, high-level requirements and first steps towards developing the database MEDUSAL and the API to access it.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Experimental uncertainty quantification using templates of expected measurement uncertainties for fast neutron-induced total, capture, and scattering cross sections

Careful experimental uncertainty quantification (UQ) is key for developing trustworthy evaluated nuclear data. Templates to account for missing or under-reported experimental uncertainties were recently developed by the covariance committee of Cross Section Evaluation Working Group (CSEWG). In this work, we illustrate the practical application and limitations of these templates for selected neutron-induced reactions, including (n, tot), (n, γ), and (n, xn) in the fast energy range, to illustrate their use in data analyses for nuclear data evaluations. We show that while the templates provide consistent framework, proper implementation still requires detailed knowledge of experimental conditions and careful treatment of nonlinear effects in cross section derivation. Case studies highlight how template-assisted UQ improves consistency with previous evaluations such as ENDF/B and reveals open challenges in propagating uncertainties across different energy regimes. The main contribution of this paper is to connect formal template recommendations with their use in practical evaluation workflows, clarifying both their benefits and current limitations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Angular-divergence calculation for Experimental Advanced Superconducting Tokamak neutral beam injection ion source based on spectroscopic measurements

Calorimetric method has been primarily applied for several experimental campaigns to determine the angular divergence of high-current ion source for the neutral beam injection system on the Experimental Advanced Superconducting Tokamak (EAST). A Doppler shift spectroscopy has been developed to provide the secondary measurement of the angular divergence to improve the divergence measurement accuracy and for real-time and non-perturbing measurement. The modified calculation model based on the W7AS neutral beam injectors is adopted to accommodate the slot-type accelerating grids used in the EAST's ion source. Preliminary spectroscopic experimental results are presented comparable to the calorimetrically determined value of theoretical calculation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

Model based multivariable controller for large scale compression stations. Design and experimental validation on the LHC 18KW cryorefrigerator

In this paper, a multivariable model-based non-linear controller for Warm Compression Stations (WCS) is proposed. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to have precise control of every pressure in normal operation or to stabilize and control the cryoplant under high variation of thermal loads (such as a pulsed heat load expected to take place in future fusion reactors such as those expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details how to set the WCS model up to synthesize the Linear Quadratic Optimal feedback gain and how to use it. After preliminary tuning at CEA-Grenoble on the 400W@1.8K helium test facility, the controller has been implemented on a Schneider PLC and fully tested first on the CERN's real-time simulator. Then, it was experimentally validated on a real CERN cryoplant. The efficiency of the solution is experimentally assessed using a reasonable operating scenario of start and stop of compressors and cryogenic turbines. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗