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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 721 records · Page 40
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DOE OSTI2022

Bioinspired oxidation of benzyl alcohol: The role of environment and nuclearity of the catalyst evaluated by multivariate analysis

Inspired by copper-containing enzymes such as galactose oxidase and catechol oxidase, in which distinct coordination environments and nuclearities lead to specific catalytic activities, we summarize here the catalytic properties of dinuclear and mononuclear copper species towards benzyl alcohol oxidation using a multivariate statistical approach. Here, the new dinuclear [Cu 2 (μ-L 1 )(μ-pz)] 2+ (1) is compared against the mononuclear [CuL 2 Cl] (2), where (L 1 ) - and (L 2 ) - are the respective deprotonated forms of 2,6-bis((bis(pyridin-2-ylmethyl)amino)methyl)-4-methylphenol, and 3-((bis(pyridin-2-ylmethyl)amino)methyl)-2-hydroxy-5-methylbenzaldehyde and (pz) - is a pyrazolato bridge. Copper(II) perchlorate (CP) is used as control. The catalytic oxidation of benzyl alcohol is pursued, aiming to assess the role of the ligand environment and nuclearity. The multivariate statistical approach allows for the search of optimal catalytic conditions, considering variables such as catalyst load, hydrogen peroxide load, and time. Species 1, 2 and CP promoted selective production of benzaldehyde at different yields, with only negligible amounts of benzoic acid. Under normalized conditions, 2 showed superior catalytic activity. This species is 3.5-fold more active than the monometallic control CP, and points out to the need for an efficient ligand framework. Species 2 is 6-fold more active than the dinuclear 1, and indicates the favored nuclearity for the conversion of alcohols into aldehydes.

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

Limits of Detection for EXAFS Characterization of Heterogeneous Single-Atom Catalysts

Single-atom catalysts (SACs), consisting of individual metal atoms dispersed on a support, attract attention due to their unique reactivity, efficient use of precious metals, and precise chemical tunability. Characterization of the metal species is crucial to substantiate structure–function relationships. Authors often use—and referees often require—X-ray absorption spectroscopy (XAS) data to prove the absence of clustered metal (or metal oxide) structures after pre-treatment and under in situ or operando conditions. However, there has been no critical assessment of the limitations of XAS in substantiating such conclusive statements, which is particularly important given the potential outsized influence of minority catalyst structures in dictating catalytic activity. Here, in this article, we quantitatively assess the detection limits of XAS to identify metal (or metal oxide) clusters in samples containing predominantly single atoms by modeling the extended X-ray absorption fine structure (EXAFS) of mixtures of structures. We identified that a significant fraction of clusters can coexist with SAC active sites (e.g., ~10% metallic Pt or ~40% oxidized Pt clusters in Pt/CeO 2 SACs), while eluding detection via EXAFS with any statistical significance. To generalize these conclusions, a descriptor-based screening of bulk metal oxides using a continuous Cauchy wavelet transform was proposed that suggests certain materials for which differentiating atomically dispersed metal species and metal oxide clusters would be infeasible by EXAFS (e.g., ReO x ). Based on this analysis, we suggest best practices for the study of SACs using EXAFS and provide recommendations to ensure that conclusions do not outpace the evidence used to support them. In this rapidly expanding research area, rigorous characterization will lead to greater understanding of the behavior of SACs and ultimately improved catalytic materials.

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

sOPTICS: a modified density-based algorithm for identifying galaxy groups/clusters and brightest cluster galaxies

A direct approach to studying the galaxy–halo connection is to analyse groups and clusters of galaxies that trace the underlying dark matter haloes, emphasizing the importance of identifying galaxy clusters and their associated brightest cluster galaxies (BCGs). In this work, we test and propose a robust density-based clustering algorithm that outperforms the traditional Friends-of-Friends (FoF) algorithm in the currently available galaxy group/cluster catalogues. Our new approach is a modified version of the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, which accounts for line-of-sight positional uncertainties due to redshift space distortions by incorporating a scaling factor, and is thereby referred to as sOPTICS. When tested on both a galaxy group catalogue based on semi-analytic galaxy formation simulations and observational data, our algorithm demonstrated robustness to outliers and relative insensitivity to hyperparameter choices. In total, we compared the results of eight clustering algorithms. The proposed density-based clustering method, sOPTICS, outperforms FoF in accurately identifying giant galaxy clusters and their associated BCGs in various environments with higher purity and recovery rate, also successfully recovering 115 BCGs out of 118 reliable BCGs from a large galaxy sample. Furthermore, when applied to an independent observational catalogue without extensive re-tuning, sOPTICS maintains high recovery efficiency, confirming its flexibility and effectiveness for large-scale astronomical surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Towards the Accurate Thermodynamic Characterization of Enzyme Reaction Mechanisms

We employed QM/MM molecular dynamics (MD) simulations to characterize the rate-limiting step of the glycosylation reaction of pancreatic α-amylase with combined DFT/molecular dynamics methods (PBE/def2-SVP : AMBER). Upon careful choice of four starting active site conformations based on thorough reactivity criteria, Gibbs energy profiles were calculated with umbrella sampling simulations within a statistical convergence of 1 -2 kcal · mol -1 . Nevertheless, Gibbs activation barriers and reaction energies still varied from 11.0 to 16.8 kcal · mol -1 and -6.3 to +3.8 kcal · mol -1 depending on the starting conformations, showing that despite significant state-of-the-art QM/MM MD sampling (0.5 ns/profile) the result still depends on the starting structure. In conclusion, the results supported the one step dissociative mechanism of Asp197 glycosylation preceded by an acid-base reaction by the Glu233, which are qualitatively similar to those from multi-PES QM/MM studies, and thus support the use of the latter to determine enzyme reaction mechanisms.

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

The Delplot kinetic method applied to systems with adsorbates: Hydrodeoxygenation of benzofuran on a bimetallic CoPd phosphide catalyst supported on KUSY

The Delplot method provides a means of analyzing conversion and selectivity data to derive a reaction sequence and has been applied so far to gas-phase or liquid-phase species. This study analyses the applicability of the method for catalytic reactions and considers for the first time adsorbed intermediates. The method is applied to a contact time study of the hydrodeoxygenation (HDO) of benzofuran at 0.5 MPa and 350 °C over a catalyst consisting of bimetallic CoPd phosphide supported on a potassium ion-exchanged ultra-stable Y (KUSY) zeolite. Both simple and detailed reaction networks were derived. The simple networks considered only gas-phase species and were modeled by a first-order sequence of steps, whereas the detailed network took into account adsorbed intermediates and was simulated using a rake mechanism. The networks were compared using F-statistics, which accounted for the differences in the number of fitting parameters. The detailed network gave a better fit to the experimental data because it represented a more realistic description of the transformation. A suggested sequence from the Delplot method was consistent with the simple network but not with the detailed network. The lack of applicability of the Delplot analysis to the network with adsorbates was linked to overly large equilibrium constants, a determination supported by Delplot fitting for a model sequence. Further, this study indicated the inapplicability of the Delplot method in determining reaction sequences that involve adsorbed species with large equilibrium constants for formation.

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

Structure of the thermal boundary layer in turbulent channel flows at transcritical conditions

Enhanced fluctuations, steep gradients, and intensified heat transfer are characteristics of wall-bounded turbulence at transcritical conditions. Although such conditions are prevalent in numerous technical applications, the structure of the thermal boundary layer under realistic density gradients and heating conditions remains poorly understood. Specifically, statistical descriptions of the temperature field in such flows are provided inconsistently using existing models. To address this issue, direct numerical simulations are performed by considering fully developed transcritical turbulent channel flow at pressure and temperature conditions that cause density changes of a factor of up to $O(20)$ between the hot and cold walls. As a consequence of the proximity of the Widom line to the hot wall, significant asymmetries are observed when comparing regions near the cold wall and near the hot wall. Previous transformations that attempt to collapse the near-wall mean temperature profiles among different cases to a single curve are examined. By addressing model deficiencies of these transformations, a formulation for an improved mean temperature transformation is proposed, with appropriate considerations for real fluid effects that involve strong variations in thermodynamic quantities. Our proposed transformation is shown to perform well in collapsing the slope of the logarithmic region to a single universal value with reduced uncertainty. Coupled with a predictive framework to estimate the non-universal shift parameter of the logarithmic region using a priori information, our transformation provides an analytic profile to model the near-wall mean temperature. These results thus provide a framework to guide the development of models for wall-bounded transcritical turbulence.

42 ENGINEERING↗
DOE OSTI2020

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

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

A Distinct Atmospheric Mode for California Precipitation

The hydrologic cycle in California is strongly influenced by wet-season (November–April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet-season precipitation variability over both northern and southern California. This mode, named as the “California precipitation mode” (CPM), is statistically distinct from other well-known modes of pressure variability such as the Pacific-North American pattern. In addition to controlling wet-season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well-known modes of pressure variability. The models also reproduce the CPM's strong association with California wet-season precipitation, giving confidence in the models’ dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally oversimulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics.

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

Acceleration of Solvation Free Energy Calculation via Thermodynamic Integration Coupled with Gaussian Process Regression and Improved Gelman–Rubin Convergence Diagnostics

The determination of the solvation free energy of ions and molecules holds profound importance across a spectrum of applications spanning chemistry, biology, energy storage, and the environment. Molecular dynamics simulations are powerful tools for computing this critical parameter. Nevertheless, the accurate and efficient calculation of the solvation free energy becomes a formidable endeavor when dealing with complex systems characterized by potent Coulombic interactions and sluggish ion dynamics and, consequently, slow transition across various metastable states. Here, in the present study, we expose limitations stemming from the conventional calculation of the statistical inefficiency g in the thermodynamic integration method, a factor that can hinder the determination of convergence of the solvation free energy and its associated uncertainty. Instead, we propose a robust scheme based on Gelman–Rubin convergence diagnostics. We leverage this improved estimation of uncertainties to introduce an innovative accelerated thermodynamic integration method based on the Gaussian Process regression. This methodology is applied to the calculation of the solvation free energy of trivalent rare-earth elements immersed in ionic liquids, a scenario in which the aforementioned challenges render standard approaches ineffective. The proposed method proves to be effective in computing solvation free energy in situations where traditional thermodynamic integration methods fall short.

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

Supported Organochromium Ethylene Oligomerization Enabled by Surface Lithiation

Here, in this work, supported organochromium ethylene polymerization catalysts have been tuned to mediate ethylene oligomerization via surface lithiation, which provides a generalizable protocol to control stereoelectronics and redox states of surface organometallic active sites. The homoleptic chromium(IV) alkyl complex Cr(CH 2 SiMe 3 ) 4 was grafted on high-surface-area anatase titania (TiO 2 ) nanoparticles as well as on silica to produce Cr/TiO 2 and Cr/SiO 2 , respectively. Treatment of these materials with excess n-butyllithium led to the reduced chromium complexes Cr/Li x TiO 2 and Cr/Li/SiO 2 , each of which still retains one hydrocarbyl ligand on chromium. A set of heterogeneous complexes were studied by electron paramagnetic resonance and X-ray absorption spectroscopy, which indicate a reduction in the oxidation state of the major chromium species to Cr II upon lithiation. Cr/Li x TiO 2 converts ethylene to hexenes with a high selectivity (>80%), which was persistent over 10 days at 80 °C, achieving >950 turnovers. The exclusive formation of C 4 and C 6 olefins, preferring the trimerization product, without a statistical (Flory–Schulz) distribution is characteristic of the oxidative cyclization oligomerization mechanism rather than the traditional Cossee–Arlman mechanism, whereas Cr/Li/SiO 2 produced a mixture of trimerization and polymerization products, suggesting site heterogeneity in the silica-based material. On the other hand, the unreduced chromium(IV) materials as well as low lithium-containing Cr/Li x TiO 2 (x < 0.16) exclusively produced ultrahigh molecular weight polyethylene, determined by differential scanning calorimetry and gel permeation chromatography analysis, likely formed via a linear-insertion mechanism, with a crossover from the polymerization to oligomerization regime observed at ~16% Li intercalation.

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

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. In this work, we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.

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

Estimating reaction parameters in mechanism-enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach

In order to quantitatively predict nano- as well as other particle-size distributions, one needs to have both a mathematical model and estimates of the parameters that appear in these models. Here, we show how one can use Bayesian inversion to obtain statistical estimates for the parameters that appear in recently derived mechanism-enabled population balance models (ME-PBM) of nanoparticle growth. The Bayesian approach addresses the question of “how well do we know our parameters, along with their uncertainties?.” The results reveal that Bayesian inversion statistical analysis on an example, prototype $\mathrm{lr(0)_n}$ nanoparticle formation system allows one to estimate not just the most likely rate constants and other parameter values, but also their SDs, confidence intervals, and other statistical information. Moreover, knowing the reliability of the mechanistic model's parameters in turn helps inform one about the reliability of the proposed mechanism, as well as the reliability of its predictions. Importantly, the paper can also be seen as a tutorial with the additional goal of achieving a “Gold Standard” Bayesian inversion ME-PBM benchmark that others can use as a control to check their own use of this methodology for other systems of interest throughout nature. Overall, the results provide strong support for the hypothesis that there is substantial value in using a Bayesian inversion methodology for parameter estimation in particle formation systems.

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

An anomalous structure disordering in Zr–Cu–Ag supercooled glass-forming liquids

An anomalous ordering-to-disordering transition in a Zr–Cu–Ag supercooled glass-forming liquid has been revealed by in situ high-energy x-ray diffraction. Upon heating, the supercooled liquid exhibits a structural crossover at T c = 716 K, evident by the change in statistical characteristics of the first sharp diffraction peak. When annealed in the vicinity of T c , the supercooled liquid first relaxes from its initial state to a denser and more ordered state. Subsequently, it becomes disordered at the medium-range scale with a slowing down of densification, as demonstrated by the broadening of the first sharp diffraction peak and a decrease in the peak intensities of the higher atomic shells in the reduced pair distribution function. Finally, a scenario for the evolution of less correlated locally favored clusters in the liquid–liquid phase transition is discussed.

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

Local Atomic Structure in Photo-isomerized Ruthenium Sulfur Dioxide Complexes Revealed by Pair Distribution Function Analysis

SO 2 linkage photoisomerization in crystalline ruthenium-based complexes has demonstrated nanophotonic phenomena such as optical switching and nano-optomechanical transduction. Molecular insights into these materials have been explored largely via the characterization of their photoinduced crystal structures via in situ single-crystal X-ray diffraction, known as photocrystallography. Photoinduced molecular disorder is present, which photocrystallography can model to the extent that it is confined within the periodic boundary of a unit cell. However, non-periodic molecular disorder is suspected to exist as well. In situ total scattering experiments were therefore carried out on finely powdered crystals of four ruthenium-sulfur dioxide complexes. Data were modeled using light-minus-dark' difference pair distribution function analysis, which afforded photoinduced structural changes exclusively. This revealed structural features that were first compared against models of photoinduced crystal structures known a priori from photocrystallography. Furthermore, statistical inference was then employed, which evidenced generally good agreement between the total scattering data and the photocrystallographic models, while revealing real differences that are indicative of a structure with only a short-range order. Overall, our findings demonstrate that in situ light-induced total scattering experiments on finely powdered crystals are able to reveal the photoinduced structure. Overall, the evidence suggests that such structure could include a short-range order as well as photocrystallographic content. Our demonstration experiment offers a pathway to develop studies that capture the short-range order in linkage photoisomers, while we have outlined the procedure for testing the validity of associated structural models.

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

Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study

We know that actinide molten salts represent a class of important materials in nuclear energy. Understanding them at a molecular level is critical to proper and optimal design of relevant technological applications. Yet, owing to the complexity of electronic structure due to the 5f orbitals, computational studies of heavy elements in condensed phases using ab initio potentials to study the structure and dynamics of these elements embedded in molten salts are difficult. This lack of efficient computational protocols makes it difficult to obtain information on properties that require extensive statistical sampling like transport. To tackle this problem, we adopted a machine-learning approach to study ThCl 4 -NaCl and UCl 3 -NaCl binary systems. The machine-learning potential, with the density functional theory accuracy, allows us to obtain long molecular dynamics trajectories (ns) for large systems (10 3 atoms) at a considerably low computing cost, thereby efficiently gaining information about their bonding structures, thermodynamics, and dynamics at a range of temperature. We observed a considerable change in the coordination environments of actinide elements and their characteristic coordination-sphere lifetime. Our study also suggests that actinides in molten salts may not follow well known entropy-scaling laws.

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

Multifidelity uncertainty quantification with models based on dissimilar parameters

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depend on different input uncertain parameters, which drastically reduces the correlation. Existing MF UQ approaches do not adequately address this issue. In this work, we propose a new sampling strategy that exploits a shared space to improve the correlation among models with dissimilar parameterization. We achieve this by transforming the original coordinates onto an auxiliary manifold using the adaptive basis (AB) method (Tipireddy and Ghanem, 2014). The AB method has two main benefits: (1) it provides an effective tool to identify the low-dimensional manifold on which each model can be represented, and (2) it enables easy transformation of polynomial chaos representations from high- to low-dimensional spaces. This latter feature is used to identify a shared manifold among models without requiring additional evaluations. Here we present two algorithmic flavors of the new estimator to cover different analysis scenarios, including those with legacy and non-legacy high-fidelity (HF) data. We provide numerical results for analytical examples, a direct field acoustic test, and a finite element model of a nuclear fuel assembly. For all examples, we compare the proposed strategy against both single-fidelity and MF estimators based on the original model parameterization.

42 ENGINEERING↗
DOE OSTI2025

Deriving the Landauer Principle From the Quantum Shannon Entropy

We derive an expression to determine the equilibrium probability distribution of a quantum state in contact with a noisy thermal environment that formally separates contributions from quantum and classical forms of probabilistic uncertainty. A statistical mechanical interpretation of this probability distribution enables us to derive an expression for the minimum free energy costs for arbitrary (reversible or irreversible) quantum state changes. In conclusion, based on this derivation, we demonstrate that–in contrast to classical systems–the free energy required to erase or reset a qubit depends sensitively on both the fidelity of the target state and on the physical properties of the environment, such as the number of quantum bath states, due primarily to the entropic effects of system-bath entanglement.

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

Interplay of physically different properties leading to challenges in separating lanthanide cations – an ab initio molecular dynamics and experimental study

Lanthanide elements have well-documented similarities in their chemical behavior, which make the valuable trivalent lanthanide cations (Ln 3+ ) particularly difficult to separate from each other in water. Here, we apply ab initio molecular dynamics simulations to compare the free energies (Δ G ads ) associated with the adsorption of lanthanide cations to silica surfaces at a pH condition where SiO – groups are present. The predicted Δ G ads for lutetium (Lu 3+ ) and europium (Eu 3+ ) are similar within statistical uncertainties; this is in qualitative agreement with our batch adsorption measurements on silica. This finding is remarkable because the two cations exhibit hydration free energies (Δ G hyd ) that differ by >2 eV, different hydration numbers, and different hydrolysis behavior far from silica surfaces. We observe that the similarity in Lu 3+ and Eu 3+ Δ G ads is the result of a delicate cancellation between the difference in Eu 3+ and Lu 3+ hydration (Δ G hyd ), and their difference in binding energies to silica. We propose that disrupting this cancellation at the two end points, either for adsorbed or completely desorbed lanthanides ( e.g. , via nanoconfinment or mixed solvents), will lead to effective Ln 3+ separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗