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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Surface Reactivity Analysis of the Crude Oil–Brine–Limestone Interface for a Comprehensive Understanding of the Low-Salinity Waterflooding Mechanism

Low-salinity waterflooding (LSWF) has proven to improve oil recovery in carbonate formations through rock wettability alteration, although the underlying mechanism remains elusive. Multivalent ionic exchange and calcite dissolution have usually been investigated using geochemical analysis in secondary coreflooding. In this work, coreflooding, in tertiary mode, coupled with a surface reactivity analysis approach was employed to investigate the interplay of wettability alteration mechanisms such as mineral dissolution, electrostatic bond attraction, and the effect of pH at in situ conditions. Improved oil recovery (IOR) in tertiary mode observed by coreflooding in Indiana limestone rocks showed an ionic strength dependence, that is, reducing brine ionic strength resulted in an increase in oil recovery. Coreflooding results showed that the seawater and low-salinity brines deprived of Mg 2+ ions resulted in the lowest IOR in tertiary mode, indicating the significance of Mg 2+ on IOR in limestone rocks. Similar results were observed through the contact angle measurement showing the limestone rock wettability state dependence on ionic strength and the effect of Mg2+ ions. Surface reactivity analysis showed an increase in solution pH, Ca 2+ and Mg 2+ ions concentration in the effluent solution from the coreflooding in tertiary mode using low salinity brines (about 40 and 20% increase in the effluent composition for Ca 2+ and Mg 2+ , respectively). These changes in solution composition were used to calculate the in situ oil–brine and rock–brine zeta potential using a validated surface complexation model, showing the changes of zeta potential as brine is injected into limestone rocks. The results show that using seawater-like brine in tertiary mode resulted in no mineral dissolution or ionic exchange. However, improved oil recovery (IOR) using such seawater-like brine was due to wettability alteration caused by reduced electrostatic bond attraction associated with Mg 2+ ions [from 2.6 × 10 –13 (mol/m 2 ) 2 for formation water salinity to 1.5 × 10 –13 (mol/m 2 ) 2 for seawater salinity]. Using low-salinity brines in tertiary mode improved oil recovery by mineral dissolution, resulting in oil desorption and an increase in solution pH. Finally, the increase in solution pH also resulted in reduced electrostatic bond attraction which lead to rock wettability alteration using low-salinity brines.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2020

Enthalpy of Uranium Adsorption onto Hematite

The influence of temperature on the adsorption of metal-ions at the solid-water interface is often overlooked, despite the important role that adsorption plays in metal ion fate and transport in the natural environment where temperatures vary widely. Herein, we examine the temperature-dependent adsorption of uranium, a widespread radioactive contaminant, onto the ubiquitous iron oxide, hematite. The multi-temperature batch adsorption data and surface complexation models indicate that adsorption of uranium, as the hexavalent uranyl (UO 2 2+ ) ion, increases significantly with increasing temperature, with an adsorption enthalpy (ΔH ads ) of +71 kJ mol –1 . We suggest that this endothermic, entropically-driven adsorption behavior is linked to reorganization of the uranyl-ion-hydration and interfacial water structures upon U VI adsorption at the hematite surface. Altogether, this work provides fundamental insight into the thermodynamics driving metal-ion adsorption reactions and provides the specific enthalpy value necessary for improved predictive geochemical modeling of U VI adsorption in the environment.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Synergistic Enhancement of Lead and Selenate Uptake at the Barite (001)–Water Interface

The interactions of heavy metals with minerals influence the mobility and bioavailability of toxic elements in natural aqueous environments. The sorption of heavy metals on covalently bonded minerals is generally well described by surface complexation models (SCMs). However, understanding sorption on sparingly soluble minerals is challenging because of the dynamically evolving chemistry of sorbent surfaces. The interpretation can be even more complicated when multiple metal ions compete for sorption. In the present study, we observed synergistically enhanced uptake of lead and selenate on the barite (001) surface through two sorption mechanisms: lattice incorporation that dominates at lower coverages and two-dimensional monolayer growth that dominates at higher coverages. Further, we also observed a systematic increase in the sorption affinity with increasing co-sorbed ion coverages, different from the assumption of invariant binding constants for individual adsorption processes in classical SCMs. Computational simulations showed thermodynamically favorable co-incorporation of lead and selenate by simultaneously substituting for barium and sulfate in neighboring sites, resulting in the formation of molecular clusters that locally match the net dimension of the substrate lattice. These results emphasize the importance of ion–ion interactions at mineral–water interfaces that control the fate and transport of contaminants in the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Simple Criterion for Feasibility of Heat Integration between Distillation Streams Based on Relative Volatilities

In a multicomponent distillation configuration, there are numerous sources and sinks of heat, and a potential way to reduce the heat duty requirement is to perform heat integration. Unfortunately, an algorithmic search of the optimal heat integration opportunities is intractable when the required temperatures of intermediate streams are computed via complex models. Instead, in this work, we introduce pressure-scaled pseudo relative volatility, a new metric to compare stream temperatures. Here, we justify the use of pseudo relative volatility by proving that this variable is a monotonically increasing function of the liquid fraction in a saturated mixture stream. Using this metric, we derive a shortcut criterion to check the feasibility of various heat integration opportunities, such as thermal coupling via heat transfer (TCH). The advantage of this approach is that it circumvents the need for explicit temperatures and instead relies on composition, component relative volatilities, and pressure—quantities that are readily available in shortcut models for optimization of distillation configurations. Leveraging this fact, we propose a new optimization framework that identifies feasible TCHs that we consider within the formulation while minimizing the total heat duty of a distillation configuration. We demonstrate, on a few examples, that our formulation can identify heat duty efficient configurations, some of which are multieffect configurations. Using this methodology, we discover configurations that are not only simpler than the fully thermally coupled (FTC) configuration but also have a much lower heat duty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Consistency-Enhanced Evolution for Variable Selection Can Identify Key Chemical Information from Spectroscopic Data

In the last few decades, spectroscopic techniques such as near-infrared (NIR) spectroscopy have gained wide applications in several industries, such as the pharmaceutical, agricultural, oil, and gas industries. As a result, various soft sensors have been developed to predict sample properties from spectroscopic readings. Because the spectroscopic readings at different wavelengths, especially at the adjacent wavelengths, are highly correlated, it has been shown that variable selection could significantly improve a soft sensor’s prediction performance while reducing the model complexity. To improve the prediction performance, most variable selection methods focus on identifying the variables (i.e., wavelengths or wavelength segments) that are strongly correlated with the dependent variable. Although many successful applications have been reported, these variable selection methods do have their limitations. Specifically, the selected wavelengths sometimes show little connection to the chemical bounds or functional groups presenting in the sample. In addition, the selected variables can be quite sensitive to the choice of the training samples. In this work, we address these limitations from a different perspective: if a variable selection algorithm can identify the truly relevant input variables, it should consistently identify the same subset of variables regardless of the choice of the training samples. Therefore, we propose a variable selection method that aims to improve the consistency of variable selection resulting from different training samples. Furthermore, the new algorithm is termed consistency-enhanced evolution for variable selection (CEEVS). To demonstrate the performance and robustness of CEEVS, we compare the proposed method with three representative variable selection methods using five published NIR data sets. These case studies clearly demonstrate that by improving the variable selection consistency, we can not only achieve improved prediction performance, but also identify key chemical information from spectroscopic data.

42 ENGINEERING↗
DOE OSTI2020

Chemistry on Quantum Computers with Virtual Quantum Subspace Expansion

Several novel methods for performing calculations relevant to quantum chemistry on quantum computers have been proposed but not yet explored experimentally. Virtual quantum subspace expansion is one such algorithm developed for modeling complex molecules using their full orbital space and without the need for additional quantum resources. Here, we implement this method on the IBM Q platform and calculate the potential energy curves of the hydrogen and lithium dimers using only two qubits and simple classical post-processing. A comparable level of accuracy would require twenty qubits with previous approaches. We also develop an approach to minimize the impact of experimental noise on the stability of a generalized eigenvalue problem that is a crucial component of the algorithm. Our results demonstrate that virtual quantum subspace expansion works well in practice.

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

Polarization Effects in Water-Mediated Selective Cation Transport across a Narrow Transmembrane Channel

We report despite the progress in modeling complex molecular systems of ever-increasing complexity, a quantitatively accurate computational treatment of ion permeation through narrow membrane channels remains challenging. An important factor to reach this goal is induced electronic polarization, which is likely to impact the permeation rate of small ions through narrow molecular pores. In this work, we extended the recently developed polarizable force field based on the classical Drude oscillators to assess the role of induced polarization effects on the energetics of sodium and potassium ion transport across the gramicidin A (gA) ion channel. The inclusion of induced polarization lowers barriers present in 1D potential of mean force (PMF) for cation permeation by ~50% compared to those obtained with the additive force field. Conductance properties calculated with 1D PMFs from Drude simulations are in better agreement with experimental results. Polarization of single-file water molecules and protein atoms forming the narrow pore has a direct impact on the free-energy barriers and cation-specific solid-state NMR chemical shifts. Sensitivity analysis indicates that small changes to water–channel interactions can alter the free energy barrier for ion permeation. These results, illustrating polarization effects present in the complex electrostatic environment of the gA channel, have broad implications for revising proposed mechanisms of ion permeation and selectivity in a variety of ion channels.

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

In Crystallo Snapshots of Rh 2 -Catalyzed C–H Amination

While X-ray crystallography routinely provides structural characterization of kinetically stable pre-catalysts and intermediates, elucidation of the structures of transient reactive intermediates, which are intimately engaged in bond-breaking and -making during catalysis, is generally not possible. Here, we demonstrate in crystallo synthesis of Rh 2 nitrenoids that participate in catalytic C-H amination, and characterize these transient intermediates as triplet adducts of Rh 2 . Further, we observe the impact of coordinating substrate, which is present in excess during catalysis, on the structure of transient Rh 2 nitrenoids. By providing structural characterization of authentic C-H functionali-zation intermediates, and not kinetically stabilized model complexes, these experiments provide the opportunity to define critical structure-activity relationships.

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

NO Coupling at Copper to cis -Hyponitrite: N 2 O Formation via Protonation and H-Atom Transfer

Copper nitrite reductases (CuNIRs) convert NO 2 – to NO as well as NO to N 2 O under high NO flux at a mononuclear type 2 Cu center. While model complexes illustrate N–N coupling from NO that results in symmetric trans-hyponitrite [Cu II ]– ONNO–[Cu II ] complexes, in this work we report NO assembly at a single Cu site in the presence of an external reductant Cp* 2 M (M = Co, Fe) to give the first copper cis-hyponitrites [Cp* 2 M]{[Cu II ](κ 2 - O 2 N 2 )[Cu I ]}. Importantly, the κ 1 –N-bound [Cu I ] fragment may be easily removed by the addition of mild Lewis bases such as CNAr or pyridine to form the spectroscopically similar anion {[Cu II ](κ 2 -O 2 N 2 )} – . The addition of electrophiles such as H + to these anionic copper(II) cis-hyponitrites leads to N 2 O generation with the formation of the dicopper(II)-bis-μ-hydroxide [Cu II ] 2 (μ- OH) 2 . One-electron oxidation of the {[Cu II ](κ 2 -O 2 N 2 )} – core turns on H-atom transfer reactivity, enabling the oxidation of 9,10- dihydroanthracene to anthracene with concomitant formation of N 2 O and [Cu II ] 2 (μ-OH) 2 . These studies illustrate both the reductive coupling of NO at a single copper center and a way to harness the strong oxidizing power of nitric oxide via the neutral cis- hyponitrite [Cu](κ 2 -O 2 N 2 ).

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

Description of the Electronic Structure of Oxyhemoglobin Using Fe L-Edge X-ray Absorption Spectroscopy

The electronic structure of oxyhemoglobin has been controversial since the discovery of the compound’s diamagnetism in 1936. Here, this study uses partial fluorescence yield Fe L-edge X-ray absorption spectroscopy (XAS) in the 3s→2p fluorescence on oxyhemoglobin solutions, measured using a transition-edge sensor detector, to obtain a quantitative experimental description of the electronic structure of the O 2 -bound iron site. The spectrum is very different from typical low-spin Fe II and Fe III heme spectra, and multiplet simulations indicate a mixed ground configuration with ∼57% low-spin Fe III and ∼43% low-spin Fe II character. This is also very different from the Fe II character found for the picket-fence porphyrin model complex. The oxyhemoglobin L-edge XAS data further show that the O 2 ligand engages in a weak σ- but strong π-bond with the iron ion, leading to the overall strong Fe–O 2 bond required for O 2 transport.

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

Isolation of a Terminal Cobalt Nitride in a Metal–Organic Framework

Transition metal nitrides are reactive intermediates in biological and industrial processes. Chemists have synthesized molecular model complexes of such reactive species to understand their function and electronic requirements for new applications. However, molecular chemistry can suffer from intra- and intermolecular decomposition pathways, which preclude further discovery of unknown reactive species. Metal-organic frameworks offer an opportunity for creating long-lived forms of such species with the vacuum of the pore suppressing degradation while simultaneously enabling substrate access for controlled reactivity studies. Here, in this study, we report the characterization of an elusive terminal cobalt nitride species generated through photolysis or thermolysis of a site-isolated cobalt azide within the evacuated metal-organic framework CoN 3 -MFU-4l. The first crystal structure of such a species is presented, with vibrational, X-ray absorption, and electron paramagnetic resonance spectroscopies providing further direct evidence for its formation while elucidating its electronic structure. The system additionally enables subsequent reactivity studies with selected substrates, revealing a unique ambiphilic behavior for a metal nitride species.

Börgel, Jonas [University of California, Berkeley,↗
DOE OSTI2026

Mimicking Extradiol Dioxygenase Reactivity on Iridium

Extradiol dioxygenases catalyze the cleavage of benzenediol (catechol) or vicinal aminophenols via oxygen atom insertion into the 2,3- C–C bond. These reactions are most often proposed to proceed through the migratory rearrangement of a d6 alkylperoxide, generating the corresponding d 6 ring expanded product. However, regiospecific insertion remains a rare outcome among synthetic model complexes. Here, a dioxygenated Ir complex (2) converts to (a) the paramagnetic metallatrioxolane (3) and (b) oxygen atom inserted products (4) and (5); all three complexes are third-row metal analogues of enzymatic intermediates. The conversion of 2 to 3 was triggered by an H• abstraction, generating a third-row metallatrioxolane that is one electron reduced from the canonical d 6 alkylperoxide. Alternatively, photolysis of 2 (467 nm) results in ring-expanded product 4, from which an H• can be abstracted to generate 5. This latter complex is also one electron reduced relative to the canonical ring expansion product. Because extradiol mechanisms were largely defined using Fe(II) metallocofactors, 3 and 5 may be especially relevant to the known Co(II) accepting variants. We propose these states became synthetically accessible via the incorporation of a catechol-like substrate into the larger, multidentate ligand L1. This perturbation enhances the affinity of L1 (and related intermediates) to the metal. The same perturbation may have also been key in characterizing the first κ 2 -bound, dianionic ortho ester ligand – the observed binding mode in the x-ray structure of 5. Broadly, we propose through this case study that connecting abiological dioxygen complexes to non-heme oxygenase-like intermediates might be a “roadmap” towards regiospecific aerobic oxygenations.

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

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (<1 Wm –1 K –1 ) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simulations in the era of exascale computing

Exascale computers — supercomputers that can perform 10 18 floating point operations per second — started coming online in 2022: in the United States, Frontier launched as the first public exascale supercomputer and Aurora is due to open soon; OceanLight and Tianhe-3 are operational in China; and JUPITER is due to launch in 2023 in Europe. Supercomputers offer unprecedented opportunities for modelling complex materials. In this Viewpoint, five researchers working on different types of materials discuss the most promising directions in computational materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine learning sheds light on microbial dark proteins

In this article, metagenomics projects have revealed more than 8 billion non-redundant microbial protein sequences from across the Earth’s biosphere. Of these, 1.17 billion proteins do not have recognizable homologues in any of the more than 100,000 reference genomes available1. Understanding the function of these microbial proteins is a daunting task. Fortunately, machine learning has recently achieved unprecedented accuracy in modelling complex biological data and making predictions. At the forefront of these advancements are machine learning-based approaches that can confidently predict atomic-level protein structures for many (but not all) amino acid sequences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamic urban land extensification is projected to lead to imbalances in the global land-carbon equilibrium

Abstract Human-Earth System Models and Integrated Assessment Models used to explore the land-atmosphere implications of future land-use transitions generally lack dynamic representation of urban lands. Here, we conduct an experiment incorporating dynamic urbanization in a multisector model framework. We integrate projected dynamic non-urban lands from a multisector model with projected dynamic urban lands from 2015 to 2100 at 1-km resolution to examine 1 st -order implications to the land system, crop production, and net primary production that can arise from the competition over land resources. By 2100, future urban extensification could displace 0.1 to 1.4 million km 2 of agriculture lands, leading to 22 to 310 Mt of compromised corn, rice, soybean, and wheat production. When considering increased corn production required to meet demands by 2100, urban extensification could cut increases in yields by half. Losses in net primary production from displaced forest, grassland, and croplands ranged from 0.24 to 2.24 Gt C yr −1 , potentially increasing land emissions by 1.19 to 6.59 Gt CO 2 yr −1 . Although these estimates do not consider adaptive responses, 1 st -order experiments can elucidate the individual role of sub-sectors that would otherwise be masked by model complexity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Stochastic self-tuning hybrid algorithm for reaction-diffusion systems

Many biochemical phenomena involve reactants with vastly different concentrations, some of which are amenable to continuum-level descriptions, while the others are not. We present a hybrid self-tuning algorithm to model such systems. The method combines microscopic (Brownian) dynamics for diffusion with mesoscopic (Gillespie-type) methods for reactions and remains efficient in a wide range of regimes and scenarios with large variations of concentrations. Furthermore, its accuracy, robustness, and versatility are balanced by redefining propensities and optimizing the mesh size and time step. We use a bimolecular reaction to demonstrate the potential of our method in a broad spectrum of scenarios: from almost completely reaction-dominated systems to cases where reactions rarely occur or take place very slowly. The simulation results show that the number of particles present in the system does not degrade the performance of our method. This makes it an accurate and computationally efficient tool to model complex multireaction systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗