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Results for “predictive synthesis”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Harnessing Biomineralization Potential of Metal-Sequestering Bacteria for Rare Earth Elements (REE) Material Synthesis

Extremophilic methylotrophs, such as Methylotuvimicrobium alcaliphilum 20ZR are known for their natural ability to carry out REE capture and uptake. It has been predicted that proteins associated with surface layers facilitate transport and homeostasis of essential metals. Indeed, electron microscopic analysis coupled with antibody staining revealed that cell envelop of metal-grown M. alcaliphilum 20ZR contains metal-binding proteins in the base of the cup-shaped structures of S-layers. While less explored, the proteins associated with S-layers are also predicted to contribute to scavenging of other minerals essential for core metabolism. Here, we employed bottom-up proteomic study to compare the protein content of S-layer fractions of M. alcaliphilum 20ZR cultures grown in the presence and absence trace metals to identify putative components REE-scavenging machinery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Efficient Active Learning for Gaussian Process Classification by Error Reduction

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query. Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis. To overcome these critical limitations, we develop computationally efficient algorithms for EER-based active learning with GPC. Further, we derive the joint predictive distribution of label pairs as a one-dimensional integral, as a result of which the computation of the acquisition function avoids retraining the GPC for each query, remarkably reducing the computational overhead. We also derive the gradient chain rule to efficiently calculate the gradient of the acquisition function, which leads to the first query synthesis active learning algorithm implementing EER-based strategies. Our experiments clearly demonstrate the computational efficiency of the proposed algorithms. We also benchmark our algorithms on both synthetic and real-world datasets, which show superior performance in terms of sampling efficiency compared to the existing state-of-the-art algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CFD modeling of non-catalytic, partial-oxidation engine reformer for flare mitigation

Flaring associated natural gas is commonly employed in the oil and gas industry to reduce methane (CH 4 ) emissions but generates carbon dioxide (CO 2 ) and harmful pollutants, significantly contributing to air pollution and posing risks to public health. To mitigate this impact, M2X Energy Inc. has developed a small-scale, modular gas-to-methanol system. This system features an engine reformer that performs fuel-rich partial oxidation of wellhead gas to produce syngas—a mixture of carbon monoxide (CO) and hydrogen (H 2 )—followed by a downstream reactor for methanol synthesis. This study focused on computational fluid dynamics (CFD) modeling of the engine reformer to simulate partial oxidation chemistry, predict the rich-burn operating limit, and assess syngas quality, ultimately aiding in design and operational optimization. The CFD model, developed within a Reynolds-Averaged Navier-Stokes (RANS) turbulence framework, incorporated sub-models for turbulent combustion, a chemical mechanism with polycyclic aromatic hydrocarbon (PAH) pathways, and soot emissions to accurately capture the fuel-rich, turbulent jet ignition and combustion processes. Model validation against experimental data showed good agreement across pre- and main-chamber pressures, apparent heat release rates, and exhaust gas concentrations of key species (H 2 , CO, CO 2 , CH 4 ) for varying intake equivalence ratios. Here, the model identified a rich-burn operating limit near a fuel-air equivalence ratio of 2.35, consistent with experimental observations. Furthermore, syngas quality analysis revealed that extending the rich-burn limit through engine reformer optimization could enhance syngas production, contributing to higher methanol synthesis efficiency.

Computational Fluid Dynamics↗
DOE OSTI2021

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

Abstract As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

DFT-guided flux synthesis of a family of layered titanates crystallizing in the lepidocrocite structure type

K 0.72 Fe 0.72 Ti 1.28 O 4 , a layered mixed-metal oxide belonging to the lepidocrocite structure type, was grown out of a molten KCl-KF mixture at 800 °C. The compound crystallizes in orthorhombic space group Cmcm and exhibits two-dimensional layers, composed of mixed Fe/Ti octahedra, separated by K ions that occupy the interlayer layer space and maintain charge balance. The targeted flux synthesis of the Ni, Cu, and Zn analogs was carried out after density functional theory (DFT) calculations predicted their formation to be energetically favorable, yielding K 0.77 Ni 0.38 Ti 1.62 O 4 , K 0.77 Cu 0.38 Ti 1.62 O 4 , and K 0.73 Zn 0.36 Ti 1.64 O 4 . Finally, all four compositions can be prepared via conventional solid-state technique by mixing and heating stoichiometric amounts of KNO 3 , appropriate transition metal oxide precursors, and TiO 2 .

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

Synthesis of inter‐lanthanide sesquioxides LnLn'O 3 by polymeric steric entrapment

Lanthanide oxides are well known in the fields of optical, electronic, and magnetic materials. Even so, there are many application spaces yet to be explored. Previous modeling of inter-lanthanide sesquioxides, in which the compound contains two lanthanide cations, predicts the lowest level energy structure as a function of chemistry, which this work seeks to verify. Three materials of interest, ErLuO 3 , LaLuO 3 , and SmLuO 3 , were synthesized for the first time by the polymeric steric entrapment (PSE) method. X-ray diffraction confirms the stable state predictions of ErLuO 3 and SmLuO 3 forming a bixbyite type structure and LaLuO 3 forming a perovskite type structure. This work demonstrates PSE as a viable and reliable route toward the synthesis of these unique materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗
DOE OSTI2025

Discovery and Characterization of a Metastable Cubic Interstitial Nickel–Carbon System with an Expanded Lattice

Metastable, i.e., kinetically favored but thermodynamically not stable, interstitial solid solutions of carbon in iron are well-understood. Carbon can occupy the interstitial atoms of the host metal, altering its properties. Alloying of the host metal results in the stabilization of the FeC x phases, widening its application. Pure nickel finds niche applications, mainly focusing on catalysis, while nickel alloys are widely applied, e.g., in gas turbines, reactors, and seawater piping. Nickel carbide (Ni 3 C) is the well-known stable Ni–C system displaying a rhombohedral (R3̅c) crystal structure. Some reports describe an elusive cubic Ni–C system, observed during certain catalytic reactions occurring on nickel and formed by the occupation of the interstitials of the metal with carbon: to date, the stabilization and characterization of this phase have not been accomplished. Hereby, we report on the synthesis of a cubic metastable NiC x phase using chemical vapor deposition of methane on supported nickel nanoparticles. The structure was predicted by DFT/ReaxFF, synthesized and monitored with in situ time-resolved synchrotron XRD, and experimentally confirmed by Rietveld refinement and (S)TEM-EELS under ambient conditions. The results show an Fm3̅m phase with a lattice parameter of a = 3.749 ± 0.037 Å at room temperature, with the highest ever reported atomic percentage of carbon occupying the octahedral interstices of 23.1%, resulting in a NiC 0.3 phase. The degree of occupation of the interstitial voids by carbon can be controlled, enabling the tuning of the host metal’s d-spacing and composition, highlighting the applicability of this synthesis route for catalytic nanoparticle preparation.

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

Direct HCN synthesis via plasma-assisted conversion of methane and nitrogen

Hydrogen cyanide (HCN) is synthesized from ammonia (NH 3 ) and methane (CH 4 ) at ~1200°C over a Pt catalyst. Ammonia synthesis entails several complex, highly emitting processes. Plasma-assisted HCN synthesis directly from CH 4 and nitrogen (N 2 ) could be pivotal for on-demand HCN production. Here, we evaluate the potential of dielectric barrier discharge (DBD) N 2 /CH 4 plasma for decentralized catalyst-free selective HCN production. We demonstrate a single-step conversion of methane and nitrogen to HCN with a 72% yield at <300°C. HCN is favored at low CH 4 concentrations with ethane (C 2 H 6 ) as the secondary product. We propose a first-principles microkinetic model with few electron impact reactions. The model accurately predicts primary product yields and elucidates that methyl radical (·CH 3 ) is a common intermediate in HCN and C 2 H 6 synthesis. Compared to current industrial processes, N 2 /CH 4 DBD plasma can achieve minimal CO 2 emissions.

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

Polymer informatics: Current status and critical next steps

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here in this paper, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In Crystallo Photochemistry: Reimagining Synthetic Tractability with Transparent Single-Crystalline Flasks

Expanding the boundaries of synthetic tractability of what molecules can be synthesized and isolated is an eternal challenge for synthetic chemists. The development of new synthetic methods and strategies enables the properties and potential functions of novel molecular targets to be experimentally evaluated. In the context of catalysis, predictable synthetic strategies are often available to access kinetically persistent intermediates such as catalyst resting states. In contrast, synthesis and characterization of the reactive intermediates are often not possible due to the fleeting lifetimes of these species. In crystallo photochemistry combines single-crystal matrix isolation with cryogenic photochemistry to enable reactive intermediates to be synthesized under conditions in which they are persistent and can be (crystallographically) characterized. This Outlook highlights key achievements of in crystallo photochemistry as well as discusses opportunities and challenges that confront realization of the potential of in crystallo synthesis to redefine the boundaries of synthetic tractability.

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

Structural basis for the calmodulin-mediated activation of eukaryotic elongation factor 2 kinase

Translation is a tightly regulated process that ensures optimal protein quality and enables adaptation to energy/nutrient availability. The α-kinase eukaryotic elongation factor 2 kinase (eEF-2K), a key regulator of translation, specifically phosphorylates the guanosine triphosphatase eEF-2, thereby reducing its affinity for the ribosome and suppressing the elongation phase of protein synthesis. eEF-2K activation requires calmodulin binding and autophosphorylation at the primary stimulatory site, T348. Biochemical studies predict a calmodulin-mediated activation mechanism for eEF-2K distinct from other calmodulin-dependent kinases. Here, we resolve the atomic details of this mechanism through a 2.3-Å crystal structure of the heterodimeric complex of calmodulin and the functional core of eEF-2K (eEF-2K TR ). This structure, which represents the activated T348-phosphorylated state of eEF-2K TR , highlights an intimate association of the kinase with the calmodulin C-lobe, creating an “activation spine” that connects its amino-terminal calmodulin-targeting motif to its active site through a conserved regulatory element.

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

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models

The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications.

25 ENERGY STORAGE↗
DOE OSTI2025

Toward the Observation of Dimagnesocene

We have examined the electronic structure of C 5 H 5 MgMgC 5 H 5 , or dimagnesocene, using high-level coupled-cluster techniques. This research is suitable in light of the remarkable synthesis of the valence-isoelectronic diberyllocene by Boronski, Crumpton, Wales, and Aldridge. The Mg–Mg bond distance is predicted to be 2.758 Å, and the Mg–Mg bond dissociation energy is predicted to be 51.8 kcal/mol. Unique aspects to the present research is our characterization of the ionization energy and the electron affinity of this molecule, the energy of dissociation into two neutral cyclopentadienyl magnesium radicals, the determination of the neutral structure at the CCSD(T)/cc-pVTZ level, and the computation of the Raman intensities at the MP2/cc-pVDZ level. Apart from mass spectroscopy, the simplest means of experimental detection is gas-phase or matrix-isolated infrared spectroscopy, in which the A 2 ″ peak at 801 cm –1 should be the most prominent, with an intensity of 581 km/mol.

bond cleavage↗
DOE OSTI2020

Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry

Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ω B97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation—for example, by enabling the development of novel machine learning models for quantitative reaction prediction.

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

Ecological strategies of microbes: Thinking outside the triangle

Abstract I asked whether Grime's triangle of competitive, stress tolerance and ruderal ecological strategies—which was originally developed for plants—applies to microbes. I conducted a synthesis of empirical studies that tested relationships among microbial traits presumed to define the competitive, stress tolerance and ruderal, and other ecological strategies. There was broad support for Grime's triangle. However, the ecological strategies were inconsistently linked to shifts in microbial communities under environmental changes like nitrogen and phosphorus addition, warming, drought, etc. We may be missing important ecological strategies that more closely influence microbial community composition under shifting environmental conditions. We may need to start by documenting changes in microbial communities in response to environmental conditions at fine spatiotemporal scales relevant for microbes. We can then develop empirically based ecological strategies, rather than modifying those based on plant ecology. Synthesis . Microbes appear to sort into similar ecological strategies as plants. However, these microbial ecological strategies do not consistently predict how community composition will shift under environmental change. By starting ‘from the ground up’, we may be able to delineate ecological strategies more relevant for microbes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Developing a microwave-driven reactor for ammonia synthesis: insights into the unique challenges of microwave catalysis

Rapid development of new ammonia (NH 3 ) synthesis techniques that enable modular, intermittent production is essential to actualizing NH 3 's potential as a clean energy carrier, since contemporary methods are configured to centralized, continuous production methods with high emissions and are incompatible with renewable sources. Here, in this mission, microwave-driven catalysis is promising for its ability to enhance reaction kinetics and apply targeted heating for efficient energy use. However, owing to an incomplete understanding of the interaction between microwave fields and catalyst beds, the development of such microwave-catalysis systems remains underexplored and challenging. This paper investigates the 10× scale-up of a microwave-based NH 3 synthesis reactor using numerical and experimental approaches, achieving the largest reported microwave-driven NH 3 reactor to date. Results elucidate phenomena unique to microwave processes that are only predictable through numerical modeling, including how a catalyst's dielectric properties influence microwave field distribution by affecting penetration depth and how energy utilization can be poor even with sufficient attenuation. These dynamics change with scale, constrain reactor geometry, and potentially hamper performance. Nonetheless, we demonstrate a production rate of 56.6 g NH$_3$ per day, the highest reported NH 3 synthesis rate for laboratory-scale alternative techniques; correspondingly, the benchmark energy efficiency achieved in this paper (45.6 g NH$_3$ kW h -1 ) is the highest reported for such reactors of sufficient scale. Even with this exemplary energy efficiency, energy losses were found in excess of 50%, an issue resolvable through scale and reactor design. The efficiencies imparted by microwaves were key in these achievements, warranting further investigation toward development of microwave-driven NH 3 systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Surface Passivation by Excess Sulfur for Controlled Synthesis of Large, Thin SnS Flakes

Tin sulfide (SnS) is part of the group IV chalcogenides (SnX, GeX; X: S, Se), a family of anisotropic layered materials considered for thin film photovoltaics, optoelectronics, and valleytronics, and predicted narrow bandgap multiferroic materials. Large ultrathin SnS flakes, suitable for a variety of applications, are challenging to synthesize because of the enhanced surface reactivity due to the open layer structure of SnS which gives rise to a competition between lateral and vertical growth. Here we investigate the effects of added sulfur on passivating the surface and modifying the balance between lateral expansion and thickening of SnS flakes in chemical vapor transport from a SnS precursor. In addition, we investigate the growth of SnS flakes and compare the results of synthesis from a pure SnS precursor with growth, in which a slight excess of sulfur is supplied in different ways. Our results demonstrate that small amounts of excess sulfur can profoundly affect the size and thickness distributions of SnS flakes. The largest and thinnest flakes are obtained if (i) traces of sulfur are added and (ii) the sulfur source consists either of small Sx fragments released at high temperatures from the reactor walls, or atomic S supplied by sublimation from SnS 2 . The likely mechanism for the observed growth modifications is a transient surface passivation of SnS flakes during growth, which reduces the reactivity of the top facet of the flakes, limits vertical growth, and thus gives rise to ensembles with increased lateral size and reduced thickness.

36 MATERIALS SCIENCE↗