SEARCH · Engineering Papers

Results for “synthesis (predictive)”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Growth Strategy of a Hybrid Yb3Rh4Sn13/LaRuSn3 Structure Type: Integrating Thermal Analysis, In Situ Diffraction, and Geometric Descriptors

Remeika phases form a versatile family of cage like intermetallics built from transition metal–centered trigonal prisms linked into three dimensional frameworks that generate two characteristic voids: a large [AX₁₂] cuboctahedral cage hosting the rare earth ion and an [XX′₁₂] icosahedral cage accommodating the tetrel atom. In this work, we present a tool driven approach for targeted solid state synthesis of Remeika phases, integrating differential scanning calorimetry, in situ neutron diffraction, and a geometric tolerance factor that quantifies rare earth size compatibility within these polyhedral cages. When combined with arc melting and metallic flux crystal growth, this framework enables prediction, verification, and isolation of specific Remeika phases. Applying this strategy, we obtain single crystals of the predicted pseudo-perovskite Fe based Remeika compound Yb0.9FeGe3.1. More broadly, this perspective highlights how coupling real time structural probes with simple geometric descriptors provides a general pathway for designing synthesis conditions and accessing potentially metastable structure types in complex intermetallic systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Synthesis of wafer-scale SWCNT forests with remarkably invariant structural properties in a bulk-diffusion-controlled kinetic regime

Robust synthesis of vertically aligned carbon nanotubes (VACNT) at large scale is required to accelerate deployment of numerous cutting-edge devices to emerging commercial applications. To address this need, this work demonstrates that the structural characteristics of single-walled CNTs (SWCNTs) produced in a growth regime dominated by bulk diffusion of the gaseous carbon precursor are remarkably invariant over a broad range of process conditions (precursor concentration increased up to 30-fold, catalyst substrate area from 1 cm 2 to 180 cm 2 , growth pressure from 20 to 790 mbar, and gas flowrates up to 8-fold). A simple growth kinetics model is derived that accounts for both reactant bulk diffusion and competing byproduct formation in the presence of excess hydrogen, which quantitatively reproduces the experimental data in the entire isothermal parameter space. The model enables critical predictions for process scale-up optimization including a potential 6-fold increase in CNT production rate, a ~90% carbon conversion efficiency in select conditions, and elimination of reaction rate decay observed at high pressures by using a hydrogen-free growth environment. This model-guided opportunity to substantially improve synthesis throughput and efficiency, in conjunction with the remarkably invariant CNT properties, is appealing for reliable VACNT device fabrication at both small and industrial scales.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Room-Temperature Synthesis of 2D Janus Crystals and their Heterostructures

Not provided.

Chemistry↗

DOE OSTI2023

Autonomous and dynamic precursor selection for solid-state materials synthesis

Abstract Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS 3 ) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS 3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS 3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Isotope effects at the origin of life: Fingerprints of the Strecker synthesis

We report strecker synthesis creates α-amino acids from prebiotically plausible substrates (cyanide, ammonia, and aldehydes) and is widely hypothesized to be a key mechanism in the chemistry that led to life on Earth and on other planets. To better constrain the synthetic environments and precursors of abiotic α-amino acids, and to determine unique signatures of abiogenic amino acids, we measured the molecular-averaged and site-specific carbon and nitrogen isotope effects for the Strecker synthesis of alanine. The reaction steps of the Strecker synthesis can be divided into two groups: an initial series of reversible amination and nitrile-addition reactions (‘equilibration’) and a second series of irreversible hydrolysis reactions (‘hydrolysis’). The equilibration of cyanide, acetaldehyde, and ammonia with the intermediate, α-aminopropionitrile (α-APN), has a measured 55.1‰ equilibrium nitrogen isotope effect between the 15 N-rich amine nitrogen in α-aminopropionitrile and the 15 N-poor ammonia and a 20.0‰ equilibrium carbon isotope effect between the 13 C-poor C-2 site in α-aminopropionitrile and the 13 C-rich carbonyl carbon in acetaldehyde. The first irreversible hydrolysis step is inferred to have an up to 10‰ normal carbon fractionation (i.e., faster for 12 C, slower for 13 C) for the whole molecule, but it also has one or more side reactions that deplete the reactive α-APN reservoir by up to 15‰. The second hydrolysis step has a 15.4‰ normal kinetic isotope effect on the amide (C-1) site of alaninamide, which becomes the carboxyl site of alanine. Other α-amino acids will likely experience similar nitrogen isotope fractionations between ammonia and their amine sites, and similar carbon isotope fractionations between the carbonyl carbon in reactant aldehydes or ketones and the intermediate α-aminonitrile, and between cyanide and the carboxyl site. Therefore, these isotope effects allow us to predict the carbon and nitrogen isotopic contents and intramolecular structures of α-amino acids formed by Strecker synthesis based on their substrates’ isotopic compositions, or to infer the isotopic compositions of substrates from which amino acids formed, for example in the case of the amino-acid-rich carbonaceous chondrites. The site-specific C and N isotopic compositions of amino acids formed by Strecker chemistry contrast with those typical of terrestrial biosynthetic amino acids, so these data also provide a means of discriminating between biogenic and abiogenic α-amino acids.

58 GEOSCIENCES↗

DOE OSTI2025

Isoreticular Tolerance and Phase Selection in the Synthesis of Multi‐Module Metal–Organic Frameworks for Gas Separation and Electrocatalytic OER

Abstract Although metal–organic frameworks are coordination‐driven assemblies, the structural prediction and design using metal‐ligand interactions can be unreliable due to other competing interactions. Leveraging non‐coordination interactions to develop porous assemblies could enable new materials and applications. Here, we use a multi‐module MOF system to explore important and pervasive impact of ligand‐ligand interactions on metal‐ligand as well as ligand‐ligand co‐assembly process. It is found that ligand‐ligand interactions play critical roles on the scope or breakdown of isoreticular chemistry. With cooperative di‐ and tri‐topic ligands, a family of Ni‐MOFs has been synthesized in various structure types including partitioned MIL‐88‐acs ( pacs ), interrupted pacs (i‐ pacs ), and UMCM‐1‐muo. A new type of isoreticular chemistry on the muo platform is established between two drastically different chemical systems. The gas sorption and electrocatalytic studies were performed that reveal excellent performance such as high C 2 H 2 /CO 2 selectivity of 21.8 and high C 2 H 2 uptake capacity of 114.5 cm 3 /g at 298 K and 1 bar.

Xiao, Yuchen [Department of Chemistry University o↗

DOE OSTI2025

Isoreticular Tolerance and Phase Selection in the Synthesis of Multi‐Module Metal–Organic Frameworks for Gas Separation and Electrocatalytic OER

Xiao, Yuchen [Department of Chemistry University o↗

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↗

NASA NTRS1986

COFS 2: 3-D dynamics and controls technology

The Control of Flexible Structures (COFS) 2 project is a complex and ambitious undertaking which will address several critical technology areas. Among them are modeling, structural dynamics, controls, and ground testing issues which are not only germane to this effort, but to other large space structure programs being contemplated. This effort requires the early integration of controls and structural dynamics considerations in order to achieve mission success. Several technology advances must be achieved in the areas of system modelling, control synthesis and methodology, sensor/actuator development, and ground testing techniques for system evaluation and on-orbit performance prediction and verification. This project offers a unique opportunity for the integration of several disciplines to produce technology advances which will benefit many future programs. In addition, the opportunities available to participate in the various levels in the phase of this project, e.g., analytical development and modelling, ground testing, and flight testing, permit for the involvement of a significant number of interested researchers and organizations from government, universities and industry.

Pyle, Jon S.↗

NASA NTRS1987

Control of flexible structures and the research community

The Control of Flexible Structures II (CPFS) program is a complex and ambitious undertaking which addresses several critical technology areas. Among them are modeling, structural dynamics, control, and ground testing issues, which are also applicable to other large space structure programs being contemplated. This effort requires early integration of controls and structural dynamic considerations. Several technological advances must be achieved in the areas of system modeling, control synthesis and methodology, sensor/actuator development, and ground testing techniques for system evaluation and on-orbit performance prediction and verification. This program offers an opportunity for the integration of several disciplines to produce technology advances which will benefit many future programs.

Keckler, Claude R.↗

NASA NTRS1995

Growth of single crystals of organic salts with large second-order optical nonlinearities by solution processes for devices

Data obtained from the electric field induced second harmonic generation (EFISH) and Kurtz Powder Methods will be provided to MSFC for further refinement of their method. A theoretical model for predicting the second-order nonlinearities of organic salts is being worked on. Another task is the synthesis of a number of salts with various counterions. Several salts with promising SHG activities and new salts will be tested for the presence of two crystalline forms. The materials will be recrystallized from dry and wet solvents and compared for SHG efficiency. Salts that have a high SHG efficiency and no tendency to form hydrates will be documented. The synthesis of these materials are included in this report. A third task involves method to aid in the growth of large, high quality single crystals by solution processes. These crystals will be characterized for their applicability in the fabrication of devices that will be incorporated into optical computers in future programs. Single crystals of optimum quality may be obtained by crystal growth in low-gravity. The final task is the design of a temperature lowering single crystal growth apparatus for ground based work. At least one prototype will be built.

Leslie, Thomas M.↗

NASA NTRS2005

Influence of the Gas Pressure on Single-wall Carbon Nanotubes Formation

Experiments and modeling have been performed to predict the effect of gas pressure on species distribution and nanotube growth rate under specific conditions of synthesis of singlewall carbon nanotubes (SWCNTs) by arc discharge. Numerical results were compared with experiments in order to find a consistent correlation between the nanotube growth and the pressure. We used argon and helium as buffer gases with a total pressure varied between 0.1 and 1 bar. We experimentally observed that both the anode erosion rate and the Brunauer-Emmett-Teller (BET) surface area of the as produced nanotube soot material are very sensitive to the total gas pressure in the reactor

Hinkov, I.↗

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↗