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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence

The traditional development of novel metal–organic frameworks (MOFs) is often hindered by challenges such as synthetic accessibility and time- and resource-intensive experimentation. High-throughput, automated experimental and computational techniques have enabled rapid chemical space exploration and theoretical MOF design. When combined with artificial intelligence (AI), these methods can be used to lead autonomous laboratories to new frontiers for MOF discovery, where these materials can be designed for a specific application, efficiently synthesized, characterized, and evaluated. Here, this perspective highlights the role of AI in advancing automated MOF synthesis and characterization, computational MOF design and screening, and the integration of these approaches within autonomous workflows to ultimately enable the MOF laboratories of the future.

Gaidimas, Madeleine A. [Northwestern University, E↗
DOE OSTI2023

Gradient process parameter optimization in additive friction stir deposition of aluminum alloys

As one of the most novel additive manufacturing methods, currently selection and optimization of processing parameters in additive friction stir deposition (AFSD) have mainly relied on experiments and subsequent characterization of microstructural and mechanical properties. Such approaches are both time- and resource-consuming. Therefore, an ultrasound elastography enhanced gradient process parameter optimization method was applied in the present work to obtain a window of optimized processing parameters for AFSD processing of aluminum alloy by varying both rotational and linear deposition speeds. Further, the quality of AFSD processed layer was investigated for physical nature of surface, dynamic elastic modulus, and microstructural aspects in cross-sections of the deposited layer. The efficiency in exploring process parameters was significantly enhanced by implementing a high-throughput screening experimental design based on application of gradient process parameters and continuous ultrasound elastographs. In addition, the applied ultrasonic elastography technique assisted in evaluating the homogeneity in microstructure and mechanical properties of AFSD sample over the entire gradients of the process parameters. The techniques adopted in current work can be further extended to identify suitable parameters for AFSD fabrication of components with desired mechanical properties such as hardness, fatigue, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Experimental Studies of Boronophenylalanine ({sup 10}BPA) Biodistribution for the Individual Application of Boron Neutron Capture Therapy (BNCT) for Malignant Melanoma Treatment

Purpose: Patients with the same histopathologic diagnosis of cutaneous melanoma treated with identical protocols of boron neutron capture therapy (BNCT) have shown different clinical outcomes. The objective of the present studies was to evaluate the biodistribution of boronophenilalanina ({sup 10}BPA) for the potential application of BNCT for the treatment of melanoma on an individual basis. Methods and Materials: The boronophenilalanine (BPA) uptake was evaluated in 3 human melanoma cell lines: MEL-J, A375, and M8. NIH nude mice were implanted with 4 10{sup 6} MEL-J cells, and biodistribution studies of BPA (350 mg/kg intraperitoneally) were performed. Static infrared imaging using a specially modified infrared camera adapted to measure the body infrared radiance of small animals was used. Proliferation marker, Ki-67, and endothelial marker, CD31, were analyzed in tumor samples. Results: The in vitro studies demonstrated different patterns of BPA uptake for each analyzed cell line (P<.001 for MEL-J and A375 vs M8 cells). The in vivo studies showed a maximum average boron concentration of 25.9 ± 2.6 μg/g in tumor, with individual values ranging between 11.7 and 52.0 μg/g of {sup 10}B 2 hours after the injection of BPA. Tumor temperature always decreased as the tumors increased in size, with values ranging between 37°C and 23°C. A significant correlation between tumor temperature and tumor-to-blood boron concentration ratio was found (R{sup 2} = 0.7, rational function fit). The immunohistochemical studies revealed, in tumors with extensive areas of viability, a high number of positive cells for Ki-67, blood vessels of large diameter evidenced by the marker CD31, and a direct logistic correlation between proliferative status and boron concentration difference between tumor and blood (R{sup 2} = 0.81, logistic function fit). Conclusion: We propose that these methods could be suitable for designing new screening protocols applied before melanoma BNCT treatment for each individual patient and lesion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Machine learning for the redox potential prediction of molecules in organic redox flow battery

Here, organic redox flow batteries (ORFB) are recognized as an innovative technology for the large-scale storage of renewable energy. The redox potential of organic redox-active molecules plays a vital role in their performance. Advanced screening techniques like high-throughput experiment and machine learning (ML) have significantly enhanced organic material performance and transformed the field of ORFB. However, the scarcity of experimental data poses a considerable challenge for ML model development in this domain. In our study, we developed lightweight graph-based Gaussian process regression (GPR) models with GPU-accelerated marginalized graph kernel and hybrid kernel to predict the redox potentials of organic redox-active molecules for ORFBs, specifically focusing on small datasets. To evaluate model accuracy, we created a new experimental database of organic redox-active molecules by the data from hundreds of published papers and assembled previous computational datasets. We also considered some key parameters, such as pH conditions and solvent type, to assess their impact on redox potential prediction. Our GPR model predicted redox potentials with high accuracy across all datasets using minimal training data. The study provides powerful tools for molecule screening and design and delivers valuable guidance on designing training datasets for costly experiments.

25 ENERGY STORAGE↗
DOE OSTI2018

Computational Evaluation of Mg–Salen Compounds as Subsurface Fluid Tracers: Molecular Dynamics Simulations in Toluene–Water Mixtures and Clay Mineral Nanopores

Molecular tracers that can be selectively placed underground and uniquely identified at the surface using simple on-site spectroscopic methods would significantly enhance subsurface fluid monitoring capabilities. To ensure their widespread utility, the solubility of these tracers must be easily tuned to oil- or water-wet conditions as well as reducing or eliminating their propensity to adsorb onto subsurface rock and/or mineral phases. In this work, molecular dynamics simulations were used to investigate the relative solubilities and mineral surface adsorption properties of three candidate tracer compounds comprising Mg–salen derivatives of varying degrees of hydrophilic character. Simulations in water–toluene liquid mixtures indicate that the partitioning of each Mg–salen compound relative to the interface is strongly influenced by the degree of hydrophobicity of the compound. Simulations of these complexes in fluid-filled mineral nanopores containing neutral (kaolinite) and negatively charged (montmorillonite) mineral surfaces reveal that adsorption tendencies depend upon a variety of parameters, including tracer chemical properties, mineral surface type, and solvent type (water or toluene). Simulation snapshots and averaged density profiles reveal insight into the solvation and adsorption mechanisms that control the partitioning of these complexes in mixed liquid phases and nanopore environments. As a result, this work demonstrates the utility of molecular simulation in the design and screening of molecular tracers for use in subsurface applications.

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

Accurate Prediction of pKb in Amines: Validation of the CAM-B3LYP/6-311+G(d,p)/SMD Model

Amines play several key roles in chemistry and biology and are involved in numerous industrial processes, often with significant economic impacts. Recently, amines are also garnering interest as catalysts for polymer synthesis and for CO 2 fixation, incentivizing the need to rapidly design and screen new amino compounds. Hence, developing reliable methods to predict their physicochemical properties, e.g., the base dissociation constant (pKb), is pivotal. Here, a density functional theory (DFT)-based approach was employed to compute the pKb of substituted amines, exploring the impact of several key parameters, including (i) the number of explicit water molecules at the reaction center, (ii) the van der Waals (vdW) surface, and (iii) solvent polarizability. In previous work, it was determined that including two explicit water molecules at the reaction center resulted in highly accurate pKb estimates for primary amines. Here, we find that including a third water molecule at the reaction center is essential for accurate pKb for secondary and tertiary amines. The revised methodology was then applied to a wider selection of amines, obtaining a minimum average error (MAE) < 0.4. In conclusion, this result represents an extension of our “easy-to-use method,” a simple and direct DFT approach exploiting CAM-B3LYP/SMD/6-311G+(d,p) to compute pKb without post facto modifications.

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

Trends in Formic Acid Electro-Oxidation on Transition Metals Alloyed with Platinum and Palladium

Direct formic acid fuel cells (DFAFCs) have emerged as a promising power source to meet increased demands for alternative energy sources in the transportation and portable energy storage sectors. Furthermore, these fuel cells utilize formic acid (FA), a nontoxic and carbon-neutral fuel when produced from biomass or via CO 2 reduction. Despite the promise of DFAFCs, the best monometallic catalysts, platinum and palladium, are poisoned by CO through the indirect oxidation pathway and require large overpotentials. By alloying Pt and Pd with other metals, we aim to improve both the activity and selectivity of these catalysts. Here, we present a systematic density functional theory (GGA-PW91) study of FA electro-oxidation (FAO) on the (111) facet of bimetallic Pt (Pt 3 M) or Pd (Pd 3 M) catalysts (M = Au, Ag, Cu, Pt, Pd, Ir, Rh, Ru, or Re) to evaluate the catalytic performance of these surfaces. For each surface, we calculate free energy diagrams and onset potentials of three key reaction mechanisms: direct oxidation of FA via carboxyl (COOH), direct oxidation of FA via formate (HCOO), and the indirect oxidation of FA that first forms CO en route to full oxidation to CO 2 . We then display the trends in the form of phase diagrams that compare the activity of the calculated surfaces against regions of high activity using the free energies of adsorbed CO and OH as descriptors, enabling high-throughput screening and design of improved catalysts, particularly those alloying Pt or Pd with Ir, Ru, or Re.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

General Structure–Reactivity Relationship for Oxygen on Transition-Metal Oxides

Despite many recent developments in designing and screening catalysts for improved performance, transition-metal oxides continue to prove to be challenging due to the myriad inherent complexities of the systems and the possible morphologies that they can exhibit. Herein we propose a structural descriptor, the adjusted coordination number (ACN), which can generalize the reactivity of the oxygen sites over the many possible surface facets and defects of a transition-metal oxide. We demonstrate the strong correlation of this geometric descriptor with the electronic and energetic properties of the active sites on five facets of four transition-metal oxides. We then use the structural descriptor to predict C–H activation energies to show the great potential of using ACN for the prediction of catalytic performance. This study presents a first look into quantifying the relation between active site structure and reactivity of transition-metal-oxide catalysts.

Fung, Victor↗
DOE OSTI2017

Thermodynamic Route to Efficient Prediction of Gas Diffusivity in Nanoporous Materials

We report an efficient computational procedure for rapid and accurate prediction of the self-diffusivity of gas molecules in nanoporous materials by implementing the transition state theory for intercage hopping at infinite dilution with the string method in conjunction with the excess-entropy scaling for predicting gas diffusion coefficients at finite loadings. The theoretical procedure has been calibrated with molecular dynamics simulations for the diffusion coefficients of methane and hydrogen gases in representative nanoporous materials including metal organic frameworks and zeolites. Combined with the classical density functional theory for calculating the excess entropy of gas molecules in micropores, the theoretical procedure enables efficient computation of both thermodynamic and transport properties important for design and screening of nanostructured materials for gas storage and separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Water-Activated VOPO 4 for Magnesium Ion Batteries

Rechargeable Mg batteries, using high capacity and dendrite-free Mg metal anodes, are promising energy storage devices for large scale smart grid due to low cost and high safety. However, the performance of Mg batteries is still plagued by the slow reaction kinetics of their cathode materials. Recent discoveries demonstrate that water in cathode can significantly enhance the Mg-ion diffusion in cathode by an unknown mechanism. Here, we propose the water-activated layered-structure VOPO 4 as a novel cathode material and examine the impact of water in electrode or organic electrolyte on the thermodynamics and kinetics of Mg-ion intercalation/deintercalation in cathodes. Electrochemical measurements verify that water in both VOPO 4 lattice and organic electrolyte can largely activate VOPO 4 cathode. Thermodynamic analysis demonstrates that the water in the electrolyte will equilibrate with the structural water in VOPO 4 lattice, and the water activity in the electrolyte alerts the mechanism and kinetics for electrochemical Mg-ion intercalation in VOPO 4 . Theoretical calculations and experimental results demonstrate that water reduces both the solid-state diffusion barrier in the VOPO 4 electrode and the desolvation penalty at the interface. To achieve fast reaction kinetics, the water activity in the electrolyte should be larger than 10 –2 . The proposed activation mechanism provides guidance for screening and designing novel chemistry for high performance multivalent-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Catalytic oxidation of Li 2 S on the surface of metal sulfides for Li−S batteries

Significance A series of metal sulfides were systematically investigated as polar hosts to reveal the key parameters correlated to the energy barriers and polysulfide adsorption capability in Li−S batteries. The investigation demonstrates that the catalyzing oxidation capability of metal sulfides is critical in reducing the energy barrier and contributing to the remarkably improved battery performance. Density functional theory simulation allows us to identify the mechanism for how binding energy and polysulfides trapping dominate the Li 2 S decomposition process and overall battery performance. The understanding can serve as a general guiding principle for the rational design and screening of advanced materials for high-energy Li−S batteries.

Zhou, Guangmin↗
DOE OSTI2023

Triple junction solute segregation in Al-based polycrystals

Solute segregation is a crucial means of stabilizing nanostructured alloys, and at very small grain sizes, this requires consideration of triple junctions (TJs), which attain a meaningful volume fraction and thus become relevant for bulk material behavior. Here, in this study, the solute segregation spectra for grain boundary (GB) and TJ sites are calculated for a large number of dilute Al-based binary systems with available interatomic potentials. A defect-identifying algorithm is applied to quantify the average GB thickness and classify the intergranular site spectra into GB and junction subspectra. The algorithm is also applied to a hybrid electronic-level database for GB segregation of various solutes in Al, yielding polycrystalline TJ solute segregation spectra from first principles. The results suggest that TJ segregation is alloy or interatomic potential dependent and can exhibit either boundary or junction preference. With these spectra as inputs, the spectral GB segregation model gives quantitative predictions of segregation as a function of grain size, temperature, and total solute concentration, suitable for alloy screening and design.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

SIMBAD : a sequence-independent molecular-replacement pipeline

The conventional approach to finding structurally similar search models for use in molecular replacement (MR) is to use the sequence of the target to search against those of a set of known structures. Sequence similarity often correlates with structure similarity. Given sufficient similarity, a known structure correctly positioned in the target cell by the MR process can provide an approximation to the unknown phases of the target. An alternative approach to identifying homologous structures suitable for MR is to exploit the measured data directly, comparing the lattice parameters or the experimentally derived structure-factor amplitudes with those of known structures. Here, SIMBAD , a new sequence-independent MR pipeline which implements these approaches, is presented. SIMBAD can identify cases of contaminant crystallization and other mishaps such as mistaken identity (swapped crystallization trays), as well as solving unsequenced targets and providing a brute-force approach where sequence-dependent search-model identification may be nontrivial, for example because of conformational diversity among identifiable homologues. The program implements a three-step pipeline to efficiently identify a suitable search model in a database of known structures. The first step performs a lattice-parameter search against the entire Protein Data Bank (PDB), rapidly determining whether or not a homologue exists in the same crystal form. The second step is designed to screen the target data for the presence of a crystallized contaminant, a not uncommon occurrence in macromolecular crystallography. Solving structures with MR in such cases can remain problematic for many years, since the search models, which are assumed to be similar to the structure of interest, are not necessarily related to the structures that have actually crystallized. To cater for this eventuality, SIMBAD rapidly screens the data against a database of known contaminant structures. Where the first two steps fail to yield a solution, a final step in SIMBAD can be invoked to perform a brute-force search of a nonredundant PDB database provided by the MoRDa MR software. Through early-access usage of SIMBAD , this approach has solved novel cases that have otherwise proved difficult to solve.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Rational synthesis of atomically precise graphene nanoribbons directly on metal oxide surfaces

Atomically precise graphene nanoribbons (GNRs) attract great interest because of their highly tunable electronic, optical, and transport properties. However, on-surface synthesis of GNRs is typically based on metal surface–assisted chemical reactions, where metallic substrates strongly screen their designer electronic properties and limit further applications. In this work, we present an on-surface synthesis approach to forming atomically precise GNRs directly on semiconducting metal oxide surfaces. The thermally triggered multistep transformations preprogrammed in our precursors’ design rely on highly selective and sequential activations of carbon-bromine (C-Br) and carbon-fluorine (C-F) bonds and cyclodehydrogenation. The formation of planar armchair GNRs terminated by well-defined zigzag ends is confirmed by scanning tunneling microscopy and spectroscopy, which also reveal weak interaction between GNRs and the rutile titanium dioxide substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Native Plasmid-Encoded Mercury Resistance Genes Are Functional and Demonstrate Natural Transformation in Environmental Bacterial Isolates

Plasmid-mediated horizontal gene transfer (HGT) is a major driver of genetic diversity in bacteria. We experimentally validated the function of a putative mercury resistance operon present on an abundant 8-kbp native plasmid found in groundwater samples without detectable levels of mercury. Phylogenetic analyses of the plasmid-encoded mercury reductases from the studied groundwater site show them to be distinct from those reported in proximal metal-contaminated sites. We synthesized the entire native plasmid and demonstrated that the plasmid was sufficient to confer functional mercury resistance in Escherichia coli . Given the possibility that natural transformation is a prevalent HGT mechanism in the low-cell-density environments of groundwaters, we also assayed bacterial strains from this environment for competence. We used the native plasmid-encoded metal resistance to design a screen and identified 17 strains positive for natural transformation. We selected 2 of the positive strains along with a model bacterium to fully confirm HGT via natural transformation. From an ecological perspective, the role of the native plasmid population in providing advantageous traits combined with the microbiome’s capacity to take up environmental DNA enables rapid adaptation to environmental stresses. Horizontal transfer of mobile genetic elements via natural transformation has been poorly understood in environmental microbes. Here, we confirm the functionality of a native plasmid-encoded mercury resistance operon in a model microbe and then query for the dissemination of this resistance trait via natural transformation into environmental bacterial isolates. We identified 17 strains including Gram-positive and Gram-negative bacteria to be naturally competent. These strains were able to successfully take up the plasmid DNA and obtain a clear growth advantage in the presence of mercury. Our study provides important insights into gene dissemination via natural transformation enabling rapid adaptation to dynamic stresses in groundwater environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

An Analytical Technology Platform to Identify Symbiotic Interactions Between PGPR and Root Microbiome Members from Biofuel Feedstocks

The goal of this STTR project was to establish a commericially-viable technology platform that can be used to develop formulations of plant growth promoting rhizobacteria (PGPR) that can be used as biofertilizers for improved production of woody biomass. The platform has been designed to screen thousands of different formulations of microorganisms in search for those formulations that best support the growth and survival of PGPR species. The focus of the Phase I project was to provide the existing technology with new capabilities that define its commerical pathway. These goals were to (1) adapt the existing platform for use with Populus isolates and (2) developing a highly-automated image analysis software tool that can rapidly screen large data sets to statistically identify optimized biofertilizer formulations. The project has produced a software routine for this screening and identification need, and has generated preliminary data for use of this approach to identify symbiotic PGPR communities that may be useful for promoting Populus growth.

09 BIOMASS FUELS↗
DOE OSTI2020

Green Factor

Los Alamos is designing a screening platform coupled to our biosensor to customize production processes in cell-free environments that offer biomanufacturers higher titers and yields, better resource utilization and cost-effective bioprocess. Our technology aims at enabling cell-free biosynthetic systems by providing enzyme variants generated by coupling with our biosensor technology, that can utilize cheap raw material cofactor analogues. This will enable bioprocesses based on cell free synthetic biology for a sustainable bioeconomy.

42 ENGINEERING↗