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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Can we observe the ion-neutral drift velocity in prestellar cores?

Given the low-ionization fraction of molecular clouds, ambipolar diffusion is thought to be an integral process in star formation. However, chemical and radiative-transfer effects, observational challenges, and the fact that the ion-neutral drift velocity is inherently very small render a definite detection of ambipolar diffusion extremely non-trivial. Here, we study the ion-neutral drift velocity in a suite of chemodynamical, non-ideal magnetohydrodynamic (MHD), two-dimensional axisymmetric simulations of prestellar cores where we alter the temperature, cosmic-ray ionization rate, visual extinction, mass-to-flux ratio, and chemical evolution. Subsequently, we perform a number of non-local thermodynamic equilibrium (non-LTE) radiative-transfer calculations considering various idealized and non-idealized scenarios in order to assess which factor (chemistry, radiative transfer, and/or observational difficulties) is the most challenging to overcome in our efforts to detect the ion-neutral drift velocity. We find that temperature has a significant effect in the amplitude of the drift velocity with the coldest modelled cores (T = 6 K) exhibiting drift velocities comparable to the sound speed. Against expectations, we find that in idealized scenarios (where two species are perfectly chemically co-evolving) the drift velocity ‘survives’ radiative-transfer effects and can in principle be observed. However, we find that observational challenges and chemical effects can significantly hinder our view of the ion-neutral drift velocity. Finally, we propose that HCN and HCNH + ⁠, being chemically co-evolving, could be used in future observational studies aiming to measure the ion-neutral drift velocity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Structures of methane and ammonia monooxygenases in native membranes

Methane- and ammonia-oxidizing bacteria play key roles in the global carbon and nitrogen cycles, respectively. These bacteria use homologous copper membrane monooxygenases to accomplish the defining chemical transformations of their metabolisms: the oxidations of methane to methanol by particulate methane monooxygenase (pMMO) and ammonia to hydroxylamine by ammonia monooxygenase (AMO), enzymes of prime interest for applications in mitigating climate change. However, investigations of these enzymes have been hindered by the need for disruptive detergent solubilization prior to structure determination, confounding studies of pMMO and precluding studies of AMO. Here, we overcome these challenges by using cryoEM to visualize pMMO and AMO directly in their native membrane arrays at 2.4 to 2.8 Å resolution. These structures reveal details of the copper centers, numerous bound lipids, and previously unobserved components, including identifiable and distinct supernumerary helices interacting with pMMO and AMO, suggesting a widespread role for these helices in copper membrane monooxygenases. Comparisons between these structures, their metallocofactors, and their unexpected protein–protein interactions highlight features that may govern activity or the formation of higher-order arrays in native membranes. The ability to obtain molecular insights within the native membrane will enable further understanding of these environmentally important enzymes.

Science & Technology - Other Topics↗
DOE OSTI2024

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Imaging beyond the surface region: Probing hidden materials via atomic force microscopy

Probing material properties at surfaces down to the single-particle scale of atoms and molecules has been achieved, but high-resolution subsurface imaging remains a nanometrology challenge due to electromagnetic and acoustic dispersion and diffraction. The atomically sharp probe used in scanning probe microscopy (SPM) has broken these limits at surfaces. Subsurface imaging is possible under certain physical, chemical, electrical, and thermal gradients present in the material. Of all the SPM techniques, atomic force microscopy has entertained unique opportunities for nondestructive and label-free measurements. Here, we explore the physics of the subsurface imaging problem and the emerging solutions that offer exceptional potential for visualization. We discuss materials science, electronics, biology, polymer and composite sciences, and emerging quantum sensing and quantum bio-imaging applications. The perspectives and prospects of subsurface techniques are presented to stimulate further work toward enabling noninvasive high spatial and spectral resolution investigation of materials including meta- and quantum materials.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

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

Complex water networks visualized by cryogenic electron microscopy of RNA

The stability and function of biomolecules are directly influenced by their myriad interactions with water. Here we investigated water through cryogenic electron microscopy (cryo-EM) on a highly solvated molecule: the Tetrahymena ribozyme. By using segmentation-guided water and ion modelling (SWIM), an approach combining resolvability and chemical parameters, we automatically modelled and cross-validated water molecules and Mg 2+ ions in the ribozyme core, revealing the extensive involvement of water in mediating RNA non-canonical interactions. Unexpectedly, in regions where SWIM does not model ordered water, we observed highly similar densities in both cryo-EM maps. In many of these regions, the cryo-EM densities superimpose with complex water networks predicted by molecular dynamics, supporting their assignment as water and suggesting a biophysical explanation for their elusiveness to conventional atomic coordinate modelling. Our study demonstrates an approach to unveil both rigid and flexible waters that surround biomolecules through cryo-EM map densities, statistical and chemical metrics, and molecular dynamics simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Catalight─An Open-Source Automated Photocatalytic Reactor Package Illustrated through Plasmonic Acetylene Hydrogenation

An open-source and modular Python package, Catalight, is developed and demonstrated to automate (photo)catalysis measurements. (Photo)catalysis experiments require studying several parameters to evaluate performance, including the temperature, gas flow rate and composition, illumination power, and spectral profile. Catalight orchestrates measurements over this complicated parameter space and systematically stores, analyzes, and visualizes the results. To showcase the capabilities of Catalight, we perform an automated apparent activation barrier measurement of acetylene hydrogenation over a plasmonic AuPd catalyst on an Al 2 O 3 support, simultaneously varying laser power, wavelength, and temperature in a multiday experiment controlled by a simple Python script. Our chemical results unexpectedly show an increased activation barrier upon light excitation, contrary to previous findings for other plasmonic reactions and catalysts. We show that the reaction rate order with respect to both acetylene and hydrogen remains unchanged upon illumination, suggesting that molecular surface coverage is not changed by light. By analyzing the inhomogeneity of the laser-induced heating, we attribute these results to a partial photothermal effect combined with a photochemical/hot electron-driven mechanism. In conclusion, our findings highlight the capabilities of a new experiment automation tool; explore the photocatalytic mechanism for an industrially relevant reaction; and identify systematic sources of error in canonical photocatalysis experimental procedures.

Catalysts↗
DOE OSTI2025

Investigating intermolecular interactions among CO 2 , water and PEEK-ionene membrane using cryo ToF-SIMS and isotopic labeling

Cryogenic time-of-flight secondary ion mass spectrometry (cryo ToF-SIMS) has emerged as a powerful tool for investigating molecular interactions, speciation, and dynamics in materials for CO 2 capture. In this study, we apply cryo ToF-SIMS to probe interactions between CO 2 , water, and PEEK-ionene membranes—a promising material for direct CO 2 capture due to its selectivity, durability, and efficiency. Despite this potential, the mechanisms governing CO 2 diffusion and the influence of water vapor on CO 2 behavior remain unclear. To address this, we loaded PEEK-ionene membranes with 13 CO 2 and D 2 O and employed cryo ToF-SIMS to visualize the 3D distribution of CO 2 and water within the membrane. While prior studies suggest that 13 CO 2 is absorbed under ambient conditions, our cryo ToF-SIMS analysis revealed no enhancement of the 13 C/ 12 C ratio, suggesting weak CO 2 -membrane interactions. As a result, CO 2 vaporizes even at low temperatures (−140°C) under vacuum conditions. In contrast, D 2 O displayed a relatively homogeneous distribution in the membrane, suggesting stronger water-membrane interactions via hydrogen bonding (18–20 kJ/mol). Interestingly, CO 2 was not detected in D 2 O-loaded membranes, indicating minimal interference from water vapor on CO 2 diffusion. As a comparison, the cryo ToF-SIMS data show that 13 CO 2 can readily react with a basic Na 2 CO 3 aqueous solution to form NaH 13 CO 3 . These findings demonstrate cryo ToF-SIMS as a critical technique for understanding gas-water-membrane interactions, offering insights for membrane functionalization to improve CO 2 capture efficiency.

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

Organic Molecules Mimic Alkali Metals Enabling Spontaneous Harpoon Reactions with Halogens

Abstract The harpoon mechanism has been a milestone in molecular reaction dynamics. Until now, the entity from which electron harpooning occurs has been either alkali metal atoms or non‐metallic analogs in their excited states. In this work, we demonstrate that a common organic molecule, octamethylcalix[4] pyrrole (omC4P), behaves just like alkali metal atoms, enabling the formation of charge‐separated ionic bonding complexes with halogens omC4P + ⋅ X − ( X =F−I, SCN) via the harpoon mechanism. Their electronic structures and chemical bonding were determined by cryogenic photoelectron spectroscopy of the corresponding anions and confirmed by theoretical analyses. The omC4P + ⋅ X − could be visualized to form from the reactants omC4P+ X via electron harpooning from omC4P to X at a distance defined by the energy difference between the ionization potential of omC4P and electron affinity of X .

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

Solvation Environment and Interface Dynamics of Li 2 B 12 H 12 and Li 2 B 12 F 12 Electrolytes Uncovered by Theory and Operando Optical and FTIR Spectroelectrochemistry

In this work, we evaluated two closo-borate salts (Li 2 B 12 H 12 and Li 2 B 12 F 12 ) in propylene carbonate from theoretical and experimental perspectives to understand how the coordination environment influences their spectroscopic and electrochemical properties. The coordination environments of the closo-borate salts were modeled via density functional theory (DFT) and molecular dynamics (MD). Vibrational spectra calculated from the predicted coordination environments are in agreement with experimentally measured steady-state FTIR data. This theoretical investigation also suggested that Li 2 B 12 F 12 would possess a higher ionic conductivity than Li 2 B 12 H 12 , which was corroborated experimentally. Additionally, an electrochemical cell was designed and fabricated that enabled operando optical and FTIR spectroelectrochemical (OP-IR-SEC) measurements. This allowed for the simultaneous measurement of the relative changes of species at a lithium electrode-liquid electrolyte interface and the visualization of lithium plating at the electrode surface. This technique could provide new chemical insights and potentially link optical changes at the electrode–electrolyte interface to specific chemical species in similar electrochemical systems. The Li 2 B 12 F 12 electrolyte was found to have a higher thermal stability, which may find utility in applications for batteries that are subject to high-temperature conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Visualization of Aluminum Ions at the Mica Water Interface Links Hydrolysis State-to-Surface Potential and Particle Adhesion

When hydrolyzable cations such as aluminum interact with solid-water interfaces, macroscopic interfacial properties (e.g., surface charge and potential) and interfacial phenomena (e.g., particle adhesion) become tightly linked with the microscopic details of ion adsorption and speciation. We use in situ atomic force microscopy to directly image individual aluminum ions at a mica-water interface and show how adsorbate populations change with pH and aluminum activity. Complementary streaming potential measurements then allow us to build a triple layer model (TLM) that links surface potentials to adsorbate populations, via equilibrium binding constants. Our model predicts that hydrolyzed species dominate the mica-water interface, even when unhydrolyzed species dominate the solution. Ab initio molecular dynamics (AIMD) simulations confirm that aluminum hydrolysis is strongly promoted at the interface. The TLM indicates that hydrolyzed adsorbates are responsible for surface-potential inversions, and we find strong correlations between hydrolyzed adsorbates and particle-adhesion forces, suggesting that these species mediate adhesion by chemical bridging.

Legg, Benjamin A.↗
DOE OSTI2021

Ultrafast visualization of phase transitions in nonequilibrium warm dense matter

Intense ultrafast laser excitation brings materials into highly nonequilibrium states with complex solid–liquid phase transitions. These ultrafast processes yield warm dense matter (WDM) conditions characterized by comparable thermal and Fermi energies, and strong ion–ion coupling. Here, we review recent studies employing mega-electron-volt ultrafast electron diffraction to resolve the structural dynamics of nonequilibrium WDM created by femtosecond laser heating of solids. Furthermore, for the study of warm dense gold, the results showed homogeneous melting that occurs within tens of picoseconds at absorbed energy densities of 0.4–1.4 MJ/kg. These results constrained the electron–ion coupling rate and revealed the melting sensitivity to nucleation seeds. For the study of radiation-damaged tungsten, the results showed a melting transition below the melting temperature. Molecular dynamics simulations suggested that this melting behavior is driven by vacancy defect clusters from radiation damage. These studies provided atomic-level insights into the melting behavior of materials under extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Interactions of clathrate hydrate promoters sodium dodecyl sulfate and tetrahydrofuran investigated using 1H diffusion nuclear magnetic resonance at hydrate-forming conditions

Thermodynamic hydrate promoters and kinetic hydrate promoters can be used to reduce the P–T conditions for clathrate hydrate synthesis to decrease the nucleation induction time while increasing growth rates. Two commonly used promoters for hydrate research are tetrahydrofuran (THF) and sodium dodecyl sulfate (SDS), which can increase the overall hydrate promotion when used in tandem as compared to individually. There are several molecular theories regarding how SDS promotes hydrate growth. This study explores the micellular theory, for which hydrate formation depends on surfactant aggregates (micelles) at a critical micelle concentration (CMC) to increase the interfacial surface area. The micellular theory is the most investigated and criticized surfactant hydrate promotion theory. To address questions related to micellar behavior, this study investigates the intermolecular behavior between SDS and THF for the identification of micelles at hydrate-forming conditions. The systems explored contained THF at 3 and 5 wt. % with varying concentrations of SDS below and above the CMC. Several methods including a qualitative visual method, conductivity, interfacial tensiometry, 13C Liquid-state Nuclear Magnetic Resonance (NMR) spectroscopy, and 1H diffusion NMR spectroscopy were evaluated at temperatures below the Krafft point of SDS and above 0 °C. The presence of THF at low concentrations decreased the critical temperature for the formation of SDS micelles, where SDS is solubilized in THF/water solution at hydrate-forming temperatures without precipitation. The CMC of SDS was decreased significantly even at hydrate-forming conditions. Mixed surfactant–cosolvent micellular behavior of SDS in the presence of low concentrations of THF was confirmed at hydrate-forming conditions above 0 °C.

Chemistry↗
DOE OSTI2020

Endophyte-Promoted Phosphorus Solubilization in Populus

Phosphorus is an important plant nutrient, but in soils the majority of phosphorus, present in the form of a phosphate, forms metal complexes in soil making it relatively unavailable to plants. Therefore, inorganic phosphate solubilization is an important process of plant growth promotion by plant associated bacteria and fungi. Non nodulating plant species have been shown to thrive in low-nutrient environments, in some instances by relying on plant associated microorganisms called endophytes. These microorganisms live within the plant and help supply nutrients for the plant. Despite their potential enormous environmental importance, there are a limited number of studies looking at the direct molecular impact of phosphate solubilizing endophytic bacteria on the host plant. In this work, we studied the impact of two endophyte strains of wild poplar (Populus trichocarpa) that solubilize phosphate. Using a combination of x-ray imaging, spectroscopy methods, and proteomics we report direct evidence of endophyte-promoted phosphorus uptake in poplar. We found that the solubilized phosphate inside the plant may react and become insoluble again suggesting that the phosphate could be re-released within the plant by endophytes. Using synchrotron x-ray fluorescence spectromicroscopy combined with x-ray absorption near-edge structure, we visualized the nutrient phosphorus inside poplar roots inoculated by the selected endophytes and found the phosphorus in both forms of organic and inorganic phosphates inside the root. Tomography-based root imaging revealed a markedly different root biomass and root architecture for poplar samples inoculated with the phosphate solubilizing bacteria strains. Proteomics characterization on poplar roots coupled with protein network analysis revealed novel proteins and metabolic pathways with possible involvement in endophyte enriched phosphorus uptake. These findings suggest an important role of endophytes for phosphorus acquisition and provide a deeper understanding of the critical symbiotic associations between poplar and the endophytic bacteria.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Hunting for Planetary Nebulae toward the Galactic Center

We present near-infrared (IR) spectra of two planetary nebula (PN) candidates in close lines of sight toward the Galactic center (GC) using the Gemini Near-Infrared Spectrograph at Gemini North. High-resolution images from radio continuum and narrowband IR observations reveal ringlike or barrel-shaped morphologies of these objects, and their mid-IR spectra from the Spitzer Space Telescope exhibit rich emission lines from highly-excited species such as [S iv], [Ne iii], [Ne v], and [O iv]. We also derive elemental abundances using the Cloudy synthetic models, and find an excess amount of the s-process element krypton in both targets, which supports their nature as PNe. We estimate foreground extinction toward each object using near-IR hydrogen recombination lines, and find significant visual extinctions (A {sub V} > 20). The distances inferred from the size versus surface brightness relation of other PNe are 9.0 ± 1.6 kpc and 7.6 ± 1.6 kpc for SSTGC 580183 and SSTGC 588220, respectively. These observed properties along with the abundance patterns and close proximity to Sgr A* (projected distances ≲20 pc) make it highly probable that these objects are the first confirmed PN objects in the nuclear stellar disk. The apparent scarcity of such objects resembles the extremely low rate of PN formation in old stellar systems, but is in line with the current rate of sustained star formation activity in the Central Molecular Zone.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

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

Near-Surface Imaging of the Multicomponent Gas Phase above a Silver Catalyst during Partial Oxidation of Methanol

Fundamental chemistry in heterogeneous catalysis is increasingly explored using operando techniques in order to address the pressure gap between ultrahigh vacuum studies and practical operating pressures. Because most operando experiments focus on the surface and surface-bound species, there is a knowledge gap of the near-surface gas phase and the fundamental information the properties of this region convey about catalytic mechanisms. In this work, we demonstrate in situ visualization and measurement of gas-phase species and temperature distributions in operando catalysis experiments using complementary near-surface optical and mass spectrometry techniques. The partial oxidation of methanol over a silver catalyst demonstrates the value of these diagnostic techniques at 600 Torr (800 mbar) pressure and temperatures from 150 to 410 °C. Planar laser-induced fluorescence provides two-dimensional images of the formaldehyde product distribution that show the development of the boundary layer above the catalyst under different flow conditions. Raman scattering imaging provides measurements of a wide range of major species, such as methanol, oxygen, nitrogen, formaldehyde, and water vapor. Near-surface molecular beam mass spectrometry enables simultaneous detection of all species using a gas sampling probe. Detection of gas-phase free radicals, such as CH 3 and CH 3 O, and of minor products, such as acetaldehyde, dimethyl ether, and methyl formate, provides insights into catalytic mechanisms of the partial oxidation of methanol. The combination of these techniques provides a detailed picture of the coupling between the gas phase and surface in heterogeneous catalysis and enables parametric studies under different operating conditions, which will enhance our ability to constrain microkinetic models of heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗