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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Interplay between Intrinsic Thermal Stability and Expansion Properties of Functionalized UiO-67 Metal–Organic Frameworks

The UiO family of metal-organic frameworks (MOFs) has been extensively studied for several applications owing to their high stability rendered by their robust secondary building units. The efficient design and use of these materials require a fundamental understanding of their thermal stability and its impact on chemical and structural functionality. In this work, we provide a detailed characterization of the intrinsic thermal behavior of the UiO-67 and functional analogues, UiO-67-NH 2 and UiO-67-CH 3 . Using in situ temperature-programmed X-ray diffraction, we find that distortion of the carboxylate group on the organic linker leads to negative thermal expansion (NTE) of the UiO-67 MOFs during heating. This NTE behavior is correlated with rich and reversible thermal changes observed in the MOF infrared spectral signature as samples are heated to the sample activation temperature (473 K). We find that in the absence of oxygen, activated UiO-67 samples show higher thermal stability compared to ambient or inert environments, with temperature-programmed desorption revealing an overall stability trend: UiO-67 > UiO-67- CH 3 > UiO-67-NH 2 . Two stages of change are observed during thermal treatment above 473 K, which are directly related to deformation of the inorganic node and the isotropic NTE behavior of these materials. Ultimately, these results provide a real-time interpretation of the fundamental thermoresponsive behavior of UiO-67 MOFs and offer a foundation for accurate interpretation of MOF interactions with guest molecules and their temperature dependence.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessing Composite Structure in Metal–Organic Framework-Polymer Mixed-Matrix Membranes

Metal-organic frameworks (MOFs) are renowned for their tunable structure, porosity, and internal chemistry, with demonstrated applications in molecular separations, storage, and conversion. While they are widely usable, the powdery characteristics of MOF materials can be limiting for large-scale processing and implementation in devices. Incorporating MOF particles into polymer supports affords engineering solutions to overcome these issues, yet the nature of the resulting composites is difficult to assess. Here, in this work, we present spectroscopic and calorimetric methods that we believe help establish a holistic physicochemical picture of the composite structure using a series of Zr MOFs with different pore sizes as a testbed. Power law decays are observed in X-ray scattering profiles in low q-space ranging between 2.4 and 3.3, which we interpret as changes in scattering due to polymer infiltrating MOF particles. This interpretation is supported by solid-state nuclear magnetic resonance spectroscopy and differential scanning calorimetry measurements that identify populations of the MOF-associated polymer. Additionally, positron annihilation lifetime spectroscopy measurements collected on a series of composites with different MOF-polymer ratios show multiple decay constants, each correlated to a different free volume elements. In combination with the spectroscopic, calorimetric, and scattering results, we utilize the trends in decay constants as a function of polymer mass fraction to hypothesize a polymer infiltration mechanism whereby large pores are preferentially filled, followed by small pores and, later still, interstitial spaces between particles. Even with vigorous investigation of polymer, MOF, and interface characteristics, the complex and heterogeneous nature of the composites makes absolute structural assertions difficult. We envision that the approaches demonstrated here will be a useful foundation to assess and ultimately guide the design of future MOF-polymer composites.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Soft X-ray Absorption Spectroscopy of Liquids and Solutions.

X-ray absorption spectroscopy (XAS) is an electronic absorption technique for which the initial state is a deeply buried core level. The photon energies corresponding to such transitions are governed primarily by the binding energies of the initial state. Because the binding energies of core electrons vary significantly among atomic species, this makes XAS an element-selective spectroscopy. Proper interpretation of XA spectra can provide detailed information on the local chemical and geometric environment of the target atom. The introduction of liquid microjet and flow cell technologies into XAS experiments has enabled the general study of liquid samples. Liquids studied to date include water, alcohols, and solutions with relevance to biology and energy technology. This Review summarizes the experimental techniques employed in XAS studies of liquid samples and computational methods used for interpretation of the resulting spectra and summarizes salient experiments and results obtained in the XAS investigations of liquids.

Smith, Jacob W↗
DOE OSTI2020

Methodology for the development of empirical models relating 13C NMR spectral features to fuel properties

Effective formulation of new gasoline or diesel fuels for internal combustion engines would benefit from the development of reliable models for predicting key fuel properties based on a set of molecular descriptors obtained from a single measurement. This is particularly relevant in the case of renewable fuels, where the available fuel sample quantity may be limited. In this work, we present a statistically-based methodology for building empirical models to predict multiple properties from one-dimensional 13C nuclear magnetic resonance (NMR) spectra measured on around 200 microliters of a liquid fuel. NMR spectra contain information about the molecular composition of a sample and the carbon types and molecular substructures therein. Our approach uses this information to build sparse, interpretable models, where the predicted properties are linked to specific molecular features. The approach takes into consideration the constrained nature of the features making up the one-dimensional NMR spectrum, which, after standardization, represent a relative fuel composition. We point to the limitations in interpretability that arise when building this type of empirical predictive model and suggest how these limitations may be diminished. Among the many properties important for maximizing engine performance and minimizing emissions, we build models that predict derived cetane number and distillation temperatures as these are of particular interest because of their links to fuel economy, drivability, and engine-out emissions. Results suggest that the properties of interest may be impacted by only a few of the 27 13C NMR regions represented in the data, pointing to new directions for further testing in the development of improved fuels.

predictive models, biofuels., NMR Analysis↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Trace Uranium Partitioning in a Multiphase Nano-FeOOH System

The characterization of trace elements in nanomaterials using extended X-ray absorption fine structure (EXAFS) spectroscopy constitutes a first step toward understanding how impurities or dopants affect the properties of the host phase. However, limitations to EXAFS interpretation complicate the analysis of trace concentrations of impurities that are distributed across multiple phases in a heterogeneous system. Ab initio molecular dynamics (AIMD)-informed EXAFS analysis was employed to investigate the immobilization of trace uranium associated with nanophase iron (oxyhydr)oxides, a model system for the geochemical sequestration of radiotoxic contaminants. The reductive transformation of ferrihydrite (Fe(OH)3) to nano-particulate iron oxyhydroxide minerals in the presence of uranyl (UO2)2+(aq) resulted in the preferential incorporation of U into goethite (a-FeOOH) over lepidocrocite (g-FeOOH), even though reaction conditions favored the formation of excess lepidocrocite. This unexpected result is supported by atomically resolved transmission electron microscopy. Using this model system, we demonstrate how AIMD-informed EXAFS analysis lifts the strict statistical limitations of traditional shell-by-shell EXAFS modeling, enabling the detailed analysis of the local bonding environment, charge compensation mechanisms, and oxidation states of polyvalent impurities in complex multi-phase nano-systems.

ab initio molecular dynamics↗
DOE OSTI2017

Growth Kinetics and Size Distribution Dynamics of Viscous Secondary Organic Aerosol

Low bulk diffusivity inside viscous semisolid atmospheric secondary organic aerosol (SOA) can prolong equilibration time scale, but its broader impacts on aerosol growth and size distribution dynamics are poorly understood. In this article, we present quantitative insights into the effects of bulk diffusivity on the growth and evaporation kinetics of SOA formed under dry conditions from photooxidation of isoprene in the presence of a bimodal aerosol consisting of Aitken (ammonium sulfate) and accumulation (isoprene or α-pinene SOA) mode particles. Aerosol composition measurements and evaporation kinetics indicate that isoprene SOA is composed of several semivolatile organic compounds (SVOCs), with some reversibly reacting to form oligomers. Model analysis shows that liquid-like bulk diffusivities can be used to fit the observed evaporation kinetics of accumulation mode particles but fail to explain the growth kinetics of bimodal aerosol by significantly under-predicting the evolution of the Aitken mode. In contrast, the semisolid scenario successfully reproduces both evaporation and growth kinetics, with the interpretation that hindered partitioning of SVOCs into large viscous particles effectively promotes the growth of smaller particles that have shorter diffusion time scales. This effect has important implications for the growth of atmospheric ultrafine particles to climatically active sizes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Theoretical Evaluation of Metal–Ligand Bonding in Neptunium Compounds in Relation to 237 Np Mössbauer Spectroscopy

The 237 Np Mössbauer isomer shift and quadrupole splitting (QS) are powerful probes for the metal–ligand bonding of neptunium, a 5f-element of vital importance in the nuclear fuel cycle. A large set of Np compounds with different oxidation states (III) to (VII) is studied to investigate, by first-principles calculations, isomer shifts and the QS trends in relation to the Np oxidation state. Natural Bond Orbital analysis reveals that in addition to donation bonding to the 5f shell, participation of the 6d and 7s neptunium shells in covalent (donation) bonding substantially impacts the isomer shifts. The isomer shift cannot be interpreted solely by the 5f shell electron count. The isomer shift for Np(II) compounds is estimated to be in the range of 31–34 mm/s, less positive than for Np(III) compounds. For the QS, density functional calculations fail to reproduce the quadrupole splitting for some Np(VI) ionic solids. A multiconfigurational wave function approach reproduces the observed QS trends. The calculations give a semiquantitative interpretation of the trends for Np oxidation states (V) to (VII). Furthermore, the contrasting QS for standard and “reverse” neptunyl(VI), at the opposite extremes of the observed QS scale, arises predominantly from the different crystal environments.

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

Computationally-Guided Assignment of Unexpected Signals in the Raman Spectra of Uranyl Triperoxide Complexes

Combination of uranium, peroxide, and mono- (Na, K) or divalent (Mg, Ca, Sr) cations under alkaline aqueous conditions results in the rapid formation of anionic uranyl triperoxide monomers (UTs), (UO 2 (O 2 ) 3 ) 4– , exhibiting unique Raman signatures. Electronic structure calculations were decisive for the interpretation of the spectra and assignment of unexpected signals associated with vibrations of the uranyl and peroxide ions. Finally, assignments were verified by 18 O isotopic labeling of the uranyl ions supporting the computational-based interpretation of the experimentally observed peaks and the assignment of a novel asymmetric vibration of the peroxide ligands, v2 (O 2 2– ).

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary- structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. Here, we introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehydrogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 Å, map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

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

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

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

Data-Driven Kinetic Reaction Networks for Separation Chemistry

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. Here, in this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO 2 (NO 3 ) 2 (DEHiBA) 2 , agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

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

The Interplay of Pauli Repulsion, Electrostatics, and Field Inhomogeneity for Blueshifting and Redshifting Vibrational Probe Molecules

Many molecules' vibrational frequencies are sensitive to intermolecular electric fields, enabling them to probe the field in complex molecular environments. However, it is often unclear whether the probe is responding to the local electric field or other types of intermolecular interactions, inhibiting interpretation of the frequency and effectiveness as probes. This is especially true for molecules whose vibrational frequencies blueshift instead of the more typical redshift in hydrogen bonding configurations. In this work, we computationally investigate the causes of redshifting versus blueshifting over a range of vibrational reporters. First, we apply adiabatic energy decomposition analysis to a paradigmatic set of probes, finding that redshifting only occurs when electrostatic interactions are strong enough to overcome the dominant and large blueshifting contribution of Pauli repulsion. Furthermore, we demonstrate that field inhomogeneity can further shift the frequency of many probes substantially to either reinforce or counteract the shift expected from a homogeneous field. We find that redshifting is reinforced by electric field inhomogeneity, otherwise field inhomogeneity further weakens the electrostatic contribution relative to Pauli repulsion, leading to blueshifting. Further calculations indicate that the probe's response to field inhomogeneity can be understood by considering the mass of the atoms involved in the stretching mode and sign of the electric field. In explaining the interplay of different intermolecular interactions and field inhomogeneity for many probes, our results should enable the use and interpretation of spectroscopic probes and their connection to electric fields in more complex systems.

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

Reactive Molecular Dynamics Simulations and Quantum Chemistry Calculations To Investigate Soot-Relevant Reaction Pathways for Hexylamine Isomers

Sooting tendencies of a series of nitrogen-containing hydrocarbons (NHCs) have been recently characterized experimentally using the yield sooting index (YSI) methodology. This work aims to identify soot-relevant reaction pathways for three selected C6H15N amines, namely, dipropylamine (DPA), diisopropylamine (DIPA), and 3,3-dimethylbutylamine (DMBA) using ReaxFF molecular dynamics (MD) simulations and quantum mechanical (QM) calculations and to interpret the experimentally observed trends. ReaxFF MD simulations are performed to determine the important intermediate species and radicals involved in the fuel decomposition and soot formation processes. QM calculations are employed to extensively search for chemical reactions involving these species and radicals based on the ReaxFF MD results and also to quantitatively characterize the potential energy surfaces. Specifically, ReaxFF simulations are carried out in the NVT ensemble at 1400, 1600, and 1800 K, where soot has been identified to form in the YSI experiment. These simulations account for the interactions among test fuel molecules and pre-existing radicals and intermediate species generated from rich methane combustion, using a recently proposed simulation framework. ReaxFF simulations predict that the reactivity of the amines decrease in the order DIPA > DPA > DMBA, independent of temperature. Both QM calculations and ReaxFF simulations predict that C2H4, C3H6, and C4H8 are the main nonaromatic soot precursors formed during the decomposition of DPA, DIPA, and DMBA, respectively, and the associated reaction pathways are identified for each amine. Both theoretical methods predict that sooting tendency increases in the order DPA, DIPA, and DMBA, consistent with the experimentally measured trend in YSI. This work demonstrates that sooting tendencies and soot-relevant reaction pathways of fuels with unknown chemical kinetics can be identified efficiently through combined ReaxFF and QM simulations. Overall, predictions from ReaxFF simulations and QM calculations are consistent, in terms of fuel reactivity, major intermediates, and major nonaromatic soot precursors.

fuel decomposition↗
DOE OSTI2023

Resolving Discrepancies between State-of-the-Art Theory and Experiment for HO 2 + HO 2 via Multiscale Informatics

Recent high-level theoretical calculations predict a mild temperature dependence for HO 2 + HO 2 inconsistent with state-of-the-art experimental determinations that upheld the stronger temperature dependence observed in early experiments. Via MultiScale Informatics analysis of the theoretical and experimental data, we identified an alternative interpretation of the raw experimental data that uses HO 2 + HO 2 rate constants nearly identical to theoretical predictions---implying that the theoretical and experimental data are actually consistent, at least when considering the raw data from experimental studies. Here, similar analyses of typical signals from low-temperature experiments indicate that an HOOOOH intermediate---identified by recent theory but absent from earlier interpretations---yields modest effects that are smaller than, but may have contributed to, the scatter in data among different experiments. More generally, the findings demonstrate that modern chemical theories and experiments have progressed to a point where meaningful comparison requires joint consideration of their data simultaneously.

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

Distinguishing Surface and Bulk Reactivity: Concentration-Dependent Kinetics of Iodide Oxidation by Ozone in Microdroplets

Iodine oxidation reactions play an important role in environmental, biological, and industrial contexts. The multiphase reaction between aqueous iodide and ozone is of particular interest due to its prevalence in the marine atmosphere and unique reactivity at the air–water interface. Here, we explore the concentration dependence of the I – + O 3 reaction in levitated microdroplets under both acidic and basic conditions. To interpret the experimental kinetics, molecular simulations are used to benchmark a kinetic model, which enables insight into the reactivity of the interface, the nanometer-scale subsurface region, and the bulk interior of the droplet. For all experiments, a kinetic description of gas- and liquid-phase diffusion is critical to interpreting the results. We find that the surface dominates the iodide oxidation kinetics under concentrated and acidic conditions, with the reactive uptake coefficient approaching an upper limit of 10 –2 at pH 3. In contrast, reactions in the subsurface dominate under more dilute and alkaline conditions, with inhibition of the surface reaction at pH 12 and an uptake coefficient that is 10× smaller. In conclusion, the origin of a changing surface mechanism with pH is explored and compared to previous ozone-dependent measurements.

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

Simulations of Attosecond Metallization in Quartz and Diamond Probed with Inner-Shell Transient Absorption Spectroscopy

When dielectrics are hit with intense infrared (IR) laser pulses, transient metalization can occur. The initial attosecond dynamics behind this metallization are not entirely understood. Therefore, simulations are needed to understand this process and to help interpret experimental observations of it, such as with attosecond transient absorption (ATA). In this paper, we present first-principles simulations of ATA based on bulk-mimicking clusters and real-time time-dependent density functional theory (RT-TDDFT), with Koopmans-tuned range-separated hybrid functionals and Gaussian basis sets. Our method gives good agreement with the experiment for the breakdown threshold in silica and diamond. This breakdown voltage corresponds to a Keldysh parameter of approximately one and thus involves a transition to a regime where the dynamics are driven by tunneling. Pumping at an amplitude just below this value causes a mixture of multiphoton and tunneling excitations across the band gap to occur. The computed extreme ultraviolet and X-ray attosecond transient spectra also agree well with the experiment and show a decrease in optical density due to the transient population of the conduction band from the IR field. First-principles approaches such as this are valuable for interpreting the complicated modulations in a spectrum and for guiding future attosecond experiments on solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

New Insights into Low-Temperature Oxidation of Propane from Synchrotron Photoionization Mass Spectrometry and Multiscale Informatics Modeling

We investigated the low-temperature oxidation of propane at 4 Torr and temperatures of 530, 600, and 670 K. The oxidation is initiated by pulsed laser photolysis of oxalyl chloride, (COCl)2, at 248 nm, which rapidly generates a ~1:1 mixture of 1-propyl (n-propyl) and 2-propyl (i-propyl) radicals via the fast Cl + propane reaction. Reactants, intermediates and products are probed with isomeric selectivity by time-resolved multiplexed photoionization mass spectrometry (MPIMS) with tunable synchrotron vacuum UV radiation as the ionization source. At all three temperatures, the major stable product species is propene, formed in the C3H7 + O2 reactions by direct HO2-elimination from both n- and i-propyl peroxy radicals. The experimentally derived propene yields relative to the initial concentration of Cl atoms are (20 ± 4)% at 530 K, (55 ± 11)% at 600 K, and (86 ± 17)% at 670 K at a reaction time of 20 ms. The lower yield of propene at low temperature reflects substantial formation of propyl peroxy radicals, which do not completely decompose on the experimental time scale. In addition, we detect the C3H6O isomers methyloxirane, oxetane, acetone and propanal as minor products. Our measured yields of oxetane and methyloxirane, which are co-products of OH radicals, suggest a revision of the OH formation pathways in models of low-temperature propane oxidation. The experimental results are modeled and interpreted using a multi-scale informatics approach that is presented in detail in a separate publication (Burke, M. P.; Goldsmith, C. F.; Klippenstein, S. J.; Welz, O.; Huang H.; Antonov I. O.; Savee J. D.; Osborn D. L.; Zádor, J.; Taatjes, C. A.; Sheps, L., Multi-Scale Informatics for Low-Temperature Propane Oxidation: Further Complexities in Studies of Complex Rections, submitted, 2015). The model predicts the time profiles and yields of the experimentally observed primary products well, and shows satisfactory agreement for products formed mostly via secondary radical-radical reactions.

Combustion↗