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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Calibrating Bayesian generative machine learning for Bayesiamplification

Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Generalized Integral Boundary Layer Equations in Streamline Curvilinear Coordinates

The MEIT equations are derived in orthogonal streamline curvilinear coordinates defined by the inviscid velocity at the wall and the surface-normal direction. The resulting formulations is (1) is not restricted to flat-plate or axisymmetric geometries, (2) accommodates vortical inviscid flows (e.g., induced by curved shocks) and wall mass blowing, and (3) allows for an arbitrary definition of the edge state. The streamline ODEs are also extended to a surface PDE framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Proactive Intrusion Detection and Mitigation System

SAND2023-05661O The proactive intrusion detection and mitigation system (PIDMS) provides grid-edge situational awareness for cybersecurity defense by capturing real-time distributed energy resource (DER) network traffic and performance data with a novel approach that improves the detection and prevention of cyber-physical attacks. The PIDMS addresses the grid-edge security gap with real-time analysis of both network traffic and photovoltaic performance data to deliver a novel, cyber-physical intrusion detection system (IDS) approach that increases the accuracy and effectiveness of detection and mitigation. This hybrid IDS analysis enables dual monitoring that increases the workload of the adversary; both cyber and physical data would have to be simultaneously spoofed to evade detection. Furthermore, monitoring and analyzing cyber data are insufficient in some cases. For example, in an insider threat aimed at disrupting inverter grid-support functions where proper credentials and authentication are achieved, only the altered PV performance would indicate abnormal behavior. All in all, the PIDMS provides novel capabilities for: • Distributed, real-time cyber-physical detection and mitigation analysis • Cybersecurity defense for grid-edge systems • Analysis framework that can provide situational awareness across the transmission, distribution, and DER systems The PIDMS sensor is designed to collect cyber-physical data, process the data using machine-learning algorithms, detect abnormal events, and deploy mitigations. With these goals, the main functional PIDMS objectives are: • Capability to collect cyber-physical data • Onboard storage of cyber-physical data • Peer-to-peer communication • Computationally efficient machine-learning algorithms • Online cyber-physical data analysis • Alerting/visualization capabilities • Mitigation deployment capability with bump-in-the-wire (BITW) implementation Each of these functional objectives enable PIDMS to perform effective cyber-physical intrusion detection and mitigation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Jones, Christian↗
DOE OSTI2025

Evaluating the Impact of Tritium Permeation Membrane Performance and Direct Internal Recycling on Fusion Fuel Cycle Efficiency Using TMAP8

An efficient fuel cycle is vital to sustainable and cost-effective energy generation in fusion systems. Since tritium is not widely available, fusion systems must breed their own tritium for sustainable fusion deuterium-tritium reactions. An inefficient fuel cycle increases the tritium inventory needed for operations, which increases costs, constraints on tritium management systems, and safety concerns. A fuel cycle model is a powerful tool for understanding tritium inventories and flow rates across all systems in the fuel cycle. By simplifying the technical details into time-dependent tritium flow rates and inventories, the model can simulate the entire fuel cycle with high computational efficiency, even for technologies that are still under development. It can therefore quantify the impact of new tritium management technologies on fuel cycle efficiency. To evaluate the impact of key components on reducing tritium inventory, we are using and expanding an existing fuel cycle models based on latest advancements in fuel cycle research. The new model integrates Tritium Permeation Membrane (TPM) and Direct Internal Recycling (DIR) to enhance tritium transport from blanket breeders and plasma exhaust. These fuel cycle models are implemented in TMAP8 (Tritium Migration Analysis Program, version 8), a MOOSE-based open-source application designed to provide cutting-edge capabilities for tritium transport and fuel cycle modeling. The study aims to demonstrate the extensibility of existing fuel cycle modeling capability in TMAP8 and to offer a proof-of-principle design for future fusion plant systems. The presentation will cover the performance of fuel cycle modeling capabilities available in TMAP8, highlight advancements in fuel cycle research, and present a sensitivity analysis of these models. The results underline potential approaches and technology solutions to lower tritium inventory requirements, highlighting their role in shaping the future of fusion energy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Revolutionizing thermal Management in Next-Generation AI data centers: Challenges and breakthrough innovations

Data centers (DCs) serve as critical infrastructure for powering the growth and evolution of AI. Next-generation AI DCs present unique challenges in thermal management driven by unprecedented computational demands. This paper provides a comprehensive summary of key stakeholder perspectives on technology gaps, infrastructure requirements, test bed needs, emerging opportunities, and preliminary solutions related to thermal management for AI DCs. It establishes six strategic pillars of thermal management for next generation AI DC: reliability, deployability, efficiency, resilience, measurability, and valorization. The discussion spans a range of critical topics, including advanced cooling technologies, thermal strategies for emerging modular and edge DCs, system-level optimization and control frameworks, infrastructure planning and grid integration designs, benchmarking approaches, and pathways for waste heat recovery and reuse. The proposed research, development, and demonstration efforts are aimed at accelerating the deployment of AI DCs while ensuring energy efficiency, reliability, safety, and regulatory compliance.

Wang, Pengtao [ORNL] (ORCID:0000000214713429)↗
DOE OSTI2020

Atomic-Scale Structural Mapping of Active Sites in Monolayer PGM-Free Catalysts by Low-Voltage 4D-STEM

Two-dimensional (2D) materials have attracted a large amount of attention in both basic and applied fields, and scanning transmission electron microscopy (STEM) is often uniquely well-suited for characterizing the atomic-scale structure of these materials [1-4]. As a result, STEM is poised to significantly impact progress on platinum group metal (PGM)-free catalysts, which are currently under intense development to enable low-cost, commercially viable hydrogen fuel cells [5]. While recent advancements have resulted in fuel cell performance comparable to Pt catalysts by some measures [6], cell durability remains a significant challenge, limiting practical applications [7]. Catalytically active sites in PGM-free materials are proposed to be FeN4 complexes embedded in a graphene lattice (Fig. 1b) within layered or other larger materials, but this is still under debate largely due to the range of potential actives sites predicted by computational methods and lack of methods for directly validating these models [5]. Fundamental insights into the atomic structure and resulting degradation pathways of proposed active sites are therefore needed to fully understand and control cell performance and durability [6].2D materials typically make ideal samples for STEM, but those within PGM-free catalysts present additional challenges since these materials are often defect-rich, with a high density of edges, dopant atoms, etc., which significantly increase susceptibility to beam damage at standard operating voltages. This makes analysis of potential FeN4 active sites particularly challenging, since a large proportion of Fe exists at edge sites where beam-induced atomic displacements can prohibit high-resolution structural characterization [6]. Conventional dark-field imaging compounds this problem by producing less signal for a given dose and being less sensitive to light elements than dose-efficient phase contrast imaging techniques such as those enabled by four-dimensional (4D)-STEM [8-10] (Fig. 1a). Consequently, active site structural analysis is often left to methods such as low-resolution imaging combined with quantum chemical calculations [6], which hinders accurate determination of reaction and degradation mechanisms.Here, we demonstrate direct atomic-scale structural mapping of FeN4 sites by performing low-voltage 4D-STEM on a model PGM-free catalyst system with many exposed monolayer regions. To accomplish this, we pair a 30 keV aberration-corrected probe with a fast pixelated detector that has optimal performance at low beam voltages [11]. This enables us to simultaneously image light and heavy elements with high signal-to-noise by center-of-mass analysis (Fig. 1c) while minimizing beam-induced atomic displacements at sensitive sites. The monolayer nature of these materials additionally allows for experimental validation by direct comparison with multislice simulations [12] of model structures (Fig. 1d-e). This work demonstrates how low-voltage 4D-STEM will provide new insights into the atomic-scale structure and degradation mechanisms of active sites in PGM-free catalysts, facilitating the development of low-cost hydrogen fuel cells and other energy conversion technologies in the future [13].

Zachman, Michael↗
DOE OSTI2020

On the mechanism of soot nucleation. II. E-bridge formation at the PAH bay

A recently proposed mechanism of soot nucleation based upon the formation of a rotationally-activated dimer in the collision of an aromatic molecule and a radical leading to a stable, doubly-bonded E-bridge between them, rooted in the existence of a five-membered ring on the molecule edge, has been further investigated with a focus on the 5-6 E-bridge forming in the reaction of a PAH cyclopenta group with a bay site of another PAH. As a prototype reaction of this kind, we examined the reaction between 4-phenanthrenyl and acenaphthylene and, to project these results to larger aromatic structures, we also explored the size effect of the E-bridge forming reactions by computing the 1-naphthyl + acenaphthylene system and comparing these results with the prior results for pyrenyl + acepyrene. The two systems have been studied through high-level G3(MP2,CC)//B3LYP/6-311G(d,p) ab initio calculations of their potential energy surfaces combined with the RRKM/Master Equation calculations of reaction rate constants. With PAH monomers of similar sizes involved, the formation of E-bridge structures at the bay radical sites appeared to be faster and lead to increased nucleation rates as compared to the zigzag-forming ones. Furthermore, a model combining both the bay and zigzag rotationally-induced formation of E-bridges successfully reaches the level of nucleation fluxes comparable to those of the irreversible pyrene dimerization, thus affirming the rotationally-activated dimerization as a feasible mechanism for soot particle nucleation.

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

Synthesis and anisotropic magnetism in quantum spin liquid candidates A YbSe 2 ( A = K and Rb)

The quantum spin liquid (QSL) state in rare-earth triangular lattices has attracted much attention recently due to its potential application in quantum computing and communication. Here, we report the single-crystal growth synthesis, crystal structure characterizations, and magnetic properties of AYbSe 2 (A = K and Rb) compounds. The x-ray diffraction analysis shows that AYbSe 2 (A = K and Rb) crystallizes in a trigonal space group, R-3m (No. 166) with Z = 3. AYbSe 2 possesses a two-dimensional (2D) Yb–Se–Yb layered structure formed by edged-shared YbSe 6 octahedra. The magnetic properties are highly anisotropic for both title compounds, and no long-range order is found down to 0.4 K, revealing the possible QSL ground state in these compounds. The isothermal magnetization exhibits a one-third magnetization plateau when the magnetic fields are applied in the ab-plane. Heat capacity is performed along both ab-plane and c axis and features the characteristic dome for triangular magnetic lattice compounds as a function of magnetic fields. Due to the change in the interlayer and intralayer distance of Yb 3+ , the dome shifts to low fields from KYbSe 2 to RbYbSe 2 . All these results indicate that the AYbSe 2 family presents unique frustrated magnetism close to the possible QSL and noncollinear spin states.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structural and chemical evolution in layered oxide cathodes of lithium-ion batteries revealed by synchrotron techniques

Abstract Rechargeable battery technologies have revolutionized electronics, transportation and grid energy storage. Many materials are being researched for battery applications, with layered transition metal oxides (LTMO) the dominating cathode candidate with remarkable electrochemical performance. Yet, daunting challenges persist in the quest for further battery developments targeting lower cost, longer lifespan, improved energy density and enhanced safety. This is, in part, because of the intrinsic complexity of real-world batteries, featuring sophisticated interplay among microstructural, compositional and chemical heterogeneities, which has motivated tremendous research efforts using state-of-the-art analytical techniques. In this research field, synchrotron techniques have been identified as a suite of effective methods for advanced battery characterization in a non-destructive manner with sensitivities to the lattice, electronic and morphological structures. This article provides a holistic overview of cutting-edge developments in synchrotron-based research on LTMO battery cathode materials. We discuss the complexity and evolution of LTMO’s material properties upon battery operation and review recent synchrotron-based research works that address the frontier challenges and provide novel insights in this field. Finally, we formulate a perspective on future directions of synchrotron-based battery research, involving next-generation X-ray facilities and advanced computational developments.

25 ENERGY STORAGE↗
DOE OSTI2022

High-resolution Compton spectroscopy using x-ray microcalorimeters

X-ray Compton spectroscopy is one of the few direct probes of the electron momentum distribution of bulk materials in ambient and operando environments. Herein, we report high-resolution inelastic x-ray scattering experiments with high momentum and energy transfer performed at a storage-ring-based high-energy x-ray light source facility using an x-ray transition-edge sensor (TES) microcalorimeter detector. The performance was compared with a silicon drift detector (SDD), an energy-resolving semiconductor detector, and Compton profiles were measured for lithium and cobalt oxide powders relevant to lithium-ion battery research. Spectroscopic analysis of the measured Compton profiles demonstrates the high-sensitivity to the low- Z elements and oxidation states. The line shape analysis of the measured Compton profiles in comparison with computed Hartree–Fock profiles is usually limited by the resolution of the semiconductor detector. We have characterized an x-ray TES microcalorimeter detector for high-resolution Compton scattering experiments using a bending magnet source at the Advanced Photon Source with a double crystal monochromator, providing monochromatic photon energies near 27.5 keV. The momentum resolution below 0.16 atomic units (a.u.) was measured, yielding an improvement of more than a factor of 7 over a state-of-the-art SDD for the same scattering geometry. Furthermore, the lineshapes of narrow valence and broad core electron profiles of sealed lithium metal were clearly resolved using an x-ray TES compared to smeared and broadened lineshapes observed when using the SDD. High-resolution Compton scattering using the energy-resolving area detector shown here presents new opportunities for spatial imaging of electron momentum distributions for a wide class of materials with applications ranging from electrochemistry to condensed matter physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Resolving the fast ion distribution from imaging neutral particle analyzer measurements

A recently developed imaging neutral particle analyzer (INPA) on the DIII-D tokamak (Du 2018 Nucl. Fusion 58 082006) enables fast ion velocity-space tomography of high fidelity at the interrogated phase space. To accomplish this, the spatial and energy depending fast ( E < 80 keV) neutral flux towards the INPA stripping foils is calculated with FIDASIM and a newly developed code INPASIM simulates the INPA instrumental response to this neutral flux. Included in INPASIM is the neutral-foil interaction, the Larmor orbit tracing between the foil and the phosphor, the phosphor response to the incident ion flux as well as camera focusing. Benefiting from heavy, localized velocity-space weights and excellent signal to noise, computed tomography using the Ridge regression method is able to successfully reconstruct fine-scale velocity-space structures produced by multiple neutral beams separated by as small as ~3 keV in tests. Applying the inversion method to a sawtooth crash event reveals a significant profile flattening of confined passing particles across q = 1 flux surface, as well as a redistribution of fast ions into the trapped orbits at the plasma edge close to the last closed flux surface.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

White Box Access to Quantum Testbeds for Co-Design

At Lawrence Livermore National Laboratory (LLNL), we operate and maintain the Quantum Device and Integration Testbed (QuDIT) facility, a small quantum testbed that supports about 10 active research teams (including our own) and over 50 internal and external collaborators. This testbed is designed to give remote white box access to users for research, training, and outreach. A guiding principle behind the development of our testbed infrastructure, software and user interfaces is to empower users to perform experiments at the cutting edge of quantum information science at any level of abstraction, from materials studies, device physics and control and characterization techniques to algorithm development and quantum operating system design. Our testbed targets a multilevel quantum system (qudit) to expand the accessible Hilbert space of a simple-to-manufacture quantum device and focuses on quantum simulation, typically implemented through custom gates designed with quantum optimal control methods, rather than on a universal computing framework with a fixed gate set. We leverage the Lab’s high-performance computing (HPC) program and related expertise to simulate quantum systems, develop hybrid algorithms, and generate gates optimized for given simulations. Additionally, we have adopted a co-design philosophy from the HPC community in designing new hardware, so that the systems we develop are optimized for the specific physics simulations we plan to use them for.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Low-Power Nuclear Heating Tests of UN Fuel Particles in Hydrogen Environment for Nuclear Thermal Propulsion

Recently, there has been a reemergence of interest within the space-exploration community regarding nuclear thermal propulsion (NTP) for interplanetary travel. NTP, as conceptualized, is a compact propulsion source, is relatively light because there is no need for an oxidizing agent and has a very high specific impulse compared to traditional forms of propulsion. These factors would reduce first-leg orbital-rocketry costs (by the source’s low weight and volume) and decrease overall travel time for astronauts with higher transit speeds. Idaho National Laboratory was provided bare, uncoated, and tungsten-coated uranium nitride fuel particles as part of a preliminary investigation. The fuel was fabricated by BWX Technologies and irradiated at the Massachusetts Institute of Technology research reactor. X-ray computed tomography and scanning electron microscopy and associated chemical analysis techniques were performed on these particles at Idaho National Laboratory. Both coated and uncoated forms were examined in the as-fabricated and as-irradiated conditions. The tungsten coating was found to be intact following the irradiation, indicating positive attributes for performance under these low-power-irradiation conditions. The irradiation of uncoated particles resulted in nitrogen enrichment at the edge of the particle.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. Here, we present TERACHEM CLOUD, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TERACHEM CLOUD to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TERACHEM CLOUD framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

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

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Transformation of an Embedded Five-Membered Ring in Polycyclic Aromatic Hydrocarbons via the Hydrogen-Abstraction–Acetylene-Addition Mechanism: A Theoretical Study

Five-membered rings are constituents of many polycyclic aromatic hydrocarbons (PAHs) and their presence on the edges of large PAHs has been repeatedly observed experimentally. However, modern kinetic combustion models often do not consider the growth of PAHs through the transformation of the five-membered rings. Here, in connection with the above, we carried out a theoretical study of the mechanism of Hydrogen-Abstraction-aCetylene-Addition (HACA) transformation of an embedded five-membered ring on the armchair PAH edge to a six-membered ring, considering cyclopenta[d,e,f]phenanthrene (4,5-methylenephenanthrene) as a prototype system for this process. The potential energy surface for the reactions of cyclopenta[d,e,f]phenanthrenyl radicals produced by direct H abstractions from cyclopenta[d,e,f]phenanthrene with acetylene has been compiled at the G3(MP2,CC)//B3LYP/6-311G(d,p) level of theory including zero-point vibrational energy corrections. The computed energies and molecular parameters were then used to solve the Rice-Ramsperger-Kassel-Marcus Master Equation (RRKM-ME) in order to calculate reaction rate at various pressures and temperatures, which were fitted to modified Arrhenius equation for further kinetic modeling. The results show that the HACA transformation of the embedded five-membered ring to a six-membered ring is possible, albeit slow. The most viable reaction mechanism involves the R2 + C 2 H 2 reaction, where the acetylene molecules adds to a σ-radical in the six-membered ring adjacent to the five-membered ring via a low entrance barrier. The predominant product of R2 + C 2 H 2 is predicted to be 3 ethynyl 4H cyclopenta[def]phenanthrene Pr5 via immediate H elimination from the initial addition complex. Next, Pr5 undergoes H-assisted isomerization to 4aH-pentaleno[4,3,2,1-cdef]phenanthrene Pr4, and the latter adds an H atom eventually forming the 1-pyrenylmethyl radical Pr3: R2 + C 2 H 2 ⇌ 3 ethynyl 4H cyclopenta[def]phenanthrene (Pr5) + H ⇌ 4aH-pentaleno[4,3,2,1-cdef]phenanthrene (Pr4) + H Pr5 + H ⇌ Pr4 + H Pr4 + H → 1-pyrenylmethyl (Pr3) This HACA sequence may be competitive with the methyl radical addition to the R1 radical formed by H abstraction from the CH 2 group in the five-membered ring of cyclopenta[d,e,f]phenanthrene, which provides a pathway to pyrene following two H atom losses. Relative contributions of the two mechanisms of the five- to six-membered ring transformation would strongly depend on the branching ratios of the R1 and R2 radicals produced by the H abstractions and the available concentration of C 2 H 2 vs. CH 3 and hence differ in different flames.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗