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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The Role of Technical Guidance and Analysis Tools in Remedy Decisions, Implementation, and Closure - 18307

Selection, design, and implementation of environmental remedies are conducted under regulatory frameworks that define the activities, communication, and documentation requirements for the remediation process. However, there are site situations that are not well described in the basic regulatory frameworks, and technical guidance and analysis tools can help facilitate remedy decisions and actions. An example where technical guidance and analysis tools are helpful is in the transition from pump-and-treat (P and T) remediation to monitored natural attenuation (MNA). Standard MNA regulatory guidance does not account for a plume initially controlled by P and T hydraulic conditions rather than natural-gradient conditions. For instance, MNA guidance discusses demonstrating plume stability as a means to evaluate the suitability of MNA. However, plume stability under long-term natural gradient conditions cannot be directly demonstrated while the plume is being contained by a P and T system. Thus, additional guidance is needed to describe other measures that can be used to support transition from P and T to MNA. As an additional example, standard MNA regulatory guidance is not written to provide suitable information for application to vadose-zone contamination; discussion and examples in the regulatory guidance are predominantly for groundwater. Use of contaminant transport assessments is an important component of vadose-zone remedy decisions where technical guidance and analysis tools can facilitate these decisions. For volatile compounds, the assessment may need to also consider flux to the surface and constraints associated with vapor intrusion. Recent technical guidance and associated analysis tools provide sites with approaches to support remedy decisions for the above conditions. In particular, these recent approaches target developing and implementing P and T and soil vapor extraction remediation exit strategies and facilitating assessment and implementation of remedies for coupled vadose-zone/groundwater contamination. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, in this work, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Towards directed energy deposition of metals using polymer-based supports: porosity of 316L stainless steel deposited on carbon-fiber-reinforced ABS

Directed energy deposition (DED) is increasingly valuable to many industries because of its high deposition rates relative to other metal additive manufacturing processes, but the design space of DED is limited. For instance, steep overhangs are difficult or impossible to manufacture. Polymer-based support structures could help address this challenge. The viability of DED on polymer composite substrates has begun to be explored, specifically with 316L stainless steel on carbon-fiber-reinforced ABS substrates. Monolithic metal components can be deposited on the polymer, but it was found that gas release during polymer degradation causes porosity due to gas entrapment in the metal. An interlayer cooling time was introduced to reduce polymer degradation and decrease the porosity due to gas entrapment, but this led to porosity from lack of fusion. The results of this work provide insight into process parameter selection and scan strategy development to enable the use of polymer support structures in blown-powder DED.

Kurfess, Rebecca↗
DOE OSTI2017

Identifying and Breaking Scaling Relations in Molecular Catalysis of Electrochemical Reactions

Improving molecular catalysis for important electrochemical proton-coupled electron transfer (PCET) reactions, such as the interconversions of H+/H2, O2/H2O, CO2/CO, and N2/NH3, is an ongoing challenge. Synthetic modifications to the molecular catalysts are valuable but often show trade-offs between turnover frequency (TOF) and the effective overpotential required to initiate catalysis (?eff). Herein, we derive a new approach for improving efficiencieshigher TOF at lower ?eff by changing the concentrations and properties of the reactants and products, rather than by modifying the catalyst. The dependence of TOF on ?eff is shown to be quite different upon changing, for instance, the pKa of the acid HA versus the concentration or partial pressure of a reactant or product. Using the electrochemical reduction of dioxygen catalyzed by iron porphyrins in DMF as an example, decreasing [HA] 10-fold lowers ?eff by 59 mV and decreases the TOF by a factor of 10. Alternatively, a 10- fold decrease in Ka(HA) also lowers ?eff by 59 mV but only decreases the TOF by a factor of 2. This approach has been used to improve a catalytic TOF by 104 vs the previously reported scaling relationship developed via synthetic modifications to the catalyst. The analysis has the potential to predict improved efficiency and product selectivity of any molecular PCET catalyst, based on its mechanism and rate law.

Pegis, Michael L.↗
DOE OSTI2023

User Guide for the Los Alamos National Laboratory Fallout Calculator (V.1.1)

The Los Alamos National Laboratory Fallout Calculator is a Java™ application that can calculate the dose from many nuclear detonations producing fallout. The fallout calculations are performed for one or multiple selected winds. The winds are from the years 2000 to 2010, for all 12 months, and for the 1 st , 10 th , and 20 th of each month. The winds are available from 0 meters to 50,000 meters altitude MSL. This Fallout Calculator performs ideal calculations. There is no terrain. The ground is perfectly flat at sea level. There is no rain that would cause radioactive hot spots. The wind is historical, not a current wind. Also, the wind is fixed in time. It does not vary over the 24 hours it takes the fallout to arrive on the ground. Therefore, this calculator is suitable for operational type studies. For instance, it can be used to calculate the relative difference in fallout patterns from different possible nuclear laydowns. It can be used to understand how frequently fallout would be likely to occur in a particular part of the US. It could be used to develop plans for how to protect the population from a nuclear attack. However, it should not be used to predict the dose in a city from an actual laydown, or to predict safe corridors for the population to use to egress from areas with high fallout.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

High-Throughput Open-Air Plasma Activation of Metal-Oxide Thin Films with Low Thermal Budget

Sputter-processed oxide films are usually annealed at high temperature (activation process) to achieve stable electrical characteristics through the formation of strong metal-oxide chemical bonds. For instance, indium– gallium–zinc oxide (IGZO) films typically need a thermal treatment at 300 °C for ≥1 h as an activation process. We introduce an open-air plasma treatment (OPT) to rapidly and effectively activate sputter-processed IGZO films. The OPT effectively induces metal-oxide chemical bonds in IGZO films at temperatures as low as 240 °C, with a dwell time on the order of a second. Furthermore, by controlling the plasma-processing conditions (scan speed, distance α between plasma nozzle and samples, and gas flow rate), the electrical characteristics and the microstructure of the IGZO films can be easily tuned. Finally, OPT can be utilized to implement a selective activation process. Plasma-treated IGZO thin-film transistors (TFTs) display comparable electrical characteristics to those of conventionally thermal treated IGZO TFTs. Through in-depth optical, chemical, and physical characterizations, we validate that OPT simultaneously dissociates weak chemical bonds by UV radiation and ion bombardment and reestablishes the metal-oxide network by radical reaction and OPT-induced heat.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Structural, dynamical, and transport properties of the hydrated halides: How do At{sup −} bulk properties compare with those of the other halides, from F{sup −} to I{sup −}?

The properties of halides from the lightest, fluoride (F{sup −}), to the heaviest, astatide (At{sup −}), have been studied in water using a polarizable force-field approach based on molecular dynamics (MD) simulations at the 10 ns scale. The selected force-field explicitly treats the cooperativity within the halide-water hydrogen bond networks. The force-field parameters have been adjusted to ab initio data on anion/water clusters computed at the relativistic Möller-Plesset second-order perturbation theory level of theory. The anion static polarizabilities of the two heaviest halides, I{sup −} and At{sup −}, were computed in the gas phase using large and diffuse atomic basis sets, and taking into account both electron correlation and spin-orbit coupling within a four-component framework. Our MD simulation results show the solvation properties of I{sup −} and At{sup −} in aqueous phase to be very close. For instance, their first hydration shells are structured and encompass 9.2 and 9.1 water molecules at about 3.70 ± 0.05 Å, respectively. These values have to be compared to the F{sup −}, Cl{sup −}, and Br{sup −} ones, i.e., 6.3, 8.4, and 9.0 water molecules at 2.74, 3.38, and 3.55 Å, respectively. Moreover our computations predict the solvation free energy of At{sup −} in liquid water at ambient conditions to be 68 kcal mol{sup −1}, a value also close the I{sup −} one, about 70 kcal mol{sup −1}. In all, our simulation results for I{sup −} are in excellent agreement with the latest neutron- and X-ray diffraction studies. Those for the At{sup −} ion are predictive, as no theoretical or experimental data are available to date.

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

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

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

Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations

We present an approach for quantitatively predicting the temperature-dependent single-component adsorption behavior of linear alkanes in silica and Na-exchanged cationic zeolites using machine learning (ML) models trained from extensive molecular simulations based on force fields with coupled cluster accuracy. A high-performing classification model was developed to distinguish between instances with negligible and non-negligible adsorption. Subsequently, two ML models were trained to predict the single-component adsorption loading and the heat of adsorption at any pressure at 300 K for any zeolite topology and silicon-to-aluminum ratio. The ML models were trained on International Zeolite Association (IZA) zeolites, and their transferability to hypothetical zeolites was successfully validated. We then expand the power of these predictions to adsorbed mixtures at arbitrary temperatures by integrating them with the Clausius–Clapeyron equation and ideal adsorbed solution theory (IAST). This approach was validated and then applied to a temperature swing adsorption separation process to demonstrate its practical utility. We demonstrate how predictions from this ML-enabled approach can allow the selection of high-performing materials that are then validated using detailed molecular simulations based on quantitatively accurate force fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

SU-F-T-285: Evaluation of a Patient DVH-Based IMRT QA System

Purpose: To evaluate the clinical performance of a patient DVH-based QA system for prostate VMAT QA. Methods: Mobius3D(M3D) is a QA software with an independent beam model and dose engine. The MobiusFX(MFX) add-on predicts patient dose using treatment machine log files. We commissioned the Mobius beam model in two steps. First, the stock beam model was customized using machine commissioning data, then verified against the TPS with 12 simple phantom plans and 7 clinical 3D plans. Secondly, the Dosimetric Leaf Gap(DLG) in the Mobius model was fine-tuned for VMAT treatment based on ion chamber measurements for 6 clinical VMAT plans. Upon successful commissioning, we retrospectively performed IMRT QA for 12 VMAT plans with the Mobius system as well as the ArcCHECK-3DVH system. Selected patient DVH values (PTV D95, D50; Bladder D2cc, Dmean; Rectum D2cc) were compared between TPS, M3D, MFX, and 3DVH. Results: During the first commissioning step, TPS and M3D calculated target Dmean for 3D plans agree within 0.7%±0.7%, with 3D gamma passing rates of 98%±2%. In the second commissioning step, the Mobius DLG was adjusted by 1.2mm from the stock value, reducing the average difference between MFX calculation and ion chamber measurement from 3.2% to 0.1%. In retrospective prostate VMAT QA, 5 of 60 MFX calculated DVH values have a deviation greater than 5% compared to TPS. One large deviation at high dose level was identified as a potential QA failure. This echoes the 3DVH QA result, which identified 2 instances of large DVH deviation on the same structure. For all DVH’s evaluated, M3D and MFX show high level of agreement (0.1%±0.2%), indicating that the observed deviation is likely from beam modelling differences rather than delivery errors. Conclusion: Mobius system provides a viable solution for DVH based VMAT QA, with the capability of separating TPS and delivery errors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

AN APPROACH TO BRINGING AUTOMATED FAULT DETECTION AND DIAGNOSIS (AFDD) TOOLS FOR HVAC&R INTO THE MAINSTREAM

Heating, ventilation and air-conditioning (HVAC) systems can consume over 5 quads of energy annually, representing 30% of energy consumption in the U.S. in commercial buildings. Additionally, commercial refrigeration (R) systems add about 2 quads to commercial buildings energy consumption. Most HVAC systems have one or more faults (low/high refrigerant charge, valve leakage, condenser/evaporator fouling, filter/dryer restriction, economizer faults, controls faults) that result in increased energy consumption. Automated fault detection and diagnosis (AFDD) tools have been developed to address this national issue and many tools are commercially available. AFDD tools have the potential to save considerable energy for existing commercial RTUs, chillers and refrigeration systems. These devices can be used for both retro commissioning, and, when faults are addressed, continuous commissioning as well. However, there appears to be multiple market barriers for this technology. A key market barrier for this technology is the lack of awareness of AFDD products among potential customers. Most HVAC contractors are not familiar with the latest AFDD technologies and HVAC technicians lack skills regarding these technologies. Quantifying potential benefits to building owners is difficult since there are several FDD tools with varying capabilities. For instance, there are several FDD products ranging from handling just economizer faults to those that also handle full-blown refrigerant-side and air-side faults. Methods/algorithms used in FDD vary significantly. Even though there are efforts to develop standards, currently there are no standards/methods to define functions, capabilities, accuracy, and reliability of FDD tools. Moreover, most of the commercial AFDD tools have not been verified in the field independently. This paper presents a comprehensive approach to bringing HVAC AFDD tools into the mainstream. The approach involves demonstrating ten commercially available tools at ten different sites, independent testing and evaluation of the FDD tools, communication with various stakeholders, identifying market barriers, and assisting utility companies in developing incentive programs. This paper presents selection of AFDD tools, ite identification, and field testing and evaluation method.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

First Measurement Of Charged-current 1 Muon 2 Proton 0 Pion Single Differential Cross-sections On Argon-40 At 0.8 Gev Average Neutrino Energy With The MicroBooNE Detector

Constraining systematic uncertainties that arise from neutrino-nucleon cross-section modeling is critical for upcoming neutrino oscillation experiments. Neutrino interactions that lead to the ejection of 2 particles from the target nucleus, leaving the nucleus with 2 holes, are one cross-section of interest. These instances, known as 2-particle 2-hole (2p2h) states, can be produced either by neutrino interaction modes or from the influence of nuclear physics on the neutrino interaction. While there are many theoretical models that attempt to describe the nature of 2p2h states, very few experimental measurements of this process exist, making it difficult to benchmark theoretical calculations. Furthermore, no high statistic cross-section measurements of 2p2h states exist on $^{40}$Ar, the target nucleus of many current and next generation neutrino oscillation experiments. This analysis aims to address this issue by providing the first high-statistic single differential cross-section me asurements of 2p2h states. Using data collected from the first 3 years of running of the MicroBooNE experiment, we select $\nu_\mu$ charged-current interaction events that produce a single muon, 2 protons, and no pions in the final state (CC1$\mu$2p0$\pi$). The single differential cross-section is then extracted as a function of a variety of physics variables and compared to theoretical predictions from several event generator model sets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A deep learning approach to identify missing is-a relations in SNOMED CT

Abstract Objective SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT. Materials and Methods Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

The spectral energy distributions of active galactic nuclei

ABSTRACT We present spectral energy distributions (SEDs) of 41 active galactic nuclei, derived from multiwavelength photometry and archival spectroscopy. All of the SEDs span at least 0.09 to 30 $\mu$m, but in some instances wavelength coverage extends into the X-ray, far-infrared, and radio. For some active galactic nuclei (AGNs) we have fitted the measured far-infrared photometry with greybody models, while radio flux density measurements have been approximated by power laws or polynomials. We have been able to fill some of the gaps in the spectral coverage using interpolation or extrapolation of simple models. In addition to the 41 individual AGN SEDs, we have produced 72 Seyfert SEDs by mixing SEDs of the central regions of Seyferts with galaxy SEDs. Relative to the literature, our templates have broader wavelength coverage and/or higher spectral resolution. We have tested the utility of our SEDs by using them to generate photometric redshifts for 0 < z ≤ 6.12 AGNs in the Boötes field (selected with X-ray, IR, and optical criteria) and, relative to SEDs from the literature, they produce comparable or better photometric redshifts with reduced flux density residuals.

Brown, M. J. I.↗
DOE OSTI2024

Adaptive Fault Current-Limiting Control of MMC for Protection of Multiterminal HVDC Systems: Preprint

For the development of multi-terminal high voltage DC (MTDC) transmission, it is critical to design the protection system that can selectively isolate the faulty area from the healthy part of the dc grid using DC circuit breakers (DCCBs) while ensuring continuous operation of converter stations in the healthy part. However, because of the lack of fault current blocking capability in half-bridge (HB) modular multilevel converters (MMCs), when a dc fault occurs, the rising fault currents can quickly reach the blocking threshold within a few milliseconds and disrupt the operation of MMCs in the healthy part. Large DC reactors are often considered in series with DCCBs to reduce the rate of rise of fault currents and prevent blocking of MMCs in the healthy part of the grid. However, large DC reactors can prohibitively increase the cost of the system, particularly when they are considered in an offshore environment, for instance in offshore wind projects. Large dc reactors can also introduce stability issues and create post-fault oscillations. This paper presents an adaptive fault current limiting control method for MMCs to avoid their blocking and enable continuous operation of MTDC systems. It contains two parts: The first part is based on circulating current feedforward control that emulates virtual reactors in each arm of an MMC, which is immediately activated when the fault current starts to increase, to reduce the rate of rise of the fault current; the second part is triggered when the fault current exceeds a preset threshold by temporarily bypassing the SMs, serving as a complement to the fault current limiting effect of the first part. Both parts do not require fault detection signal and they are activated automatically during faults. Simulation case studies of a four-terminal bipolar MMC-HVDC system are presented to demonstrate the effectiveness of the proposed control methods.

active fault current limiting↗
DOE OSTI2022

Detecting Cage Crossing and Filling Clusters of Magnesium and Carbon Atoms in Zeolite SSZ-13 with Atom Probe Tomography

The conversion of methanol to valuable hydrocarbon molecules is of great commercial interest, as the process serves as a sustainable alternative for the production of, for instance, the base chemicals for plastics. The reaction is catalyzed by zeolite materials. By the introduction of magnesium as a cationic metal, the properties of the zeolite, and thereby the catalytic performance, are changed. With atom probe tomography (APT), nanoscale relations within zeolite materials can be revealed: i.e., crucial information for a fundamental mechanistic understanding. We show that magnesium forms clusters within the cages of zeolite SSZ-13, while the framework elements are homogeneously distributed. These clusters of just a few nanometers were analyzed and visualized in 3-D. Magnesium atoms seem to initially be directed to the aluminum sites, after which they aggregate and fill one or two cages in the zeolite SSZ-13 structure. The presence of magnesium in zeolite SSZ-13 increases the lifetime as well as the propylene selectivity. By using operando UV–vis spectroscopy and X-ray diffraction techniques, we are able to show that these findings are related to the suppression of aromatic intermediate products, while maintaining the formation of polyaromatic compounds. Further nanoscale analysis of the spent catalysts showed indications of magnesium redistribution after catalysis. Unlike zeolite H-SSZ-13, for which only a homogeneous distribution of carbon was found, carbon can be either homogeneously or heterogeneously distributed within zeolite Mg-SSZ-13 crystals as the magnesium decreases the coking rate. Carbon clusters were isolated, visualized, and analyzed and were assumed to be polyaromatic compounds. Small one-cage-filling polyaromatic compounds were identified; furthermore, large-cage-crossing aromatic molecules were found by isolating large coke clusters, demonstrating the unique coking mechanism in zeolite SSZ-13. Short-length-scale evidence for the formation of polyaromatic compounds at acid sites is discovered, as clear nanoscale relations between aluminum and carbon atoms exist.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗