Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Smoothing methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Parallelization and Performance Portability in Hydrodynamics Codes

With the eve of Exascale computing, performance and portability are at the forefront of all scientific codes. Adding more cores and more energy to a system is no longer a sustainable way to achieve performance, and extra effort must now be made to improve performance in all areas of code and code development. Using hydrodynamic codes as a basis, this work explores numerous techniques to achieve performance in different ways. Adaptive mesh refinement (AMR) is a necessary technique to improve memory optimization in mesh-based simulations. However it is invasive and conventionally difficult to integrate into existing applications, so we present a new branch of AMR to create a smooth transition to these optimizations, which not only improves performance, but also greatly reduces developer effort. We introduce the concept of this improvement as Phantom-Cell AMR, and assess theoretically the improvements, as well as present an application of its use. Other work included involves and investigation into an efficient data structure that ensures optimal memory layout for cache performance, with a target of making codes performant and portable across all architectures. All of the work targets both performance and portability, not just on CPU hardware, but specifically across GPU architectures. Parallel performance is key to all of the methods presented, but the research makes a great effort to improve the portability of all applications to prepare for current high performance computing systems and those on the horizon.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computational Analysis of Water Film Thickness During Rain Events for Assessing Hydroplaning Risk Part 2: Rough Road Surfaces

Hydroplaning occurs when a water film forms on a road during a rain event and vehicles are traveling at a speed that does not provide sufficient time for the tires to push the water film out of the tire path. Under these conditions, the tire loses contact with the road and the driver may lose control over the vehicle. The present study is a part of a multiyear research effort aimed at gaining a better understanding of the factors that contribute to hydroplaning risk in order to minimize the occurrence of hydroplaning accidents. Three-dimensional computational fluid dynamics (CFD) analysis was used to investigate the water film thickness on multilane roads, one of the key parameters in evaluating hydroplaning risk. In the first part of the study, water films forming on wide roadways, i.e. multilane highways with 2, 4, and 6 lanes per side, were analyzed, with varying cross slope, longitudinal slope, and rainfall rate. Roadways with and without curbs and drainage were included in the analysis. The analysis in the first part was limited to nearly smooth asphalt or concrete surfaces because the maximum roughness height that can be specified in a CFD model using wall functions to determine shear stress at the road surface boundary is one half the thickness of computational cell layer adjacent to the boundary and the need to resolve the vertical velocity distribution in a thin water film requires cell layers that are a fraction of a millimeter thick. In this part of the study, the analysis was extended to consider the influence of pavement roughness over a range extending up 3.6 mm on the development of water film thickness. Various methods of modeling roughness were investigated and are presented: the roughness height model, a meshed-out geometry of the macrotexture of the pavement, and a porous region model. The computational results were analyzed and compared with experimental measurements performed by Gallaway et al.

42 ENGINEERING↗
DOE OSTI2021

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.

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

Feasibility of the LAMMPS SPH Package for Simulating Bubble Motion in Vibrating Containers

The Smoothed Particle Hydrodynamics (SPH) package within LAMMPS is explored as a possible tool for simulating the motion of bubbles in a vibrating liquid-filled container. As an initial test case, the unphysical but computationally less intense situation of a two-dimensional single bubble rising in a quiescent liquid under the influence of gravity is considered herein. Although physically plausible behavior was obtained under certain conditions, this behavior depends strongly on the system parameters. Moreover, the large density ratio between the liquid and bubble requires extremely small timesteps, which make the simulations undesirably computationally expensive. Ultimately, it was determined that this method is not feasible for providing quantitatively accurate results for the desired application.

42 ENGINEERING↗
DOE OSTI2024

Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-Based Sensors

Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive Control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National Electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.

data processing↗
DOE OSTI2022

Reliably obtaining white light from layered halide perovskites at room temperature

The recent observation of broadband white-light emission from the inorganic sheets of certain layered lead-bromide perovskites has instigated a multitude of studies on this unusual phenomenon. However, the vast majority of layered bromide perovskites have flat (001) inorganic sheets and display a narrow photoluminescence at room temperature. A handful of heavily distorted (001) perovskites display broad emission, but to date, there is no method of predicting which perovskites will produce white light at room temperature prior to screening different organic molecules that can template 2D perovskites and crystallizing and analyzing the material. By studying ten Pb–Cl perovskites, we find that they all exhibit a broad yellow emission, which is strikingly invariant despite different distortions in the inorganic framework seen across the series. We postulate that this broad emission is intrinsic to all layered Pb–Cl perovskites. Although broad, the emission is not white. By adding Br to the Pb–Cl perovskites we obtain both the narrow emission and the broad emission such that the combined emission color smoothly varies from yellow to warm white to cold white as a function of the halide ratio. Thus, alloying Br to Pb–Cl perovskites appears to be a simple and general strategy for reliably obtaining white light at room temperature from (001) perovskites, regardless of the templating effects of the organic molecules, which should greatly expand the number of white-light-emitting layered perovskites.

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

On-Demand Selection of the Latent Domain Orientation in Spray-Deposited Block Copolymer Thin Films

With their ability to self-assemble spontaneously into well-defined nanoscale morphologies, block copolymer (BCP) thin films are a versatile platform to fabricate functional nanomaterials. An important challenge to wider deployment of BCPs in nanofabrication is combining precise control over the nanoscale domain orientation in BCP assemblies with scalable deposition techniques that are applicable to large-area, curved, and flexible substrates. Here, in this study, we show that spray-deposited smooth films of a nominally disordered BCP exhibit latent orientations, which can be prescriptively selected by controlling solvent evaporation during spray casting. Subsequent solvent vapor annealing triggers assembly toward highly ordered cylindrical morphologies along the pathway determined by solvent evaporation in the prior spray deposition stage. Faster evaporation promotes assembly of vertically oriented cylinders spanning the entire film thickness (100–300 nm). In comparison, slow solvent evaporation permits intermicellar aggregation and incipient cylinder formation in solution, which induces horizontal cylinder assembly upon annealing. The evaporatively controlled latent orientation mechanism presented herein elucidates how nonequilibrium phenomena during casting govern successive self-assembly pathways and facilitates a versatile method to dictate the domain orientation of BCP thin films on demand on flexible and highly curved substrates or in distinct pattern areas on the same substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n -Moment Bulk Microphysics Scheme

The small-scale microphysical processes governing the formation of precipitation particles cannot be resolved explicitly by cloud resolving and climate models. Instead, they are represented by microphysics schemes that are based on a combination of theoretical knowledge, statistical assumptions, and fitting to data (“tuning”). Historically, tuning was done in an ad hoc fashion, leading to parameter choices that are not explainable or repeatable. Recent work has treated it as an inverse problem that can be solved by Bayesian inference. The posterior distribution of the parameters given the data—the solution of Bayesian inference—is found through computationally expensive sampling methods, which require over $\mathcal{O}$(10 5 ) evaluations of the forward model; this is prohibitive for many models. We present a proof of concept of Bayesian learning applied to a new bulk microphysics scheme named “Cloudy,” using the recently developed Calibrate-Emulate-Sample (CES) algorithm. Cloudy models collision-coalescence and collisional breakup of cloud droplets with an adjustable number of prognostic moments and with easily modifiable assumptions for the cloud droplet mass distribution and the collision kernel. The CES algorithm uses machine learning tools to accelerate Bayesian inference by reducing the number of forward evaluations needed to $\mathcal{O}$(10 2 ). It also exhibits a smoothing effect when forward evaluations are polluted by noise. In a suite of perfect-model experiments, we show that CES enables computationally efficient Bayesian inference of parameters in Cloudy from noisy observations of moments of the droplet mass distribution. In an additional imperfect-model experiment, a collision kernel parameter is successfully learned from output generated by a Lagrangian particle-based microphysics model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Direct Numerical Simulation of Transitional and Turbulent Flows Over Multi-Scale Surface Roughness—Part I: Methodology and Challenges

Abstract High-fidelity simulation of transitional and turbulent flows over multi-scale surface roughness presents several challenges. For instance, the complex and irregular geometrical nature of surface roughness makes it impractical to employ conforming structured grids, commonly adopted in large-scale numerical simulations due to their high computational efficiency. One possible solution to overcome this problem is offered by immersed boundary methods, which allow wall boundary conditions to be enforced on grids that do not conform to the geometry of the solid boundary. To this end, a three-dimensional, second-order accurate boundary data immersion method (BDIM) is adopted. A novel mapping algorithm that can be applied to general three-dimensional surfaces is presented, together with a newly developed data-capturing methodology to extract and analyze on-surface flow quantities of interest. A rigorous procedure to compute gradient quantities such as the wall shear stress and the heat flux on complex non-conforming geometries is also introduced. The new framework is validated by performing a direct numerical simulation (DNS) of fully developed turbulent channel flow over sinusoidal egg-carton roughness in a minimal-span domain. For this canonical case, the averaged streamwise velocity profiles are compared against results from the literature obtained with a body-fitted grid. General guidelines on the BDIM resolution requirements for multi-scale roughness simulation are given. Momentum and energy balance methods are used to validate the calculation of the overall skin friction and heat transfer at the wall. The BDIM is then employed to investigate the effect of irregular homogeneous surface roughness on the performance of an LS-89 high-pressure turbine blade at engine-relevant conditions using DNS. This is the first application of the BDIM to realize multi-scale roughness for transitional flow in transonic conditions in the context of high-pressure turbines. The methodology adopted to generate the desired roughness distribution and to apply it to the reference blade geometry is introduced. The results are compared to the case of an equivalent smooth blade.

Engineering↗
DOE OSTI2023

Constraining accuracy of the pairwise velocities in N -body simulations using scale-free models

We present a continuation of an analysis that aims to quantify resolution of N-body simulations by exploiting large (up to N = 4096 3 ) simulations of scale-free cosmologies run using abacus. Here, we focus on radial pairwise velocities of the matter field, both by direct estimation and through the cumulative two-point correlation function (using the pair conservation equation). We find that convergence at the 1 per cent level of the mean relative pairwise velocity can be demonstrated over a range of scales, evolving from a few times the grid spacing at early times to slightly below this scale at late times. We show the analysis of two different box sizes as well as from averaging results from the smaller boxes, and compare the power of the two aforementioned estimators in constraining accuracy at each scale. Down to scales of the order of the smoothing parameter, convergence is obtained at ∼5 per cent precision, and shows a behaviour indicating asymptotic stable clustering. In conclusion, we also infer for LCDM simulations conservative estimates on the evolution of the lower cut-off to resolution (at 1 and 5 per cent precision) as a function of redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Grain boundary solute segregation across the 5D space of crystallographic character

Solute segregation in materials with grain boundaries (GBs) has emerged as a popular method to thermodynamically stabilize nanocrystalline structures. However, the impact of varied GB crystallographic character on solute segregation has never been thoroughly examined. Here, this work examines Co solute segregation in a dataset of 7272 Al bicrystal GBs that span the 5D space of GB crystallographic character. Considerable attention is paid to verification of the calculations in the diverse and large set of GBs. In addition, the results of this work are favorably validated against similar bicrystal and polycrystal simulations. As with other work, we show that Co atoms exhibit strong segregation to sites in Al GBs and that segregation correlates strongly with GB energy and GB excess volume. Segregation varies smoothly in the 5D crystallographic space but has a complex landscape without an obvious functional form.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Report on FY 2023 Research and Development on Specially Designed Creep-fatigue Experiments on Alloy 617 in Support of Improving Creep-fatigue Evaluation Approaches

Experimental and numerical studies in support of developing the integrated Elastic–Perfectly Plastic (EPP) plus Simplified Model Test (SMT) design methodology, referred to as the EPP+SMT method, continued in FY 2023. This report focuses on the methods for extrapolating the EPP+SMT creep-fatigue life curves at long hold times and low strain ranges at elevated temperatures. In this work, the available uniaxial creep-fatigue failure data on Alloy 617 at temperatures of 950°C and 850°C were analyzed to provide a guidance on the development of the extrapolation method and the creep-fatigue failure criteria. A viscoplastic constitutive model for Alloy 617 was adopted to extrapolate the mechanical responses to low strain ranges and long hold times. A set of design curves of Alloy 617 at temperatures of 950°C, 850°C, and 800°C with tensile hold times of 1 hr, 100 hr, and 1,000 hr are developed. Furthermore, creep-fatigue testing on two notch specimen geometries, shallow-notch and sharp V-notch, on Alloy 617 was performed 950°C to understand the multiaxial stress relaxation behavior. The experimental and numerical results on the notch specimens were compared with those on the standard uniaxial smooth bar specimens. The effect of multi-axial stress state combined with elastic follow-up on the stress relaxation behavior was investigated in this report.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Normalizing Flows for Microscopic Many-Body Calculations: An Application to the Nuclear Equation of State

We report that normalizing flows are a class of machine learning models used to construct a complex distribution through a bijective mapping of a simple base distribution. We demonstrate that normalizing flows are particularly well suited as a Monte Carlo integration framework for quantum many-body calculations that require the repeated evaluation of high-dimensional integrals across smoothly varying integrands and integration regions. As an example, we consider the finite-temperature nuclear equation of state. An important advantage of normalizing flows is the ability to build highly expressive models of the target integrand, which we demonstrate enables precise evaluations of the nuclear free energy and its derivatives. Furthermore, we show that a normalizing flow model trained on one target integrand can be used to efficiently calculate related integrals when the temperature, density, or nuclear force is varied. This work will support future efforts to build microscopic equations of state for numerical simulations of supernovae and neutron star mergers that employ state-of-the-art nuclear forces and many-body methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Measurement of the 252 Cf ⁢(sf) prompt fission neutron spectrum utilizing 12 C ⁡(𝑛, 𝑛) and 9 Be ⁢(𝑛, 𝑛) neutron scattering reference measurements

The 252 Cf spontaneous fission (sf), prompt fission neutron spectrum (PFNS) is a fundamental quantity for nuclear physics measurements of neutron-emitting reactions. This energy distribution of neutrons emitted from fission has been considered a neutron data standard for decades and has been utilized as a reference for neutron detection efficiency, validation of Monte Carlo simulations, benchmarking of dosimetry standards, and more. A significant portion of the global collection of nuclear data on neutron-induced reactions is correlated with the 252 Cf ⁢(sf) PFNS. Despite the reliance on this quantity by the nuclear physics community, the historical collection of 252 Cf PFNS measurements display systematic disagreements that are not understood or easily explained. These experimental discrepancies could potentially bias the 252 Cf PFNS Standard evaluation. On top of this, these past experiments frequently employed correlated experimental measurement or analysis methods. The artificial intelligence (AI)/machine learning (ML)-informed californium chi-nuclear data experiment (AIACHNE) project was formed to (a) investigate these discrepancies utilizing AI/ML methods to identify outlying regions of literature data, assign these regions to features of the experiment itself, and perform an improved evaluation of the 252 Cf PFNS and (b) perform a new experimental measurement of this quantity designed to improve upon the existing literature database. Here, in this work, we report on the AIACHNE 252 Cf PFNS experiment utilizing a new analysis method uncorrelated with all previous measurements: neutron efficiency determinations based on elastic neutron scattering on 12 C and 9 Be . This new method provides an independent test of the existing literature data and evaluation of the 252 Cf ⁢(sf) PFNS. The method is described with detailed covariance quantification procedures, as well as a direct discussion of the sources of uncertainty described as requirements in the “Templates” series of papers. The 252 Cf ⁢(sf) PFNS reported in this work agrees well with the overall shape of the existing standard PFNS evaluation as well as many literature measurements, thus verifying the current evaluation utilizing new techniques. However, the results suggest that there are deficiencies in the angle-differential 12 C and 9 Be ⁢(𝑛, 𝑛) evaluated nuclear data, which produce unphysical structures in the reported result. While these structures are relatively minor, they become obvious because of the high statistical precision of the data and the expected smooth continuity of the 252 Cf ⁢(sf) PFNS.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Symmetry breaking in an extended O(2) model

Motivated by attempts to quantum simulate lattice models with continuous Abelian symmetries using discrete approximations, we study an extended-O(2) model in two dimensions that differs from the ordinary O(2) model by the addition of an explicit symmetry breaking term − h q cos ( q φ ) . Its coupling h q allows to smoothly interpolate between the O(2) model ( h q = 0 ) and a q -state clock model ( h q → ∞ ). In the latter case, a q -state clock model can also be defined for noninteger values of q . Thus, such a limit can also be considered as an analytic continuation of an ordinary q -state clock model to noninteger q . In previous work, we established the phase diagram for noninteger q in the infinite coupling limit ( h q → ∞ ). We showed that there is a second-order phase transition at low temperature and a crossover at high temperature. In this work, we seek to establish the phase diagram at finite values of the coupling using Monte Carlo and tensor methods. We show that for noninteger q , the second-order phase transition at low temperature and crossover at high temperature persist to finite coupling. For integer q = 2 , 3, 4, we know there is a second-order phase transition at infinite coupling (i.e. the well-known clock models). At finite coupling, we find that the critical exponents for q = 3 , 4 vary with the coupling, and for q = 4 the transition may turn into a Berezinskii-Kosterlitz-Thouless transition at small coupling. We comment on the similarities and differences of the phase diagrams with those of quantum simulators of the Abelian-Higgs model based on ladder-shaped arrays of Rydberg atoms. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2026

Temperature-dependent changes in gas chromatographic separation metrics for trihexyl(tetradecyl)phosphonium-based ionic liquid stationary phases and comparison to conventional polysiloxane stationary phases

Here, to assess whether the trihexyl(tetradecyl)phosphonium chloride ([P 66614 + ][Cl - ]) ionic liquid (IL), employed as a gas chromatographic stationary phase, exhibits temperature-dependent phase transitions or structural heterogeneity, retention thermodynamics for probe molecules of varied chemical structure were investigated by gas chromatography (GC) over the temperature range of 25 to 64 °C using van’t Hoff analysis. All van’t Hoff plots for n-alkanes (C8–C12) and other probe analytes were linear, indicating the absence of detectable phase-transition behavior. The [P 66614 + ][Cl - ] IL was further compared with the [P 66614 + ] bis[(trifluoromethyl)sulfonyl]imide ([P 66614 + ][NTf 2 - ]) IL and commercial polysiloxane stationary phases in terms of temperature-dependent resolution behavior and chromatographic selectivity over the temperature range of 28–52 °C. The resolution factors of analytes in mixtures obtained using gas chromatography–mass spectrometry (GC–MS) and the summation of resolution values decreased smoothly with temperature on both IL stationary phases and on the dimethylphenylcyano-substituted polymeric stationary phase (OV-1701), whereas the poly(50% diphenyl/50% dimethyl siloxane) stationary phase (SPB-50) exhibited irregularities in its temperature-dependent summation of resolution profile, showing a distinct maximum in the temperature range of 34–40 °C. Selectivity values for the IL stationary phases were largely determined by the anion, with the [P 66614 + ][Cl - ] IL providing enhanced separation between polar and aromatic analytes, while the [P 66614 + ][NTf 2 - ] IL favored dispersive and π-π interactions that resulted in a reversal of elution order for some aliphatic–aromatic and alkyne-containing analyte pairs. Capillary columns prepared using different surface activation methods and coated with identical [P 66614 + ][Cl - ] IL stationary phase showed indistinguishable temperature-dependent trends in summation of resolution value and selectivity. An evaluation of analyte-specific selectivity provides insight into the characteristic separation behavior of the stationary phases and enables identification of analyte classes that are more efficiently separated on the [P 66614 + ]-based IL stationary phases compared to conventional stationary phase materials.

Chromatographic selectivity↗
DOE OSTI2020

Sublimation Kinetics for Individual Graphite and Graphene Nanoparticles (NPs): NP-to-NP Variations and Evolving Structure-Kinetics and Structure-Emissivity Relationships

A single nanoparticle (NP) mass spectrometry method was used to measure sublimation rates as a function of nanoparticle temperature (T NP ) for sets of individual graphite and graphene NPs. Initially, the NP sublimation rates were ~400 times faster than that for bulk graphite, and there were large NP-to-NP variations. Over time, the rates slowed substantially, though remaining well above the bulk rate. The initial activation energies (E a values) were correspondingly low and doubled as a few monolayer’s worth of material were sublimed from the surfaces. The high initial rates and low E a values are attributed to large numbers of edge, defect, and other low coordination sites on the NP surfaces, and the changes are attributed to atomic-scale “smoothing” of the surface by preferential sublimation of the less stable sites. The emissivity of the NPs also changed after heating, most frequently increasing. Here, the emissivity and sublimation rates were anti-correlated, leading to the conclusion that high densities of low-coordination sites on the NP surfaces enhances sublimation but suppresses emissivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗