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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The Relative Importance of Assumed Infrasound Source Terms and Effects of Atmospheric Models on the Linear Inversion of Infrasound Time Series at the Source Physics Experiment

We invert far-field infrasound data for the equivalent seismoacoustic time-domain moment tensor to assess the effects of variable atmospheric models and source phenomena. The infrasound data were produced by a series of underground chemical explosions that were conducted during the Source Physics Experiment (SPE), which was originally designed to study seismoacoustic signal phenomena. The first goal is to investigate the sensitivity of the inversion to the variability of the estimated atmospheric model. The second goal is to determine the relative contribution of two presumed source mechanisms to the observed infrasonic wavefield. Rather than using actual atmospheric observations to estimate the necessary atmospheric Green’s functions, we build a series of atmospheric models that rely on publicly available, regional-scale atmospheric observations. The atmospheric observations are summarized and interpolated onto a 3D grid to produce a model of sound speed at the time of the experiment. For each of four SPE acoustic datasets that we invert, we produced a suite of three atmospheric models for each chemical explosion event, based on 10 yrs of meteorological data: an average model, which averages the atmospheric conditions for 10 yrs prior to each SPE event, as well as two extrema models. To parameterize the inversion, we assume that the source of infrasonic energy results from the linear combination of explosion-induced surface spall and linear seismic-to-elastic mode conversion at the Earth’s free surface. We find that the inversion yields relatively repeatable results for the estimated spall source. Conversely, the estimated isotropic explosion source is highly variable. This suggests that 1) the majority of the observed acoustic energy is produced by the spall and/or 2) our modeling of the elastic energy, and the subsequent conversion to acoustic energy, is too simplistic.

58 GEOSCIENCES↗
DOE OSTI2020

Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing twelve photo-z algorithms applied to mock data produced forLarge Synoptic Survey Telescope The Rubin Observatory Legacy Survey of Space and Time (lsst) Dark Energy Science Collaboration (desc). By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs to each code, we demonstrate the impact of the assumptions underlying each technique on the output photo-z PDFs. In the absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret multiple metrics of the ensemble properties of the derived photo-z PDFs as well as traditional reductions to photo-z point estimates. Additionally, we report systematic biases and overall over/under-breadth of the photo-z PDFs of many popular codes, which may indicate avenues for improvement in the algorithms or implementations. Furthermore, we raise attention to the limitations of established metrics for assessing photo-z PDF accuracy; though we identify the conditional density estimate (CDE) loss as a promising metric of photo-z PDF performance in the case where true redshifts are available but true photo-z PDFs are not, we emphasize the need for science-specific performance metrics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Chemical Potential of Metal Atoms in Supported Nanoparticles: Dependence upon Particle Size and Support

Metal nanoparticles supported on oxide surfaces form the basis for many industrial catalysts and promise to play an ever-increasing role in future energy and environmental technologies. The chemical potential (μ) of the metal atoms in these particles depends strongly on particle size and support and is an important factor that determines their catalytic properties, including their binding strengths to adsorbed reaction intermediates and their long-term stability against sintering. We present here a method for estimating this chemical potential as a function of particle size for different metal/oxide combinations. We show that this chemical potential for late transition metals is well approximated for a particle of diameter D by μ( D ) = [(3γ m – E adh )(1 + D o / D )](2 V m / D ), where γ m is the surface energy of the bulk metal, E adh is the adhesion energy at the bulk metal/oxide interface, and D o is ~1.5 nm, and V m is the molar volume of the bulk metal. We further show that E adh increases with (1) increasing heat of formation of the most stable oxide of the metal from metal gas atoms plus O 2 (gas) per mole of metal atoms, (2) decreasing enthalpy of reduction of the oxide to its next lower oxidation state plus O 2 (gas), per mole of oxygen atoms, and (3) increasing density of surface oxygen atoms on the oxide surface. The linear scaling of E adh with these properties allows estimations of E adh for a variety of metal/oxide combinations. Using this E adh estimate in the above equation with known values for γ m allows estimates of metal chemical potential versus particle size for late transition metals on various oxide supports. WE conclude this will improve our ability to understand structure–property relations in catalysis and design better catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Influence of defects on the thermal conductivity of compressed LiF

We report defect formation in LiF, which is used as an observation window in ramp and shock experiments, has significant effects on its transmission properties. Given the extreme conditions of the experiments it is hard to measure the change in transmission directly. Using molecular dynamics, we estimate the change in conductivity as a function of the concentration of likely point and extended defects using a Green-Kubo technique with careful treatment of size effects. With this data, we form a model of the mean behavior and its estimated error; then, we use this model to predict the conductivity of a large sample of defective LiF resulting from a direct simulation of ramp compression as a demonstration of the accuracy of its predictions. Given estimates of defect densities in a LiF window used in an experiment, the model can be used to correct the observations of thermal energy through the window. Also, the methodology we develop is extensible to modeling, with quantified uncertainty, the effects of a variety of defects on the thermal conductivity of solid materials.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Comparison of measured and estimated maximum skin doses during CT fluoroscopy lung biopsies

Purpose: To measure patient-specific maximum skin dose (MSD) associated with CT fluoroscopy (CTF) lung biopsies and to compare measured MSD with the MSD estimated from phantom measurements, as well as with the CTDIvol of patient examinations. Methods: Data from 50 patients with lung lesions who underwent a CT fluoroscopy-guided biopsy were collected. The CT protocol consisted of a low-kilovoltage (80 kV) protocol used in combination with an algorithm for dose reduction to the radiology staff during the interventional procedure, HandCare (HC). MSD was assessed during each intervention using EBT2 gafchromic films positioned on patient skin. Lesion size, position, total fluoroscopy time, and patient-effective diameter were registered for each patient. Dose rates were also estimated at the surface of a normal-size anthropomorphic thorax phantom using a 10 cm pencil ionization chamber placed at every 30°, for a full rotation, with and without HC. Measured MSD was compared with MSD values estimated from the phantom measurements and with the cumulative CTDIvol of the procedure. Results: The median measured MSD was 141 mGy (range 38–410 mGy) while the median cumulative CTDIvol was 72 mGy (range 24–262 mGy). The ratio between the MSD estimated from phantom measurements and the measured MSD was 0.87 (range 0.12–4.1) on average. In 72% of cases the estimated MSD underestimated the measured MSD, while in 28% of the cases it overestimated it. The same trend was observed for the ratio of cumulative CTDIvol and measured MSD. No trend was observed as a function of patient size. Conclusions: On average, estimated MSD from dose rate measurements on phantom as well as from CTDIvol of patient examinations underestimates the measured value of MSD. This can be attributed to deviations of the patient's body habitus from the standard phantom size and to patient positioning in the gantry during the procedure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

ASCR Workshop Position Paper: Challenges and Opportunities in High Energy Physics

High energy particle physics and cosmology concern themselves with estimating fundamental parameters of nature, such as the masses and interactions of fundamental particles like the Higgs boson and the rate of expansion of the universe. In doing so, they analyze exabyte-scale datasets, some of the largest in all of science, and face many challenges in subsequent data analysis. These challenges are shared between the two disciplines, but we focus on particle physics to highlight one specific domain. In particle physics, the standard method for estimating parameters involves performing Monte Carlo (MC) integration as a function of both parameters of interest and nuisance parameters using an expensive simulator, counting the number of observed collision events (i.i.d. samples) from an experiment in the corresponding integration domains, and forming a Poisson likelihood function. This likelihood function is then used in a Frequentist manner to construct a maximum likelihood point estimate (MLE) and confidence set for the parameters. To sufficiently populate the high-dimensional integration domains, simulators consume billions of CPU-hours annually and produce hundreds of petabytes of intermediate output data. Several techniques have been developed to: optimize definitions of the integration domains so as to be maximally sensitive to a particular subset of parameters, efficiently estimate the integrals, and build robust surrogate models by interpolating between integral evaluations at different parameter points. One can view this whole endeavor as classical Simulation-Based Inference (SBI).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

ELLIPTICAL GALAXY MASSES OUT TO FIVE EFFECTIVE RADII: THE REALM OF DARK MATTER

We estimate the masses of elliptical galaxies out to five effective radii using planetary nebulae and globular clusters as tracers. A sample of 15 elliptical galaxies with a broad variation in mass is compiled from the literature. A distribution function-maximum likelihood analysis is used to estimate the overall potential slope, normalization, and velocity anisotropy of the tracers. We assume power-law profiles for the potential and tracer density and a constant velocity anisotropy. The derived potential power-law indices lie in between the isothermal and Keplerian regime and vary with mass: there is tentative evidence that the less massive galaxies have steeper potential profiles than the more massive galaxies. We use stellar mass-to-light ratios appropriate for either a Chabrier/KTG (Kroupa, Tout and Gilmore) or Salpeter initial mass function to disentangle the stellar and dark matter components. The fraction of dark matter within five effective radii increases with mass, in agreement with several other studies. We employ simple models to show that a combination of star formation efficiency and baryon extent are able to account for this trend. These models are in good agreement with both our measurements out to five effective radii and recent Sloan Lens ACS Survey measurements within one effective radii when a universal Chabrier/KTG initial mass function is adopted.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Density functional modeling of the binding energies between aluminosilicate oligomers and different metal cations

Interactions between negatively charged aluminosilicate species and positively charged metal cations are critical to many important engineering processes and applications, including sustainable cements and aluminosilicate glasses. In an effort to probe these interactions, here we have calculated the pair-wise interaction energies (i.e., binding energies) between aluminosilicate dimer/trimer and 17 different metal cations M n+ (M n+ = Li + , Na + , K + , Cu + , Cu 2+ , Co 2+ , Zn 2+ , Ni 2+ , Mg 2+ , Ca 2+ , Ti 2+ , Fe 2+ , Fe 3+ , Co 3+ , Cr 3+ , Ti 4+ and Cr 6+ ) using a density functional theory (DFT) approach. Analysis of the DFT-optimized structural representations for the clusters (dimer/trimer + M n+ ) shows that their structural attributes (e.g., interatomic distances) are generally consistent with literature observations on aluminosilicate glasses. The DFT-derived binding energies are seen to vary considerably depending on the type of cations (i.e., charge and ionic radii) and aluminosilicate species (i.e., dimer or trimer). A survey of the literature reveals that the difference in the calculated binding energies between different M n+ can be used to explain many literature observations associated with the impact of metal cations on materials properties (e.g., glass corrosion, mineral dissolution, and ionic transport). Analysis of all the DFT-derived binding energies reveals that the correlation between these energy values and the ionic potential and field strength of the metal cations are well captured by 2nd order polynomial functions ( R 2 values of 0.99–1.00 are achieved for regressions). Given that the ionic potential and field strength of a given metal cation can be readily estimated using well-tabulated ionic radii available in the literature, these simple polynomial functions would enable rapid estimation of the binding energies of a much wider range of cations with the aluminosilicate dimer/trimer, providing guidance on the design and optimization of sustainable cements and aluminosilicate glasses and their associated applications. Finally, the limitations associated with using these simple model systems to model complex interactions are also discussed.

Gong, Kai↗
DOE OSTI2022

Uncertainty reduction in residual stress measurements by an optimised inverse solution using nonconsecutive polynomials

Many destructive methods for measuring residual stresses such as the slitting method require an inverse analysis to solve the problem. The accuracy of the result as well as an uncertainty component (the model uncertainty) depends on the basis functions used in the inverse solution. The use of a series expansion as the basis functions for the inverse solution was analysed in a previous work for the particular case where functions orders grew consecutively. The present work presents a new estimation of the model uncertainty and a new improved methodology to select the final basis functions for the case where the basis is composed of polynomials. Including nonconsecutive polynomial orders in the basis generates a larger space of possible solutions to be evaluated and allows the possibility to include higher-order polynomials. The paper includes a comparison with two other inverse analyses methodologies applied to synthetically generated data. With the new methodology, the final error is reduced and the uncertainty estimation improved.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Theoretical Model for Volume Fraction of UC, 235U Enrichment, and Effective Density of Final U 10Mo Alloy

The purpose of this document is to provide a theoretical framework for (1) estimating uranium carbide (UC) volume fraction in a final alloy of uranium with 10 weight percent molybdenum (U-10Mo) as a function of final alloy carbon concentration, and (2) estimating effective 235 U enrichment in the U-10Mo matrix after accounting for loss of 235 U in forming UC. This report will also serve as a theoretical baseline for effective density of as-cast low-enriched U-10Mo alloy. Therefore, this report will serve as the baseline for quality control of final alloy carbon content

36 MATERIALS SCIENCE↗
DOE OSTI2015

Initial Probabilistic Evaluation of Reactor Pressure Vessel Fracture with Grizzly and Raven

The Grizzly code is being developed with the goal of creating a general tool that can be applied to study a variety of degradation mechanisms in nuclear power plant components. The first application of Grizzly has been to study fracture in embrittled reactor pressure vessels (RPVs). Grizzly can be used to model the thermal/mechanical response of an RPV under transient conditions that would be observed in a pressurized thermal shock (PTS) scenario. The global response of the vessel provides boundary conditions for local models of the material in the vicinity of a flaw. Fracture domain integrals are computed to obtain stress intensity factors, which can in turn be used to assess whether a fracture would initiate at a pre-existing flaw. These capabilities have been demonstrated previously. A typical RPV is likely to contain a large population of pre-existing flaws introduced during the manufacturing process. This flaw population is characterized stastistically through probability density functions of the flaw distributions. The use of probabilistic techniques is necessary to assess the likelihood of crack initiation during a transient event. This report documents initial work to perform probabilistic analysis of RPV fracture during a PTS event using a combination of the RAVEN risk analysis code and Grizzly. This work is limited in scope, considering only a single flaw with deterministic geometry, but with uncertainty introduced in the parameters that influence fracture toughness. These results are benchmarked against equivalent models run in the FAVOR code. When fully developed, the RAVEN/Grizzly methodology for modeling probabilistic fracture in RPVs will provide a general capability that can be used to consider a wider variety of vessel and flaw conditions that are difficult to consider with current tools. In addition, this will provide access to advanced probabilistic techniques provided by RAVEN, including adaptive sampling and parallelism, which can dramatically decrease run times.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Covering complete proteomes with X-ray structures: a current snapshot

The current and the attainable coverage by X-ray structures of proteins and their functions on the scale of the ‘protein universe’ are estimated. A detailed analysis of the coverage across nearly 2000 proteomes from all superkingdoms of life and functional annotations is performed, with particular focus on the human proteome and the family of GPCR proteins. Structural genomics programs have developed and applied structure-determination pipelines to a wide range of protein targets, facilitating the visualization of macromolecular interactions and the understanding of their molecular and biochemical functions. The fundamental question of whether three-dimensional structures of all proteins and all functional annotations can be determined using X-ray crystallography is investigated. A first-of-its-kind large-scale analysis of crystallization propensity for all proteins encoded in 1953 fully sequenced genomes was performed. It is shown that current X-ray crystallographic knowhow combined with homology modeling can provide structures for 25% of modeling families (protein clusters for which structural models can be obtained through homology modeling), with at least one structural model produced for each Gene Ontology functional annotation. The coverage varies between superkingdoms, with 19% for eukaryotes, 35% for bacteria and 49% for archaea, and with those of viruses following the coverage values of their hosts. It is shown that the crystallization propensities of proteomes from the taxonomic superkingdoms are distinct. The use of knowledge-based target selection is shown to substantially increase the ability to produce X-ray structures. It is demonstrated that the human proteome has one of the highest attainable coverage values among eukaryotes, and GPCR membrane proteins suitable for X-ray structure determination were determined.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Fitting Matérn smoothness parameters using automatic differentiation

The Mat$\acute{e}$rn covariance function is ubiquitous in the application of Gaussian processes to spatial statistics and beyond. Perhaps the most important reason for this is that the smoothness parameter $\nu$ gives complete control over the mean-square differentiability of the process, which has significant implications for the behavior of estimated quantities such as interpolants and forecasts. Unfortunately, derivatives of the Mat$\acute{e}$rn covariance function with respect to $\nu$ require derivatives of the modified second-kind Bessel function $K$ $\nu$ with respect to $\nu$. While closed form expressions of these derivatives do exist, they are prohibitively difficult and expensive to compute. For this reason, many software packages require fixing $\nu$ as opposed to estimating it, and all existing software packages that attempt to offer the functionality of estimating $\nu$ use finite difference estimates for $\partial$ $\nu$ $K$ $\nu$ . In this work, we introduce a new implementation of $K$$\nu$ that has been designed to provide derivatives via automatic differentiation (AD), and whose resulting derivatives are significantly faster and more accurate than those computed using finite differences. Here, we provide comprehensive testing for both speed and accuracy and show that our AD solution can be used to build accurate Hessian matrices for second-order maximum likelihood estimation in settings where Hessians built with finite difference approximations completely fail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

New evolution equations for the joint response-excitation probability density function of stochastic solutions to first-order nonlinear PDEs

By using functional integral methods we determine new evolution equations satisfied by the joint response-excitation probability density function (PDF) associated with the stochastic solution to first-order nonlinear partial differential equations (PDEs). The theory is presented for both fully nonlinear and for quasilinear scalar PDEs subject to random boundary conditions, random initial conditions or random forcing terms. Particular applications are discussed for the classical linear and nonlinear advection equations and for the advection-reaction equation. By using a Fourier-Galerkin spectral method we obtain numerical solutions of the proposed response-excitation PDF equations. These numerical solutions are compared against those obtained by using more conventional statistical approaches such as probabilistic collocation and multi-element probabilistic collocation methods. It is found that the response-excitation approach yields accurate predictions of the statistical properties of the system. In addition, it allows to directly ascertain the tails of probabilistic distributions, thus facilitating the assessment of rare events and associated risks. The computational cost of the response-excitation method is order magnitudes smaller than the one of more conventional statistical approaches if the PDE is subject to high-dimensional random boundary or initial conditions. The question of high-dimensionality for evolution equations involving multidimensional joint response-excitation PDFs is also addressed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Learning concave-convex profiles of data transport over dedicated connections

Dedicated data transport infrastructures are increasingly being deployed to support distributed big-data and high-performance computing scenarios. These infrastructures employ data transfer nodes that use sophisticated software stacks to support network transport among sites, which often house distributed file and storage systems. Throughput measurements collected over such infrastructures for a range of round trip times (RTTs) reflect the underlying complex end-to-end connections, and have revealed dichotomous throughput profiles as functions of RTT. In particular, concave regions of throughput profiles at lower RTTs indicate near-optimal performance, and convex regions at higher RTTs indicate bottlenecks due to factors such as buffer or credit limits. We present a machine learning method that explicitly infers these concave and convex regions and transitions between them using sigmoid functions. We also provide distribution-free confidence estimates for the generalization error of these concave-convex profile estimates. Throughput profiles for data transfers over 10 Gbps connections with 0–366 ms RTT provide important performance insights, including the near optimality of transfers performed with the XDD tool between XFS filesystems, and the performance limits of wide-area Lustre extensions using LNet routers. A direct application of generic machine learning packages does not adequately highlight these critical performance regions or provide as precise confidence estimates.

Rao, Nageswara↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2020

Simple hydrogenic estimates for the exchange and correlation energies of atoms and atomic ions, with implications for density functional theory

Exact density functionals for the exchange and correlation energies are approximated in practical calculations for the ground-state electronic structure of a many-electron system. An important exact constraint for the construction of approximations is to recover the correct non-relativistic large-Z expansions for the corresponding energies of neutral atoms with atomic number Z and electron number N = Z, which are correct to leading order (–0.221 Z 5/3 and –0.021 Z ln Z respectively) even in the lowest-rung or local density approximation. We find that hydrogenic densities lead to E x (N,Z) ≈ –0.354 N 2/3 Z (as known before only for Z >> N >> 1) and E c ≈ –0.02 N ln N . These asymptotic estimates are most correct for atomic ions with large N and Z >> N, but we find that they are qualitatively and semi- quantitatively correct even for small N and for N ≈ Z. The large-N asymptotic behavior of the energy is pre-figured in small-N atoms and atomic ions, supporting the argument that widely-predictive approximate density functionals should be designed to recover the correct asymptotics. Here, it is shown that the exact Kohn-Sham correlation energy, when calculated from the pure ground-state wavefunction, should have no contribution proportional to Z in the Z → ∞ limit for any fixed N.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Optical Thin Films for Gas Sensing in Advanced Coal Fired Power Plants

Even for existing coal based plants, the opportunity for sensors and controls to improve efficiency is great. A wide range of gas species are of interest for relevant applications. Functional sensor layers for embedded sensing must be compatible with extreme conditions (temperature, pressure, corrosive). Au incorporated metal oxides have been looked at by a number of other authors previously for gas sensing, but have often focused on temperatures below 500{degree}C. Au nanoparticle incorporated metal oxide thin films have shown enhanced gas sensing response. In prior work, we have demonstrated that material systems such as Au nanoparticle incorporated TiO{sub 2} films exhibit a potentially useful optical response to changing gas atmospheres at temperatures up to ~800-850{degree}C. Current work is focused on sputter-deposited Au/TiO{sub 2} films. Au and Ti are multi-layered sputter deposited, followed by a 950{degree}C oxidation step. Increasing Au layer thickness yields larger particles. Interband electronic transitions significantly modify the optical constants of Au as compared to the damped free electron theory. A high temperature oxidation (20%O{sub 2}/N{sub 2}) treatment was performed at 700{degree}C followed by a reduction (4%H{sub 2}/N{sub 2}) treatment to illustrate the shift in both absorption and scattering with exposure to reducing gases. Shift of localized surface plasmon resonance (LSPR) absorption peak in changing gas atmospheres is well documented, but shift in the peak associated with diffuse scattering is a new observation. Increasing Au layer-thickness results in an increase in LSPR absorption and a shift to longer wavelengths. Diffuse scattering associated with the LSPR resonance of Au shows a similar trend with increasing Au thickness. To model the temperature dependence of LSPR, the modification to the plasmon frequency, the damping frequency, and the dielectric constant of the oxide matrix must be accounted for. Thermal expansion of Au causes a decrease in free-carrier concentration and plasmon frequency with increasing temperature. Increased scattering of electrons with increasing temperatures results in an increased damping frequency for free carriers. The thermo-optic coefficient of TiO{sub 2} is assumed to be constant or decreasing with increasing temperature. A Custom Designed System Can Be Used to Monitor Optical Transmission and Reflection of Films at Elevated Temperatures and Upon Exposure to Changing Ambient Gas Atmospheres. The calculated extinction peak broadens and reduces peak height with increasing temperature. Extinction spectra measured using the elevated temperature system deviate from measurements performed with an integrating sphere. Modifications to measured transmittance and reflectance spectra are observed with increasing temperatures. Optical constants of Au nanoparticles were measured for elevated temperature. The thermo-optic coefficient of TiO{sub 2} strongly affects the shift in LSPR absorption peak. Peak broadening is dictated by the increased damping frequency of Au. In all cases of the theoretical modeling, we are assuming a particle of Au embedded in a TiO{sub 2} matrix without any interparticle interaction. Localized surface plasmon resonance in noble metal nanoparticles is associated with the free electrons. Surface charges create an internal field that acts as a restoring force on displaced charge carriers resulting in an oscillation with an associated resonance. An estimate of the sensitivity as a function of wavelength for sensing done using a single wavelength transmission measurement can be derived by focusing on the partial derivative of Q{sub abs} with respect to the material parameter that we assume is changing during the experiment. We focus on the free carrier density. However, other material parameters may also change in some cases. Peak sensitivity for transmission or absorption based sensing occurs on either side of the LSPR absorption peak maximum. Theoretically predicted wavelength dependence is consistent with the literature. The wavelength of LSPR absorption peak sensitivity shifts with increasing temperature due to broadening and peak shifting. The maximum sensitivity decreases with increasing temperature. The sensitivity should also depend on the mechanism responsible for the solid-gas interaction.

36 MATERIALS SCIENCE↗