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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Evaluation of image guided motion management methods in lung cancer radiotherapy

Purpose: To evaluate the accuracy and reliability of three target localization methods for image guided motion management in lung cancer radiotherapy. Methods: Three online image localization methods, including (1) 2D method based on 2D cone beam (CB) projection images, (2) 3D method using 3D cone beam CT (CBCT) imaging, and (3) 4D method using 4D CBCT imaging, have been evaluated using a moving phantom controlled by (a) 1D theoretical breathing motion curves and (b) 3D target motion patterns obtained from daily treatment of 3 lung cancer patients. While all methods are able to provide target mean position (MP), the 2D and 4D methods can also provide target motion standard deviation (SD) and excursion (EX). For each method, the detected MP/SD/EX values are compared to the analytically calculated actual values to calculate the errors. The MP errors are compared among three methods and the SD/EX errors are compared between the 2D and 4D methods. In the theoretical motion study (a), the dependency of MP/SD/EX error on EX is investigated with EX varying from 2.0 cm to 3.0 cm with an increment step of 0.2 cm. In the patient motion study (b), the dependency of MP error on target sizes (2.0 cm and 3.0 cm), motion patterns (four motions per patient) and EX variations is investigated using multivariant linear regression analysis. Results: In the theoretical motion study (a), the MP detection errors are −0.2 ± 0.2, −1.5 ± 1.1, and −0.2 ± 0.2 mm for 2D, 3D, and 4D methods, respectively. Both the 2D and 4D methods could accurately detect motion pattern EX (error < 1.2 mm) and SD (error < 1.0 mm). In the patient motion study (b), MP detection error vector (mm) with the 2D method (0.7 ± 0.4) is found to be significantly less than with the 3D method (1.7 ± 0.8,p < 0.001) and the 4D method (1.4 ± 1.0, p < 0.001) using paired t-test. However, no significant difference is found between the 4D method and the 3D method. Based on multivariant linear regression analysis, the variances of MP error in SI direction explained by target sizes, motion patterns, and EX variations are 9% with the 2D method, 74.4% with the 3D method, and 27% with the 4D method. The EX/SD detection errors are both < 1.0 mm for the 2D method and < 2.0 mm for the 4D method. Conclusions: The 2D method provides the most accurate MP detection regardless of the motion pattern variations, while its performance is limited by the accuracy of target identification in the projection images. The 3D method causes the largest error in MP determination, and its accuracy significantly depends on target sizes, motion patterns, and EX variations. The 4D method provides moderate MP detection results, while its accuracy relies on a regular motion pattern. In addition, the 2D and 4D methods both provide accurate measurement of the motion SD/EX, providing extra information for motion management.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Project report for year 1

In the first year of the project, we developed a fire event tracking system using active fire detections recorded by the Visible Infrared Imaging Radiometer Suite (VIIRS). We employed this algorithm to track all large fires that occurred in California from 2013 to 2020, and created a retrospective Fire Event Data Suite (FEDS) that contains the vector shapes of fire perimeters, active fire fronts, and the locations and fire radiative powers for each fire pixel at 12-hour temporal resolution for each fire event. Using the 2020 Creek Fire as a test bed, we also delineated hourly fire perimeters by combining the FEDS data with the hourly rates of spread derived from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) fire data. This dataset will form the basis of the near-real-time hourly fire emissions product that we plan to develop at years 2 and 3 of the project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 $\frac{𝑀𝐻𝑧}{°C}$. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2016

Signatures from scalar dark matter with a vector-like quark mediator

We present a comprehensive study of a model where the dark matter is composed of a singlet real scalar that couples to the Standard Model predominantly via a Yukawa interaction with a light quark and a colored vector-like fermion. A distinctive feature of this scenario is that thermal freeze-out in the early universe may be driven by annihilation both into gluon pairs at one-loop (gg) and by virtual internal Bremsstrahlung of a gluon (q q-bar g). Such a dark matter candidate may also be tested through direct and indirect detection and at the LHC; viable candidates have either a mass nearly degenerate with that of the fermionic mediator or a mass above about 2 TeV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Smad4 mediated BMP2 signal is essential for the regulation of GATA4 and Nkx2.5 by affecting the histone H3 acetylation in H9c2 cells

Highlights: • BMP2 can upregulated cardiac related gene GATA4, Nkx2.5, MEF2c and Tbx5. • Inhibition of Smad4 decreased BMP2-induced hyperacetylation of histone H3. • Inhibition of Smad4 diminished BMP2-induced overexpression of GATA4 and Nkx2.5. • Inhibition of Smad4 decreased hyperacetylated H3 in the promoter of GATA4 and Nkx2.5. • Smad4 is essential for BMP2 induced hyperacetylated histone H3. - Abstract: BMP2 signaling pathway plays critical roles during heart development, Smad4 encodes the only common Smad protein in mammals, which is a pivotal nuclear mediator. Our previous studies showed that BMP2 enhanced the expression of cardiac transcription factors in part by increasing histone H3 acetylation. In the present study, we tested the hypothesis that Smad4 mediated BMP2 signaling pathway is essential for the expression of cardiac core transcription factors by affecting the histone H3 acetylation. We successfully constructed a lentivirus-mediated short hairpin RNA interference vector targeting Smad4 (Lv-Smad4) in rat H9c2 embryonic cardiac myocytes (H9c2 cells) and demonstrated that it suppressed the expression of the Smad4 gene. Cultured H9c2 cells were transfected with recombinant adenoviruses expressing human BMP2 (AdBMP2) with or without Lv-Smad4. Quantitative real-time RT-PCR analysis showed that knocking down of Smad4 substantially inhibited both AdBMP2-induced and basal expression levels of cardiac transcription factors GATA4 and Nkx2.5, but not MEF2c and Tbx5. Similarly, chromatin immunoprecipitation (ChIP) analysis showed that knocking down of Smad4 inhibited both AdBMP2-induced and basal histone H3 acetylation levels in the promoter regions of GATA4 and Nkx2.5, but not of Tbx5 and MEF2c. In addition, Lv-Smad4 selectively suppressed AdBMP2-induced expression of HAT p300, but not of HAT GCN5 in H9c2 cells. The data indicated that inhibition of Smad4 diminished both AdBMP2 induced and basal histone acetylation levels in the promoter regions of GATA4 and Nkx2.5, suggesting that Smad4 mediated BMP2 signaling pathway was essential for the regulation of GATA4 and Nkx2.5 by affecting the histone H3 acetylation in H9c2 cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Broad-band trajectory mechanics

We present here a trajectory-based solution to the elastodynamic equation of motion that is valid across a wide range of seismic frequencies. That is, the derivation of the solution does not invoke a high-frequency assumption or require that the medium have smoothly varying properties. The approach, adopted from techniques used in quantum dynamics, produces a set of coupled ordinary differential equations for the trajectory, the slowness vector and the elastic wave amplitude along the ray path. The trajectories may be determined by a direct solution of the governing equations or derived as the by-product of a numerical wavefield simulation. Synthetic tests with interfaces and layers containing increasingly narrow transition zones indicate that the conventional high-frequency trajectories associated with the eikonal equation bend too sharply into high-velocity regions as the wavelength exceeds the transition zone width. Tests in a velocity model, based upon mapped structural surfaces from the Geysers geothermal field in California, indicate that discrepancies between high-frequency and broad-band trajectories can exceed several hundred metres at wavelengths of 1 Hz. An application to a crosswell tomographic imaging experiment demonstrates that the technique provides a basis for the seismic monitoring of fluid flow along narrow features such as fracture zones.

58 GEOSCIENCES↗
DOE OSTI2019

Machine learning applied to wind turbine blades impact detection

The significant development of wind power generation worldwide brings, together with environmental benefits, wildlife concerns, especially for volant species vulnerability to interactions with wind energy facilities. For surveying such events, an automatic system for continuous monitoring of blade collisions is critical. An onboard multi-senor system capable of providing real-time collision detection using integrated vibration sensors is developed and successfully tested. However, to detect low signal-to-noise ratio impact can be challenging; hence, an advanced impact detection method has been developed and presented in this article. A robust automated detection algorithm based on support vector machine is proposed. After a preliminary signal pre-processing, geometric features specifically selected for their sensitivity to impact signals are extracted from raw vibration signal and energy distribution graph. The predictive model is formulated by training conventional support vector machine using extracted features for impact identification. Finally, the performance of the predictive model is evaluated by accuracy, precision, and recall. Results indicate a linear regression relationship between signal-to-noise ratio and model overall performance. Here, the proposed method is much reliable on higher signal-to-noise ratio (SNR≥6), but it shows to be ineffective at lower signal-to-noise ratio (SNR<2).

17 WIND ENERGY↗
DOE OSTI2016

Evaluation of the setup margins for cone beam computed tomography–guided cranial radiosurgery: A phantom study

The aim of this study is to evaluate the setup margins from the clinical target volume (CTV) to planning target volume (PTV) for cranial stereotactic radiosurgery (SRS) treatments guided by cone beam computed tomography (CBCT). We designed an end-to-end (E2E) test using a skull phantom with an embedded 6mm tungsten ball (target). A noncoplanar plan was computed (E2E plan) to irradiate the target. The CBCT-guided positioning of the skull phantom on the linac was performed. Megavoltage portal images were acquired after 15 independent deliveries of the E2E plan. The displacement 2-dimensional (2D) vector between the centers of the square field and the ball target on each portal image was used to quantify the isocenter accuracy. Geometrical margins on each patient's direction (left-right or LR, anterior-posterior or AP, superior-inferior or SI) were calculated. Dosimetric validation of the margins was performed in 5 real SRS cases: 3-dimesional (3D) isocenter deviations were mimicked, and changes in CTV dose coverage and organs-at-risk (OARs) dosage were analyzed. The CTV-PTV margins of 1.1 mm in LR direction, and 0.7 mm in AP and SI directions were derived from the E2E tests. The dosimetric analysis revealed that a 1-mm uniform margin was sufficient to ensure the CTV dose coverage, without compromising the OAR dose tolerances. The effect of isocenter uncertainty has been estimated to be 1 mm in our CBCT-guided SRS approach.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Boosted dark matter and its implications for the features in IceCube HESE data

Here we study the implications of the premise that any new, relativistic, highly energetic neutral particle that interacts with quarks and gluons would create cascade-like events in the IceCube (IC) detector. Such events would be observationally indistinguishable from neutral current deep-inelastic (DIS) scattering events due to neutrinos. Consequently, one reason for deviations, breaks or excesses in the expected astrophysical power-law neutrino spectrum could be the flux of such a particle. Motivated by features in the recent 1347-day IceCube high energy starting event (HESE) data, we focus on particular boosted dark matter (χ) related realizations of this premise. Here, χ is assumed to be much lighter than, and the result of, the slow decay of a massive scalar (φ ) which constitutes a major fraction of the Universe's dark matter (DM) . We show that this hypothesis, coupled with a standard power-law astrophysical neutrino flux is capable of providing very good fits to the present data, along with a possible explanation of other features in the HESE sample. These features include a) the paucity of events beyond ~ 2 PeV b) a spectral feature resembling a dip or a spectral change in the 400 TeV–1 PeV region and c) an excess in the 50-100 TeV region. We consider two different boosted DM scenarios, and determine the allowed mass ranges and couplings for four different types of mediators (scalar, pseudoscalar, vector and axial-vector) which could connect the standard and dark sectors. We consider constraints from gamma-ray observations and collider searches. We find that the gamma-ray observations provide the most restrictive constraints, disfavouring the 1σ allowed parameter space from IC fits, while still being consistent with the 3σ allowed region. We also test our proposal and its implications against the (statistically independent) sample of six year through-going muon track data recently released by IceCube.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. A regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Predisposing Factors of Liver Necrosis after Transcatheter Arterial Chemoembolization in Liver Metastases from Neuroendocrine Tumor

PurposeTo investigate predictive factors for liver necrosis after transcatheter arterial chemoembolization (TACE) of neuroendocrine liver metastases.MethodsA total of 164 patients receiving 374 TACE were reviewed retrospectively to analyze predictive factors of liver necrosis. We analyzed patient age and sex; metastasis number and location; percentage of liver involvement; baseline liver function test; and pretreatment imaging abnormalities such as bile duct dilatation (BDD), portal vein narrowing (PVN), and portal vein thrombosis (PVT). We analyzed TACE technique such as Lipiodol or drug-eluting beads (DEB) as the drug’s vector; dose of chemotherapy; diameter of DEB; and number, frequency, and selectivity of TACE.ResultsLiver necrosis developed after 23 (6.1 %) of 374 TACE. In multivariate analysis, DEB > 300 μm in size induced more liver necrosis compared to Lipiodol (odds ratio [OR] 35.20; p < 0.0001) or with DEB < 300 μm in size (OR 19.95; p < 0.010). Pretreatment BDD (OR 119.64; p < 0.0001) and PVT (OR 9.83; p = 0.030) were predictive of liver necrosis. BDD or PVT responsible for liver necrosis were present before TACE in 59 % (13 of 22) and were induced by a previous TACE in 41 % (9 of 22) of cases.ConclusionDEB > 300 μm in size, BDD, and PVT are responsible for increased rate of liver necrosis after TACE. Careful analysis of BDD or PVT on pretreatment images as well as images taken between two courses can help avoid TACE complications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility

Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Furthermore, our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Xylella fastidiosa, a new plant pathogen that threatens global farming: Ecology, molecular biology, search for remedies

Recently, the emergence of an important alien plant pathogen in Europe was evident when the Olive Quick Decline Syndrome (OQDS), a previously unknown disease causing rapid scorching and death of the trees, invested with particular virulence a substantial portion of the vast olive wood of Southern Italy (Salento, part of the Apulia region). Early evidence indicated a connection between the OQDS and the gram-negative bacterium Xylella fastidiosa. This bacterium can target several important crops, so that researchers from all over the world have investigated its association with host plants and vectors, the molecular biology of the infection mechanism, and the molecular reaction of the infected plants. Potentially resistant or tolerant cultivars and molecular targets which might be useful to control the infection have been identified. In vitro tests of compounds active against Xylella have also been performed. In this contribution, the literature and the available data will be reviewed to provide an up-to-date picture of the currently available knowledge on the role of Xylella in OQDS, and in diseases of other plants, with focus on the emerging threats to European farming.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Stage-parallel fully implicit Runge–Kutta solvers for discontinuous Galerkin fluid simulations

In this paper, we develop new techniques for solving the large, coupled linear systems that arise from fully implicit Runge–Kutta methods. This method makes use of the iterative preconditioned GMRES algorithm for solving the linear systems, which has seen success for fluid flow problems and discontinuous Galerkin discretizations. By transforming the resulting linear system of equations, one can obtain a method which is much less computationally expensive than the untransformed formulation, and which compares competitively with other time-integration schemes, such as diagonally implicit Runge–Kutta (DIRK) methods. We develop and test several ILU-based preconditioners effective for these large systems. We additionally employ a parallel-in-time strategy to compute the Runge–Kutta stages simultaneously. Numerical experiments are performed on the Navier–Stokes equations using Euler vortex and 2D and 3D NACA airfoil test cases in serial and in parallel settings. In conclusion, the fully implicit Radau IIA Runge–Kutta methods compare favorably with equal-order DIRK methods in terms of accuracy, number of GMRES iterations, number of matrix–vector multiplications, and wall-clock time, for a wide range of time steps.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Correspondence: Phantom phonon localization in relaxors

Manley et al. report the observation of an unexpected, weak, phonon mode located in energy between that of the transverse acoustic (TA) and soft transverse optic (TO) modes in the relaxor ferroelectric Pb[(Mg 1/3 Nb 2/3 ) 1-x Ti x ]O 3 (PMN-xPT) with Ti content x = 0.30. Referred to as the local mode (LM), the energy of this extra mode varies little with wave vector in the cubic (paraelectric) phase, and this was interpreted as evidence of phonon localization. Any excitation that is localized in space must be extended in reciprocal space Q. To confirm this property, we performed a standard test on a PMN-xPT single crystal of nominally identical composition. This was done by comparing the rates at which the Bragg, TA phonon, and LM neutron scattering cross-sections decrease as the cubic crystallographic [100] axis is tilted out of the horizontal scattering plane by rotating about the orthogonal [010] axis. We find that the LM cross section varies with tilt in a manner identical to that of the Bragg peak. A local mode with short-range spatial correlations that decay exponentially will exhibit a broad Lorentzian peak as a function of tilt. Instead, we observe a narrow Gaussian peak. We conclude that the LM is not a real mode.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗