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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Search for charged-lepton-flavour violation in Z-boson decays with the ATLAS detector

Leptons with essentially the same properties apart from their mass are grouped into three families (or flavours). The number of leptons of each flavour is conserved in interactions, but this is not imposed by fundamental principles. Since the formulation of the standard model of particle physics, the observation of flavour oscillations among neutrinos has shown that lepton flavour is not conserved in neutrino weak interactions. So far, there has been no experimental evidence that this also occurs in interactions between charged leptons. Such an observation would be a sign of undiscovered particles or a yet unknown type of interaction. Here the ATLAS experiment at the Large Hadron Collider at CERN reports a constraint on lepton-flavour-violating effects in weak interactions, searching for Z -boson decays into a τ lepton and another lepton of different flavour with opposite electric charge. The branching fractions for these decays are measured to be less than 8.1 × 10 –6 ( eτ ) and 9.5 × 10 –6 ( μτ ) at the 95% confidence level using 139 fb –1 of proton–proton collision data at a centre-of-mass energy of $\sqrt{s}=13$ TeV and 20.3 fb –1 at $\sqrt{s}=8$ TeV. These results supersede the limits from the Large Electron–Positron Collider experiments conducted more than two decades ago.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Elucidating nanoscale mechanical properties of diabetic human adipose tissue using atomic force microscopy

Abstract Obesity-related type 2 diabetes (DM) is a major public health concern. Adipose tissue metabolic dysfunction, including fibrosis, plays a central role in DM pathogenesis. Obesity is associated with changes in adipose tissue extracellular matrix (ECM), but the impact of these changes on adipose tissue mechanics and their role in metabolic disease is poorly defined. This study utilized atomic force microscopy (AFM) to quantify difference in elasticity between human DM and non-diabetic (NDM) visceral adipose tissue. The mean elastic modulus of DM adipose tissue was twice that of NDM adipose tissue (11.50 kPa vs. 4.48 kPa) to a 95% confidence level, with significant variability in elasticity of DM compared to NDM adipose tissue. Histologic and chemical measures of fibrosis revealed increased hydroxyproline content in DM adipose tissue, but no difference in Sirius Red staining between DM and NDM tissues. These findings support the hypothesis that fibrosis, evidenced by increased elastic modulus, is enhanced in DM adipose tissue, and suggest that measures of tissue mechanics may better resolve disease-specific differences in adipose tissue fibrosis compared with histologic measures. These data demonstrate the power of AFM nanoindentation to probe tissue mechanics, and delineate the impact of metabolic disease on the mechanical properties of adipose tissue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Measurement of the top quark Yukawa coupling from $\mathrm{t\bar{t}}$ kinematic distributions in the dilepton final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV

A measurement of the Higgs boson Yukawa coupling to the top quark is presented using proton-proton collision data at s=13 TeV, corresponding to an integrated luminosity of 137 fb-1, recorded with the CMS detector. The coupling strength with respect to the standard model value, Yt, is determined from kinematic distributions in tt¯ final states containing ee, μμ, or eμ pairs. Variations of the Yukawa coupling strength lead to modified distributions for tt¯ production. In particular, the distributions of the mass of the tt¯ system and the rapidity difference of the top quark and antiquark are sensitive to the value of Yt. The measurement yields a best fit value of Yt=1.16-0.35+0.24, bounding Yt<1.54 at a 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Opacity on NIF: Anchor 2 Iron (Level 2 Milestone 7118 Report)

In FY2020 we fulfilled the exit criteria of milestone 7118. We fielded iron opacity experiments at higher densities and temperatures approaching anchor 2 conditions and made good progress on overcoming the problems that we found. This report describes in detail the progress that has been made. We presented the results in a LANL seminar on August 10 and we gave two talks on our results to the National Opacity Workshop Series, one on July 9 and the other on September 14. Before discussing the anchor 2 measurements, we present an update of the NIF anchor 1 data. We did a new analysis of the anchor 1 data and compared the results to a series of calculations using the LANL ATOMIC code. Figures 1 and 2 compare the data with the best-fit to calculations. This result is significantly improved over the result published in Atoms. The differences are small for the iron quasi-continuum between 8-9.5 Å and for the Mg Heα and Lyα lines. The opacities near the centers of the iron bound-bound features also match fairly well in the 9.5-12.5 Å range. However, the opacity windows between the large iron bound-bound features are deeper in the Atomic predictions than the measurement. Furthermore, large discrepancies exist for the Mg He β, γ, and δ lines and for the short-wavelength quasicontinuum. The experiment and analysis refinements described below have set the stage for unraveling these discrepancies. Specifically, we can repeat the Anchor 1 experiments using improved backlighter, hohlraum, and spectrometers to obtain reduced backgrounds and better spectral resolution. Exploiting the AlMg calibration experiments and repeating the Anchor 1 measurements multiple times will enable formal and rigorous uncertainty determination. This well-defined path gives us confidence that high quality opacity data can be obtained on NIF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Photon Doppler Velocimetry to Spatially Resolve Plasma Density in a Power Flow Gap

The understanding of power flow plasmas is important as we look towards next generation pulsed power (NGPP) as current losses could prohibit the goals of that facility. Therefore, it is important to have accurate diagnostics of the plasma parameters on the current machines, which can be used to help inform and improve simulations. Having these plasma parameters will help validate models and simulations to provide confidence when they are expanded to conditions relevant to NGPP. One important plasma parameter that can be measured is the electron density, which can be measured by photonic Doppler velocimetry (PDV). A PDV system has several key advantages over other interferometers by measuring relatively low densities (> 1 × 10 15 cm -2 ) with both spatial and temporal resolution. Experiments were performed on the Mykonos pulsed power machine, which is a 1 MA sub scale machine in which recent platforms have been developed to explore current densities relevant to the inner magnetically insulated transmission line (MITL) on the Z machine. Experiments were performed on two different platforms, the thin foil platform and the Mykonos parallel plate platform (MP3). In addition, a combination of both single-point and multi-point measurements were used. The single-point measurements proved to be very promising, providing a clear increase in density at about 70 ns into the current rise on thin foil experiments up to about 5 × 10 17 cm -3 before the probe stopped providing signal. While we did also see returns from multi-point measurements on both platforms, the signals were not as easy to interpret due to strong background effects. However, they do show initial promise for this diagnostic to measure density at several points across a 1 mm gap. These measurements provide insights in how to improve the diagnostic so that it can provide useful information on power flow relevant experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

We report the observation of a compact binary coalescence involving a 22.2-24.3 M ⊙ black hole and a compact object with a mass of 2.50-2.67 M ⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal, GW190814, was observed during LIGO's and Virgo's third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network. The source was localized to 18.5 deg 2 at a distance of 241 +41-41 Mpc; no electromagnetic counterpart has been confirmed to date. The source has the most unequal mass ratio yet measured with gravitational waves , 0.112+0.0090.008, , and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system. The dimensionless spin of the primary black hole is tightly constrained to ≤0.07. Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is confirmed at high confidence. We estimate a merger rate density of 1-23 Gpc -3 yr -1 for the new class of binary coalescence sources that GW190814 represents. Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters. However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

First Constraints on Growth Rate from Redshift-space Ellipticity Correlations of SDSS Galaxies at 0.16 < z < 0.70

We report the first constraints on the growth rate of the universe, f(z)σ 8 (z), with intrinsic alignments (IAs) of galaxies. We measure the galaxy density-intrinsic ellipticity cross-correlation and intrinsic ellipticity autocorrelation functions over 0.16 < z < 0.7 from luminous red galaxies (LRGs) and LOWZ and CMASS galaxy samples in the Sloan Digital Sky Survey (SDSS) and SDSS-III BOSS survey. We detect clear anisotropic signals of IA due to redshift-space distortions. By combining measured IA statistics with the conventional galaxy clustering statistics, we obtain tighter constraints on the growth rate. The improvement is particularly prominent for the LRG, which is the brightest galaxy sample and known to be strongly aligned with underlying dark matter distribution; using the measurements on scales above 10 h -1 Mpc, we obtain $f{\sigma }_{8}={0.5196}_{-0.0354}^{+0.0352}$ (68% confidence level) from the clustering-only analysis and $f{\sigma }_{8}={0.5322}_{-0.0291}^{+0.0293}$ with clustering and IA, meaning 19% improvement. The constraint is in good agreement with the prediction of general relativity, f σ 8 = 0.4937 at z = 0.34. For LOWZ and CMASS samples, the improvement of constraints on f σ 8 is found to be 10% and 3.5%, respectively. Our results indicate that the contribution from IA statistics for cosmological constraints can be further enhanced by carefully selecting galaxies for a shape sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Estimation of sensor measurement errors in reactor coolant systems using multi-sensor fusion

A nuclear power plant is typically instrumented with a variety of sensors to continually monitor its variables, and their sensor’s measurements may be used to assess the plant state and initiate safety actions, if needed. Errors in sensor measurements, due to factors such as calibration drifts, critically affect such state assessments. Here, we address a problem of estimating sensor errors using physics-informed machine learning methods that use measurements collected under known plant conditions. For a given sensor, we propose an information fusion method that uses measurements from other sensors to estimate its output assuming it is error-free and provides its difference from an actual measurement as an error estimate. We present the ensemble of trees and support vector machine fusers, and evaluate their performance using measurements collected over an emulated test loop of a pressurized water reactor. The plant variables are related to each other through the underlying physical laws under inertial constraints that place bounds on their derivatives, which analytically justify the applicability of machine learning methods for computing these fusers. Under twenty scenarios, we assess their sensor error estimates for pressure sensors of the heat exchanger of a reactor’s primary coolant system. Multiple types of errors are captured by both fusers under externally induced calibration drifts, blockages, minor leaks and air gaps in sensing lines, and electromagnetic interference; the root mean square error of the estimation of error is under 2.2% percent of the maximum measurement. We present generalization equations, in the framework of statistical learning theory, for these methods that characterize the confidence probability that the estimation error is bounded by a specified parameter in future test scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Search for the chiral magnetic effect in Au+Au collisions at $\sqrt{s_{NN}}$ = 27 GeV with the STAR forward event plane detectors

A decisive experimental test of the Chiral Magnetic Effect (CME) is considered one of the major scientific goals at the Relativistic Heavy-Ion Collider (RHIC) towards understanding the nontrivial topological fluctuations of the Quantum Chromodynamics vacuum. In heavy-ion collisions, the CME is expected to result in a charge separation phenomenon across the reaction plane, whose strength could be strongly energy dependent. The previous CME searches have been focused on top RHIC energy collisions. In this Letter, we present a low energy search for the CME in Au+Au collisions at $\sqrt{s_{NN}}$ = 27 GeV. We measure elliptic flow scaled charge-dependent correlators relative to the event planes that are defined at both mid-rapidity |η| < 1.0 and at forward rapidity 2.1 < |η| < 5.1. We compare the results based on the directed flow plane ( $Ψ$ 1 ) at forward rapidity and the elliptic flow plane ( $Ψ$ 2 ) at both central and forward rapidity. The CME scenario is expected to result in a larger correlation relative to $Ψ$ 1 than to $Ψ$ 2 , while a flow driven background scenario would lead to a consistent result for both event planes. In 10-50% centrality, results using three different event planes are found to be consistent within experimental uncertainties, suggesting a flow driven background scenario dominating the measurement. We obtain an upper limit on the deviation from a flow driven background scenario at the 95% confidence level. This work opens up a possible road map towards future CME search with the high statistics data from the RHIC Beam Energy Scan Phase-II.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Preliminary Results on Bayesian Inverse UQ for OECD/NEA WPNCS Subgroup 14 Benchmark Exercise for Error Recovery and Experimental Coverage

The Organization for Economic Cooperation and Development (OECD) Working Party on Nucelar Criticality Safety (WPNCS) has proposed a benchmark exercise representative of neutronic behavior in criticality experiments. Here, the goal is to develop confidence in data assimilation techniques used to adjust nuclear data. Participants are given synthetic experimental models with associated measured data and asked to estimate the model parameters given the model and measurements as well as provide predictions for separate application models. In this work, we performed data assimilation using Bayesian inverse Uncertainty Quantification (UQ) with machine learning surrogate models to produce posterior parameter distributions for the requested parameters and posterior predictive distributions for the requested responses. Several experimental models are shown to insufficiently inform the posterior parameter distributions for the applications involved. However, given sufficient experimental data, posterior parameter estimates yielded reduced uncertainty in the response predictions of interest while covering the experimental data.

Bayesian Inference↗
DOE OSTI2024

First search for an anomalous excess of charged-current $\nu_e$ interactions without visible pions using the full MicroBooNE dataset

This public note presents an investigation of low-energy electron-neutrino events in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment. This search is motivated by the excess of low energy electromagnetic events observed by the MiniBooNE experiment and, more broadly, by the landscape of neutrino anomalies observed at short baselines. This is the first measurement to use all of the data collected by the MicroBooNE experiment, corresponding to $1.1\times 10^{21}$ protons on target. Two exclusive samples of electron neutrinos without visible pions are used, one with visible protons and one without any visible protons. MicroBooNE data is compared to two empirical models of the MiniBooNE low energy excess, one obtained by enhancing the electron-neutrino content as a function of the neutrino energy, and one representing the excess for the first time as a function of the kinematics of shower energy and angle. This measurement excludes an electron-like interpretation of the MiniBooNE excess based on these models at $\geq 99\%$ confidence level in all kinematic variables.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Northwest Combined Heat and Power Technical Assistance Partnership: Final Scientific/Technical Report

During the years of 2018 through 2023, The Washington State University Energy Program (WSU EP) operated the Northwest Combined Heat and Power Technical Assistance Partnership under contract to the US Department of Energy’s Advanced Manufacturing Office (AMO), a part of DOE’s larger Office of Energy Efficiency and Renewable Energy. This contract provided direction and funding for providing technical outreach, information and technical/economic analysis services in support of expanding development of Combined Heat and Power (CHP) over a four state region in the Pacific Northwest. The states served included Alaska, Idaho, Oregon and Washington. During the duration of this contract, the WSU EP delivered performed specific assigned tasks and provided services of various generally prescribed types, with the overall goal of increasing adoption of CHP as an efficient heat and power supply in the region. Indications – though not formally evaluated to our knowledge – are that this goal was achieved. Over many years, the WSU team has provided support for CHP developments in these states under similar previous Department of Energy contracts, and many such projects have been built. While the early-phase outreach, education and techno-economic assessments that the NW CHP TAP provides rarely immediately result in project construction, there is no doubt that the WSU EP team has had substantial influence in the construction of a number of important CHP projects in the region. Examples of projects implemented directly under the current team’s guidance and services during this contract cycle include an 875 kilowatt biomass fueled CHP system installed at the University of Idaho, and 5 megawatt natural gas fueled CHP system at the University of Montana ( UM Breaks Ground on New Power Facility, Slashes Emissions 30% (umt.edu) ). Many other CHP systems recently installed are also either directly or at least partially the result of information and technical services provided by the team. Finally, though much more difficult to quantify and less glamorous, the team has helped countless organizations to understand CHP and determine whether and how CHP may be a fit for them, before they consider paying for engineering services. Whether the ultimate outcome has been to include CHP or not, the expert skills provided by the NW CHP TAP team have provided real value to these organizations considering this relatively complex technology. Over the duration of the contract our team provided 71 Initial Technical Assessments and 17 Advanced Technical Assessments; 53 End-User Engagements and 33 Stakeholder Engagements of various types; 42 Technical Profiles (Project Profiles, Program Profiles and Policy Profiles), and produced 14 technical articles for the Department of Energy. Unfortunately, these quantities provide no measure of the value that the team has provided. Only our clients can judge that, and we are confident that they are pleased with the benefits we have offered them.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Almost medium-free measurement of the Hoyle state direct-decay component with a TPC

The structure of the Hoyle state, a highly α -clustered state at 7.65 MeV in 12 C, has long been the subject of debate. Understanding if the system comprises of three weakly interacting α particles in the 0s orbital, known as an α-condensate state, is possible by studying the decay branches of the Hoyle state. The direct decay of the Hoyle state into three α particles, rather than through the 8 Be ground state, can be identified by studying the energy partition of the three α particles arising from the decay. This paper provides details on the breakup mechanism of the Hoyle stating using a new experimental technique. Method: By using β-delayed charged-particle spectroscopy of 12 N using the Texas active target time-projection chamber, a high-sensitivity measurement of the direct 3α decay ratio can be performed without contributions from pileup events. A Bayesian approach to understanding the contribution of the direct components via a likelihood function shows that the direct component is <0.043% at the 95% confidence level. This value is in agreement with several other studies, and, here, we can demonstrate that a small nonsequential component with a decay fraction of about 10 –4 is most likely. Here, the measurement of the non-sequential component of the Hoyle state decay is performed in an almost medium-free reaction for the first time. The derived upper limit is in agreement with previous studies and demonstrates sensitivity to the absolute branching ratio. Further experimental studies would need to be combined with robust microscopic theoretical understanding of the decay dynamics to provide additional insight into the idea of the Hoyle state as an α condensate.

6 ≤ A ≤ 19↗
DOE OSTI2025

Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics

Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross section, tandem mass spectra) against reference data obtained from measurements of authentic chemical standards. The breadth of annotatable compounds using this approach is necessarily limited by availability of authentic standards, analytical throughput, and resolving power of the separations that underly the measurements. The maturation of computational methods, both theory-driven and artificial intelligence/machine learning-based, for prediction of various molecular properties relevant to multidimensional mass spectrometry measurements has opened the door to a new “reference-free” paradigm of compound annotation. Through augmenting existing reference data for molecular properties with computational predictions, the universe of identifiable chemical species can be expanded significantly beyond its current limits. An unexplored aspect of this novel approach is understanding how to gauge confidence in resulting annotations, especially as the compound search space is expanded. Intuitively, the confidence of a compound annotation is related to the inherent discriminatory power of the molecular properties used for identification, as well as the precision with which the properties are measured or predicted. In this work, we characterize this relationship between measurement precision and identification probability in a systematic and quantitative fashion for a defined region of chemical space that includes organic small molecule metabolites. Importantly, this work establishes a framework for conducting metabolite identification probability analysis that enables others to quantify this relationship for their own compounds and properties of interest.

Metabolite Identification↗
DOE OSTI2022

Measurement of the Higgs boson width and evidence of its off-shell contributions to ZZ production

Since the discovery of the Higgs boson in 2012, detailed studies of its properties have been ongoing. Besides its mass, its width - related to its lifetime - is an important parameter. One way to determine this quantity is by measuring its off-shell production, where the Higgs boson mass is far away from its nominal value, and relating it to its on-shell production, where the mass is close to the nominal value. Here, we report evidence for such off-shell contributions to the production cross section of two Z bosons with data from the CMS experiment at the CERN Large Hadron Collider. We constrain the total rate of the off-shell Higgs boson contribution beyond the Z boson pair production threshold, relative to its standard model expectation, to the interval [0.0061, 2.0] at 95% confidence level. The scenario with no off-shell contribution is excluded at a p-value of 0.0003 (3.6 standard deviations). We measure the width of the Higgs boson as $\Gamma_{\mathrm{H}}$ = 3.2 $_{-1.7}^{+2.4}$ MeV, in agreement with the standard model expectation of 4.1 MeV. In addition, we set constraints on anomalous Higgs boson couplings to W and Z boson pairs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Inter-well connectivity detection in CO 2 WAG projects using statistical recurrent unit models

Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO 2 EOR projects utilizing the water-alternating-gas (WAG) process. SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure. The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. This study results in a reliable and efficient data analytics framework that is well-suited for large field applications.

42 ENGINEERING↗
DOE OSTI2022

Modeling Validation of Mechanical and Thermal Borehole Breakouts From Polyaxial Laboratory Tests to Determine Maximum Horizontal in Situ Stress

To accurately assess rock behavior in the deep subsurface, it is necessary to measure the in situ stress directions and magnitudes. The current methods for measuring the in situ stress state in the deep subsurface primarily include hydraulic fracturing tests (i.e., the minimum horizontal stress) or occasionally observing existing compressive borehole breakouts (i.e., the maximum horizontal stress) that have occurred naturally from drilling. If there are no existing compressive breakouts in a borehole, the maximum horizontal in situ stress cannot be estimated with much confidence. In response to this data gap, a new thermal breakout technology is being developed that will provide a method for thermally inducing borehole breakouts and obtaining consistent measurements of the maximum horizontal stress magnitude. This thermal breakout technology involves heating the borehole and increasing the thermoelastic compressive stress in the rock until a breakout develops, which is directly correlated to the maximum horizontal stress magnitude. In support of developing the thermal breakout technology, polyaxial laboratory tests have been performed on small-scale boreholes within rock blocks where mechanically- and thermally-induced borehole breakouts have been created. Numerical models along with the principal of superposition were created and used to analyze the polyaxial laboratory tests to predict the maximum horizontal stress, given the same data that would be obtained in an actual subsurface borehole thermal breakout test. Multiple failure criteria were used to evaluate the best prediction of the breakout onset and maximum horizontal stress. The maximum horizontal stress predictions were compared to the actual maximum horizontal stress applied at breakout using acoustic events recorded from emission sensors in the polyaxial tests. The results showed consistent results that could be used to refine the modeling approach and failure criterion that are used to make the maximum horizontal stress predictions. This study provided insight and validation for the thermal breakout stress measurement concept.

Jones, Matt↗
DOE OSTI2024

Facile Tensile Testing Platform for In Situ Transmission Electron Microscopy of Nanomaterials

In situ tensile testing using transmission electron microscopy (TEM) is a powerful technique to probe structure-property relationships of materials at the atomic scale. In this work, a facile tensile testing platform for in situ characterization of materials inside a transmission electron microscope is demonstrated. The platform consists of: 1) a commercially available, flexible, electron-transparent substrate (e.g., TEM grid) integrated with a conventional tensile testing holder, and 2) a finite element simulation providing quantification of specimen-applied strain. The flexible substrate (carbon support film of the TEM grid) mitigates strain concentrations usually found in free-standing films and enables in situ straining experiments to be performed on materials that cannot undergo localized thinning or focused ion beam lift-out. The finite element simulation enables direct correlation of holder displacement with sample strain, providing upper and lower bounds of expected strain across the substrate. The tensile testing platform is validated for three disparate material systems: sputtered gold-palladium, few-layer transferred tungsten disulfide, and electrodeposited lithium, by measuring lattice strain from experimentally recorded electron diffraction data. The results show good agreement between experiment and simulation, providing confidence in the ability to transfer strain from holder to sample and relate TEM crystal structural observations with material mechanical properties.

2D materials↗