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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Signs of early-stage disk growth revealed with ALMA

We present ALMA 1.3 mm continuum, {sup 12}CO, C{sup 18}O, and SO data for the Class 0 protostars Lupus 3 MMS, IRAS 15398−3559, and IRAS 16253−2429 at resolutions of ∼100 au. By measuring a rotational profile in C{sup 18}O, a 100 au Keplerian disk around a 0.3 M{sub ⊙} protostar is observed in Lupus 3 MMS. No 100 au Keplerian disks are observed in IRAS 15398−3559 and IRAS 16253−2429. Nevertheless, embedded compact (<30 au) continuum components are detected. The C{sup 18}O emission in IRAS 15398−3559 shows signatures of infall with a constant angular momentum. IRAS 16253−2429 exhibits signatures of infall and rotation, but its rotational profile is unresolved. By fitting the C{sup 18}O data with our kinematic models, the protostellar masses and the disk radii are inferred to be 0.01 M{sub ⊙} and 20 au in IRAS 15398−3559, and 0.03 M{sub ⊙} and 6 au in IRAS 16253−2429. By comparing the specific angular momentum profiles from 10,000 au to 100 au in eight Class 0 and I protostars, we find that the evolution of envelope rotation can be described with conventional inside-out collapse models. In comparison with a sample of 18 protostars with known disk radii, our results reveal signs of disk growth, with the disk radius increasing as M{sub ∗}{sup 0.8±0.14} or t{sup 1.09±0.37} in the Class 0 stage, where M{sub *} is the protostellar mass and t is the age. The disk growth rate slows down in the Class I stage. In addition, we find a hint that the mass accretion rate declines as t{sup −0.26±0.04} from the Class 0 to the Class I stages.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The Mean Metal-line Absorption Spectrum of Damped Ly α Systems in BOSS

We study the mean absorption spectrum of the Damped Ly α (DLA) population at z ∼ 2.6 by stacking normalized, rest-frame-shifted spectra of ∼27,000 DLA systems from the DR12 of the Baryon Oscillation Spectroscopic Survey (BOSS)/SDSS-III. We measure the equivalent widths of 50 individual metal absorption lines in five intervals of DLA hydrogen column density, five intervals of DLA redshift, and overall mean equivalent widths for an additional 13 absorption features from groups of strongly blended lines. The mean equivalent width of low-ionization lines increases with N {sub H} {sub i}, whereas for high-ionization lines the increase is much weaker. The mean metal line equivalent widths decrease by a factor ∼1.1–1.5 from z ∼ 2.1 to z ∼ 3.5, with small or no differences between low- and high-ionization species. We develop a theoretical model, inspired by the presence of multiple absorption components observed in high-resolution spectra, to infer mean metal column densities from the equivalent widths of partially saturated metal lines. We apply this model to 14 low-ionization species and to Al iii, S iii, Si iii, C iv, Si iv, N v, and O vi. We use an approximate derivation for separating the equivalent width contributions of several lines to blended absorption features, and infer mean equivalent widths and column densities from lines of the additional species N i, Zn ii, C ii*, Fe iii, and S iv. Several of these mean column densities of metal lines in DLAs are obtained for the first time; their values generally agree with measurements of individual DLAs from high-resolution, high signal-to-noise ratio spectra when they are available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation

Here we present a new analysis methodology that allows for the self-consistent integration of multiple diagnostics including nuclear measurements, x-ray imaging, and x-ray power detectors to determine the primary stagnation parameters, such as temperature, pressure, stagnation volume, and mix fraction in magnetized liner inertial fusion (MagLIF) experiments. The analysis uses a simplified model of the stagnation plasma in conjunction with a Bayesian inference framework to determine the most probable configuration that describes the experimental observations while simultaneously revealing the principal uncertainties in the analysis. We validate the approach by using a range of tests including analytic and three-dimensional MHD models. An ensemble of MagLIF experiments is analyzed, and the generalized Lawson criterion χ is estimated for all experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

THE SWIFT GRB HOST GALAXY LEGACY SURVEY. II. REST-FRAME NEAR-IR LUMINOSITY DISTRIBUTION AND EVIDENCE FOR A NEAR-SOLAR METALLICITY THRESHOLD

We present rest-frame near-IR (NIR) luminosities and stellar masses for a large and uniformly selected population of gamma-ray burst (GRB) host galaxies using deep Spitzer Space Telescope imaging of 119 targets from the Swift GRB Host Galaxy Legacy Survey spanning 0.03 < z < 6.3, and we determine the effects of galaxy evolution and chemical enrichment on the mass distribution of the GRB host population across cosmic history. We find a rapid increase in the characteristic NIR host luminosity between z ∼ 0.5 and z ∼ 1.5, but little variation between z ∼ 1.5 and z ∼ 5. Dust-obscured GRBs dominate the massive host population but are only rarely seen associated with low-mass hosts, indicating that massive star-forming galaxies are universally and (to some extent) homogeneously dusty at high redshift while low-mass star-forming galaxies retain little dust in their interstellar medium. Comparing our luminosity distributions with field surveys and measurements of the high-z mass–metallicity relation, our results have good consistency with a model in which the GRB rate per unit star formation is constant in galaxies with gas-phase metallicity below approximately the solar value but heavily suppressed in more metal-rich environments. This model also naturally explains the previously reported “excess” in the GRB rate beyond z ≳ 2; metals stifle GRB production in most galaxies at z < 1.5 but have only minor impact at higher redshifts. The metallicity threshold we infer is much higher than predicted by single-star models and favors a binary progenitor. Our observations also constrain the fraction of cosmic star formation in low-mass galaxies undetectable to Spitzer to be small at z < 4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

VpROM: a novel variational autoencoder-boosted reduced order model for the treatment of parametric dependencies in nonlinear systems

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation of the linear nature of such operators is typically tackled via a library of local reduction subspaces, which requires the assembly of numerous local ROMs to address parametric dependencies. Our work attempts to define a more generalisable mapping between parametric inputs and reduced bases for the purpose of generative modeling. We propose the use of Variational Autoencoders (VAEs) in place of the typically utilised clustering or interpolation operations, for inferring the fundamental vectors, termed as modes, which approximate the manifold of the model response for any and each parametric input state. The derived ROM still relies on projection bases, built on the basis of full-order model simulations, thus retaining the imprinted physical connotation. However, it additionally exploits a matrix of coefficients that relates each local sample response and dynamics to the global phenomena across the parametric input domain. The VAE scheme is utilised for approximating these coefficients for any input state. This coupling leads to a high-precision low-order representation, which is particularly suited for problems where model dependencies or excitation traits cause the dynamic behavior to span multiple response regimes. Moreover, the probabilistic treatment of the VAE representation allows for uncertainty quantification on the reduction bases, which may then be propagated to the ROM response. The performance of the proposed approach is validated on an open-source simulation benchmark featuring hysteresis and multi-parametric dependencies, and on a large-scale wind turbine tower characterised by nonlinear material behavior and model uncertainty.

Conditional VAEs↗
DOE OSTI2020

Quantification of Ebola virus replication kinetics in vitro

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Exploring the physics of cesium lead halide perovskite quantum dots via Bayesian inference of the photoluminescence spectra in automated experiment

The unique optoelectronic properties of metal halide perovskite quantum dots (QDs) make them promising candidates for applications in light-emitting diodes (LEDs), scintillators, and other photonic devices. The automated micropipetting synthesis platform equipped with an optical reader enables the opportunity for high throughput synthesis and photoluminescent (PL) characterization of metal halide perovskite QDs for the first time. Here, we explore the compositional dependence of the PL behavior and stability of the combinatorial library of cesium lead halide (CsPbX 3 ) perovskites QDs via the automated platform. To study the stability of synthesized QDs in the binary and ternary configurations, we study the time-dependent PL properties using previously developed machine learning analysis. Additionally, to systematically explore the PL behavior in the ternary CsPbX 3 QDs system, we introduce the Bayesian inference framework that allows the probabilistic fit of multiple models to the PL data and establishes both optimal model and model parameter robustly. Furthermore, these behaviors can be used as a control parameter for the navigation of the multidimensional compositional spaces in automated synthesis. Furthermore, this analysis shows the nonuniformity of the PL peak behavior in the ternary CsPbX 3 QDs system. Further, the analysis confirms narrow size distribution and good quality of CsPbBr 3 QDs alloyed with low concentrations of iodide and chloride. We note that Bayesian Inference fit parameters can be further used as a control signal for navigation of the chemical spaces in automated synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2015

ENERGY DEPENDENCE OF SYNCHROTRON X-RAY RIMS IN TYCHO’S SUPERNOVA REMNANT

Several young supernova remnants (SNRs) exhibit thin X-ray bright rims of synchrotron radiation at their forward shocks. Thin rims require strong magnetic field amplification beyond simple shock compression if rim widths are only limited by electron energy losses. But, magnetic field damping behind the shock could produce similarly thin rims with less extreme field amplification. Variation of rim width with energy may thus discriminate between competing influences on rim widths. We measured rim widths around Tycho's SNR in five energy bands using an archival 750 ks Chandra observation. Rims narrow with increasing energy and are well described by either loss-limited or damped scenarios, so X-ray rim width-energy dependence does not uniquely specify a model. But, radio counterparts to thin rims are not loss-limited and better reflect magnetic field structure. Joint radio and X-ray modeling favors magnetic damping in Tycho's SNR with damping lengths ∼1%–5% of remnant radius and magnetic field strengths ∼50–400 μG assuming Bohm diffusion. X-ray rim widths are ∼1% of remnant radius, somewhat smaller than inferred damping lengths. Electron energy losses are important in all models of X-ray rims, suggesting that the distinction between loss-limited and damped models is blurred in soft X-rays. All loss-limited and damping models require magnetic fields ≳20 μG, affirming the necessity of magnetic field amplification beyond simple compression.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Continuous-Time Models for Longitudinal Data

This software is used to estimate a probabilistic continuous-time model from data. The models capture nonlinear associations between variables that change over time. They can be used to infer future values of variables. These tools were used in the manuscript entitled "Continuous-Time Probabilistic Models for Longitudinal Electronic Health Records."

Kaplan, AlanD↗
DOE OSTI2017

A Conceptual Model for the Response of Tropical Rainfall to Orbital Variations

Tropical rainfall to first order responds to variations in Earth’s orbit through shifts of the intertropical convergence zone (ITCZ) and changes in zonally averaged rainfall intensity. Here, a conceptual model is developed that represents both processes and their response to orbital insolation variations. The model predicts the seasonal evolution of tropical rainfall between 30°S and 30°N. Insolation variations impact seasonal rainfall in two different ways: thermodynamically, leading to variations in rainfall intensity through modulation of the water vapor content of the atmosphere; and dynamically, leading to shifts of the ITCZ through modulation of the global atmospheric energy budget. Thermodynamic and dynamic effects act together to shape the annual-mean response of tropical rainfall to changes in Earth’s orbit. The model successfully reproduces changes in annual-mean rainfall inferred from paleo-proxies across several glacial–interglacial cycles. It illuminates how orbital precession and variations of Earth’s obliquity affect tropical rainfall in distinct ways near the equator and farther away from it, with spectral signatures of precession and obliquity variations that shift with latitude. It also provides explanations for the observed different phasings of rainfall minima and maxima near the equator and away from it. For example, the model reproduces a phase shift of ~10 ka between rainfall records from caves in northern Borneo (4°N) and from China (approximately 30°N). Finally, the model suggests that such phase shifts arise through a different weighting of ITCZ shifts and variations in rainfall intensity, thus providing insight into the mechanisms that drive tropical rainfall changes on orbital time scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Approaching the self-dual point of the sinh-Gordon model

One of the most striking but mysterious properties of the sinh-Gordon model (ShG) is the b → 1/b self-duality of its S-matrix, of which there is no trace in its Lagrangian formulation. Here b is the coupling appearing in the model’s eponymous hyperbolic cosine present in its Lagrangian, cosh(bΦ). In this paper we develop truncated spectrum methods (TSMs) for studying the sinh-Gordon model at a finite volume as we vary the coupling constant. We obtain the expected results for b << 1 and intermediate values of b, but as the self-dual point b = 1 is approached, the basic application of the TSM to the ShG breaks down. We find that the TSM gives results with a strong cutoff Ec dependence, which disappears according only to a very slow power law in Ec. Standard renormalization group strategies — whether they be numerical or analytic — also fail to improve upon matters here. We thus explore three strategies to address the basic limitations of the TSM in the vicinity of b = 1. In the first, we focus on the small-volume spectrum. We attempt to understand how much of the physics of the ShG is encoded in the zero mode part of its Hamiltonian, in essence how ‘quantum mechanical’ vs ‘quantum field theoretic’ the problem is. In the second, we identify the divergencies present in perturbation theory and perform their resummation using a supra-Borel approximate. In the third approach, we use the exact form factors of the model to treat the ShG at one value of b as a perturbation of a ShG at a different coupling. In the light of this work, we argue that the strong coupling phase b > 1 of the Lagrangian formulation of model may be different from what is naïvely inferred from its S-matrix. In particular, we present an argument that the theory is massless for b > 1.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Time-resolved measurements of HO 2 radical in a heated plasma flow reactor

Time-resolved, absolute HO 2 number density in diluted H 2 –O 2 –Ar, CH 4 –O 2 -Ar, and C 2 H 4 –O 2 –Ar mixtures excited by a repetitive ns pulse discharge in a heated plasma flow reactor is measured by Cavity Ringdown Spectroscopy (CRDS). The experimental results are obtained at $\textit{T}$ = 300-600 K and $\textit{P}$ = 130 Torr, both during the discharge pulse burst and in the afterglow. In this work, the HO 2 number density is inferred from the CRDS data using a spectral model exhibiting good agreement with previous measurements of absolute HO 2 absorption cross sections. In the room-temperature H 2 –O 2 mixture, as well as in CH 4 –O 2 and C 2 H 4 –O 2 mixtures over the entire temperature range studied, HO2 is generated only during the discharge burst and decays in the afterglow. However, in the H 2 –O 2 mixture at elevated temperatures, $\textit{T}$ = 400-600 K, HO 2 persists in the afterglow up to 10 ms after the discharge burst, comparable with the flow residence time in the reactor. Comparison with kinetic modeling shows that the sustained reactivity after the source of radicals is turned off is due to a chain propagation / hydrogen oxidation process, which dominates the radical recombination reactions. The kinetic modeling predictions are in good agreement with the relative HO 2 number density measured in all three mixtures, although the model underpredicts the absolute number densities in H 2 –O 2 at $\textit{T}$ = 400-600 K by up to a factor of two. Detection of the sustained low-temperature reactivity in H 2 –O 2 , initiated by the radical generation in the plasma, suggests that the plasma excitation may also affect kinetics of oxidation and reforming of fuels exhibiting low-temperature chemistry below hot ignition point.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Measurement and simulation of the temperature evolution of a short pulse laser heated buried layer target

Short pulse laser heated buried layers experiments have been performed on the Orion laser facility to study the time-resolved emission characteristics of plasmas with mass densities ≥ 1 g/cc and electron temperatures > 500 eV. Our streak camera measurements focused on the K-shell emission lines of He-like and H-like aluminum from a buried aluminum layer. The data were analyzed by comparison to synthetic spectra generated with the non-local thermodynamic equilibrium (NLTE) radiation transfer code Cretin, which yielded maximum temperatures of nearly 800 eV at near solid density. The time precise history of the temperature evolution was reproduced with a 1-D radiation hydrodynamic code; however, the known effect of lateral transport of energy out of the focal spot made exacting agree with theory difficult. Furthermore, we have observed densities of ≥ 1 g/cc and temperatures of > 500 eV using the 1-D analysis, which supports the idea that the aluminum plasma is locally hotter than inferred from our spatially integrating measurements and that modeling requires the inclusion of 2-dimensional effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Earthquake detection in a simulated lunar regolith using distributed acoustic sensing

Current models of inner lunar geology have largely been inferred from the seismic experiments and observations performed during the Apollo missions that comprised a relatively small number of seismic instruments. Refining constraints on fundamental lunar relationships such as crust-mantle and mantle-core boundaries in the future will require seismic arrays spanning larger epicentral distances. A promising technology for installing dense seismic arrays with minimal human effort is distributed acoustic sensing (DAS), an approach that allows a single length of fiber optic cable to act as hundreds or thousands of sensors when coupled with a DAS interrogator. While terrestrial uses of DAS technology for seismic monitoring rely on burying the cable to maximize fidelity of seismic signal transmission to the fiber, digging meters of trench to bury optical fiber on lunar or planetary surfaces is logistically infeasible. To evaluate DAS signal attenuation due to surface deployment of cable in lunar regolith, we completed earthquake detection analyses that evaluated the sensitivity of an optic-fiber DAS system to seismic signals at different burial depths. We deployed a single-mode fiber in a 10-m open-bottom wooden box filled with a lunar regolith simulant (LRS) with fiber buried at different depths within the LRS and recorded signals for four regional and local earthquakes. The results were used to identify and evaluate signal attenuation in surface-deployed fiber compared to buried fiber in the LRS. Burial depth responses to active-source signals were also evaluated similar to previous studies characterizing DAS sensitivity of surface-deployed fiber. Atmospheric noise was minimal as the cable was deployed in an indoor environment; however, where observed, atmospheric and anthropogenic noise was filtered out using the same bandpass filtering used to identify earthquake events. We found that signal attenuation of the surface-deployed fiber compared to buried fiber was relatively high in active-source experiments but was not consistently observed in earthquake signals. That burial depth is not highly correlated to attenuation of the observed earthquake signals indicates that in a noise-limited environment, placing DAS-interrogated fiber directly at the regolith surface may be a promising deployment strategy to consider for sensing remote seismic signals during lunar exploration.

58 GEOSCIENCES↗
DOE OSTI2021

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A surprising proliferation of detwinning in β -tin at extreme loading rates

Integrating data from dynamic compression experiments of condensed matter across three national laboratories has led to insight and quantitative calibration of materials strength over decades of loading rate. For many materials, a single strength model (such as PTW) is sufficient to capture the flow-stress strain rate relationship which is monotonic. Here, we show here that β -tin, a tetragonal metal, exhibits dramatic deviations from this behavior. Naive fitting to a single PTW model is insufficient to capture the behavior; indeed, such resulting inferred flow stress versus strain exhibits a non-monotonic behavior. We suggest a resolution to this by proposing that in β -tin there are important Bauschinger effects arising from favorable conditions for twinning and detwinning. A simple yield surface model when paired with PTW hardening captures the experimental data.

36 MATERIALS SCIENCE↗