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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Detection of Apparent Early Rabies Infection by LN34 Pan-Lyssavirus Real-Time RT-PCR Assay in Pennsylvania

The Pennsylvania Department of Health Bureau of Laboratories (PABOL) tested 6855 animal samples for rabies using both the direct fluorescent antibody test (DFA) and LN34 pan-lyssavirus reverse transcriptase quantitative PCR (RT-qPCR) during 2017–2019. Only two samples (0.03%) were initially DFA negative but positive by LN34 RT-qPCR. Both cases were confirmed positive upon re-testing at PABOL and confirmatory testing at the Centers for Disease Control and Prevention by LN34 RT-qPCR and DFA. Rabies virus sequences from one sample were distinct from all positive samples processed at PABOL within two weeks, ruling out cross-contamination. Levels of rabies virus antigen and RNA were low in all brain structures tested, but were higher in brain stem and rostral spinal cord than in cerebellum, hippocampus or cortex. Taken together, the low level of rabies virus combined with higher abundance in more caudal brain structures suggest early infection. These cases highlight the increased sensitivity and ease of interpretation of LN34 RT-qPCR for low positive cases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗

DOE OSTI2021

Analysis of liquid petroleum using a laser-induced breakdown spectroscopy instrument

Here, a prototype analyzer for the direct LIBS analysis of nebulized liquid samples was developed and tested, particularly for the analysis of petroleum, organic solvents and aqueous solutions. The LIBS analyzer requires about 750 µl of liquid sample, 1 liter of N 2 gas, and 10 s of the analysis time to record 100 spectra. The limits of detection in oil and solvent are as low as 0.01–0.04 ppm for Li, Mg, and Cu. They increase for the difficult elements, such as Pb and Hg (7-10 ppm), Cl (250 ppm), and S (~0.7%). The relative standard deviation of measuring 100 ppm vanadium in oil and solvent was 1.5%. The LIBS detection limits and repeatability are better than required by the standard method ASTM D5185 for the analysis of lubricating oils in ICP-OES. Several petroleum samples were analyzed by LIBS and the quantitative results for V, Ni, and Fe compared to the ICP-OES data. Light crude oils can be nebulized and analyzed directly. Medium crude oils require minimal dilution at least 1:1, otherwise errors of determination become large. Presumably, utilization of the internal standard and chemometrics can be useful to correct for the matrix effects. In addition to the trace element analysis, the LIBS prototype demonstrated ability to measure the hydrogen-to-carbon ratio in organic liquid samples. Several unidentified features were observed in the carbon spectrum. Their possible origin is discussed.

02 PETROLEUM↗

DOE OSTI2025

Chemical imaging of individual stratospheric particles sampled over North America

The increasing size, severity, and frequency of wildfires have led to dramatic increases in particulate matter concentrations in the troposphere. Severe wildfires can generate intense convective systems capable of transporting large quantities of biomass burning organic aerosols (BBOA) to the upper troposphere and lower stratosphere (UTLS). Chemically complex organic matter and light-absorbing carbonaceous material is introduced into stratospheric regions that were historically isolated from direct surface emissions. In this study, stratospheric particles were sampled over North America during the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) campaign, an aircraft-based research project designed to characterize convective perturbation in the UTLS. Particle samples collected from six research flights during summer 2022 were analyzed using Computer-Controlled Scanning Electron Microscopy and Scanning Transmission X-ray Microscopy to investigate particle size distributions, morphology, chemical composition, and mixing state of stratospheric particles along transects across the continental United States and adjacent Pacific Ocean airspace. Analysis revealed that all sampled particles contained detectable levels of carbon, with most exhibiting organic volume fractions of 0.37 ± 0.20. Notably, about 5% of the particles also contained soot inclusions, which indicates the presence of refractory black carbon transported to stratospheric altitudes and provides direct evidence of wildfire-derived black carbon reaching the UTLS. Typical particle morphology exhibits organic shells over soot and inorganic cores and suggests secondary processing and aging of BBOA during transport to and within the UTLS. These findings provide compelling evidence that wildfire emissions play a critical role in affecting the long-term composition and radiative properties of stratospheric particles.

Sharpe, Steven [Purdue Univ., West Lafayette, IN (↗

DOE OSTI2026

Probing and Tuning Strain‐Localized Exciton Emission in 2D Material Bubbles at Room Temperature

In monolayer transition metal dichalcogenides bubbles-nanoscale deformations typically exhibiting a dome-like shape-Excitons are confined by the strain effect, which exhibits extraordinary emission properties, such as single photon generation, enhanced light emission, and spectrally tunable excitonic states. While the strain profiles of these bubbles are extensively studied, this work provides an approach 1) to directly visualize the associated exciton properties in bubbles formed in WSe2 monolayer, revealing an intrinsic emission wavelength shift of ≈40 nm, and 2) actively modify local strain, enabling further exciton emission tuning over a range of 50 nm. These are achieved by emission mapping and nanoindentation using a dielectric near-field probe, which enables the detection of local emission spectra and emission lifetimes within individual bubbles. Statistical analysis of 67 bubbles uncovers an emission wavelength distribution centered around 780 nm. Furthermore, saturation behavior in the power-dependent studies and the associated lifetime change reveal the localized nature of the strain-induced states. These findings provide direct insights into the strain-localized emission dynamics in bubbles and establish a robust framework for non-destructive, reversible, and predictable nanoscale emission control, presenting a potential avenue for developing next-generation tunable quantum optical sources.

2D materials bubbles↗

DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Flow Reactor Study of the Soot Precursors of Novel Cycloalkanes as Synthetic Jet Fuel Compounds: Octahydroindene, p -Menthane, and 1,4-Dimethylcyclooctane

Sustainable aviation fuels (SAFs) or Synthetic aviation turbine fuels (SATFs) derived from nonpetroleum sources are essential for energy security and a strong rural and agricultural economy. Airplanes operating on SAF can have lower particle emissions compared to those of conventional jet fuel, reducing air quality impacts near airports. Processing biobased isoprene or wood and agricultural waste can produce cycloalkane-rich fuels with properties meeting ASTM International’s SATF requirements. The unique structures of these cycloalkanes yield lower soot emissions because of their lack of aromatic rings. We measured the soot formation tendency as yield sooting index (YSI) and used laminar flow reactor experiments to evaluate soot precursors formed for isoprene-derived compounds p-menthane and 1,4-dimethylcyclooctane (DMCO), and octahydroindene (OHI)─ produced from woody biomass via catalytic fast pyrolysis. The combustion chemistry of the OHI and DMCO has not been previously studied. Experiments were conducted at 10 bar from 800 to 1200 K, equivalence ratios of 1.0 and 3.0, and residence times of 1.0 and 0.6 s, respectively. Experimentally detected species were used to elucidate the mechanisms of soot precursor formation. OHI exhibited the highest YSI (94.5) and formed a high concentration of benzene primarily by direct dehydrogenation of the six-membered ring. p-Menthane (YSI 92.0) and DMCO (YSI 85.0) oxidation products included fewer aromatic components but higher benzene precursors, including 1,3-butadiene, propyne, and allene. This suggests that the ring-opening pathway is dominant over the dehydrogenation pathway in the benzene formation for these compounds. This experimental speciation provides insight into the influence of the cycloalkane structure on the sooting tendencies of potential SAF blend components, thereby aiding in fuel design processes.

09 BIOMASS FUELS↗

DOE OSTI2022

J-PLUS: Uncovering a large population of extreme [OIII] emitters in the local Universe

Over the past decades, several studies have discovered a population of galaxies that undergo very strong star formation events. They are called extreme emission line galaxies (EELGs). We exploit the capabilities of the Javalambre Photometric Local Universe Survey (J-PLUS), a wide-field multifilter survey, with which 2000 square degrees of the northern sky are already observed. We use it to identify EELGs at low redshift by their [OIII]5007 emission line. We intend to provide a more complete, deep, and less biased sample of local EELGs. We selected objects with an excess of flux in the J-PLUS medium-band J0515 filter, which covers the [OIII] line at z < 0.06. We removed contaminants (stars and higher-redshift systems) using J-PLUS and WISE infrared photometry, with SDSS spectra as a benchmark. We performed spectral energy distribution fitting to estimate the physical properties of the galaxies: line fluxes, equivalent widths (EWs), masses, stellar population ages, and so on. We identify 466 EELGs at z < 0.06 with [OIII] EW over 300 Å and an r-band magnitude below 20, of which 411 were previously unknown. Most show compact morphologies, low stellar masses (log(M • /M ⊙ ) ~ 8.13 –0.58 +0.61 ), low dust extinction (E( B–V ) ~ 0.1 –0.1 +0.2 ), and very young bursts of star formation (3.0 –2.0 +2.7 Myr). Our method is up to ~20 times more efficient in detecting EELGs per Mpc 3 than broadband surveys, and it is as complete as magnitude-limited spectroscopic surveys (but reaches fainter objects). The sample is not directly biased against strong Hα emitters, in contrast with works using broadband surveys. We demonstrate that J-PLUS can identify a large sample of previously unknown EELGs showing unique properties following a clear selection process. A fraction of the EELGs are probably similar to the first galaxies in the Universe, but they are at a much lower redshift, which makes them ideal targets for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Evidence of vapor shielding effect on heat flux loaded on flowing liquid lithium limiter in EAST

For this study, a lithium (Li) vapour layer was formed around a flowing liquid Li limiter to shield against the plasma incident power and reduce limiter heat flux in the EAST tokamak. The results revealed that after a plasma operation of a few seconds, the layer became clear, which indicated a strong Li emission with a decrease in the limiter surface temperature. This emission resulted in a dense vapour around the limiter, and Li ions moved along the magnetic field to form a green shielding layer on the limiter. The plasma heat flux loaded on the limiter, measured by the probe installed on the limiter, was approximately 52% lower than that detected by a fast-reciprocating probe at the same radial position without the limiter in EAST. Additionally, approximately 42% of the parallel heat flux was dissipated directly with the enhanced Li radiation in the discharge with the liquid metal infused trenches (LIMIT) limiter. This observation revealed that the Li vapour layer exhibited an excellent shielding effect to liquid Li on plasma heat flux, which is a possible benefit of liquid-plasma-facing components in future fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Outlook for artificial intelligence and machine learning at the NSLS-II

Abstract We describe the current and future plans for using artificial intelligence and machine learning (AI/ML) methods at the National Synchrotron Light Source II (NSLS-II), a scientific user facility at the Brookhaven National Laboratory. We discuss the opportunity for using the AI/ML tools and techniques developed in the data and computational science areas to greatly improve the scientific output of large scale experimental user facilities. We describe our current and future plans in areas including from detecting and recovering from faults, optimizing the source and instrument configurations, streamlining the pipeline from measurement to insight, through data acquisition, processing, analysis. The overall strategy and direction of the NSLS-II facility in relation to AI/ML is presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A massive open cluster hiding in full sight

ABSTRACT Obscuration and confusion conspire to limit our knowledge of the inner Milky Way. Even at moderate distances, the identification of stellar systems becomes compounded by the extremely high density of background sources. Here, we provide a very revealing example of these complications by unveiling a large, massive, young cluster in the Sagittarius arm that has escaped detection until now despite containing more than 30 stars brighter than G = 13. By combining Gaia DR2 astrometry, Gaia and 2MASS photometry, and optical spectroscopy, we find that the new cluster, which we name Valparaiso 1, located at $\sim 2.3\,$ kpc, is about 75 Ma old and includes a large complement of evolved stars, among which we highlight the 4 d classical Cepheid CM Sct and an M-type giant that probably represents the first detection of an asymptotic giant branch star in a Galactic young open cluster. Although strong differential reddening renders accurate parameter determination unfeasible with the current data set, direct comparison to clusters of similar age suggests that Valparaiso 1 was born as one of the most massive clusters in the solar neighbourhood, with an initial mass close to $10^{4}\, \mathrm{M}_{\odot }$.

Negueruela, I.↗

DOE OSTI2020

Virtual Pressure Sensor for Electronic Expansion Valve Control in a Vapor Compression Refrigeration System

Virtual sensor technology, which uses simulation models/algorithms to calculate a value to represent an unmeasured variable or replace a directly measured reading, has attracted many studies in the heating, ventilation, air conditioning and refrigeration (HVAC&R) industry. However, most virtual sensor technologies are developed for fault detection and diagnostics (FDD) purposes, which generally compare the virtual sensor values with actual measured values to detect if any fault occurred and identify the causes that led to the fault. It is rare to see studies focus on control performance of virtual sensors after substituting an actual sensor. This is particularly important for the system with no redundant sensor since a virtual sensor is the most effective way to operate the system in the desirable region when any sensor failure occurs. To address this gap, this paper develops a new virtual pressure sensor technology to substitute the actual pressure measurement for electronic expansion valve (EXV) control in a vapor compression refrigeration system by integrating compressor and valve characteristics. The control performance of this proposed virtual pressure sensor technology under various operating conditions is validated with experimental data. Closed loop EXV control simulations with the proposed virtual pressure sensor are conducted, and the results are analyzed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

A Comprehensive Analysis of NGC 2158 in the Gaia Era: Photometric Parameters, Apex, and Orbit

We present an investigation of NGC 2158 using Gaia DR2 data. We identified 800 most likely cluster members with membership probability higher than 90%. The mean proper motions of this object are determined as (μ {sub x} = −0.203 ± 0.003, μ {sub y} = −1.99 ± 0.004) mas yr{sup −1}. The limiting radius, log(age), and distance of the cluster are obtained as 23.5 arcmin, 9.38 ± 0.04 Gyr, and 4.69 ± 0.22 kpc, respectively. The overall mass function slope (0.93 ± 0.14) is flatter than the Salpeter value (1.35) within the mass range of 1.17–1.44 M {sub ⊙}. This cluster also shows the mass-segregation effect and our study demonstrates that NGC 2158 is a dynamically relaxed open cluster. Using the apex diagram (AD), the apex coordinates of the cluster are obtained in different ways and are examined using the (μ {sub U},μ {sub T}) diagram. The best value of the apex coordinates is determined as A = 8724 ± 160, D = −3661 ± 530. We also determined the orbit of the cluster and found that NGC 2158 moves almost in the solar antapex direction. The resulting spatial velocity of NGC 2158 is 51 km s{sup −1}. A significant oscillation along the Z-coordinate up to 529 pc is detected. Various scenarios regarding the origin of this cluster are also discussed.

42 ENGINEERING↗

DOE OSTI2022

Securing against malicious control of circuit breakers in electrical substations

An example method for detecting and mitigating attacks on electric power substations comprises detecting a command to open or close a circuit breaker in the electric power substation. A modified extended substation model for the electric power substation is generated, based on the detected command and based on measurements in substation, where the modified extended substation model is a power flow model for the substation and for one or more directly connected neighboring substations. A power flow analysis is performed, using the modified extended substation model, to generate a predicted voltage for each of a plurality of nodes in the substation and in the one or more directly connected neighboring substations. Each predicted voltage is compared to a corresponding allowable voltage range, and execution of the command is blocked in response to determining that one or more of the voltages is outside the corresponding allowable voltage range.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collected under structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore, their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distance to the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensemble of trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fitting observed in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inverse physical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of the source strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations of underlying regression methods.

Rao, Nageswara↗

DOE OSTI2020

Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Functionalized Polymer Thin Films for Plutonium Capture and Isotopic Screening from Aqueous Sources

The rapid screening of plutonium from aqueous sources remains a critical challenge for nuclear nonproliferation efforts. The determination of trace-level Pu isotopes in water requires offsite sample preparation and analysis; therefore, new methods that combine plutonium purification, concentration, and isotopic screening in a fieldable detection system will provide an invaluable tool for nuclear safeguards. This contribution describes the development and characterization of thin polymer-ligand films for the isolation and concentration of waterborne Pu for direct spectroscopic analyses. Submicron thin films were prepared through spin coating onto Si wafers and consisted of combinations of polystyrene (PS) with dibenzoylmethane, thenoyltrifluoroacetone, and di(2-ethylhexyl)phosphoric acid (HDEHP). Pu uptake studies from solutions at pH from 2.3 to 6.3 indicated that only films containing HDEHP exhibited significant recovery of Pu. High alpha spectroscopy peak energy resolutions were achieved for PS-HDEHP films over a range of film thicknesses from 30 to 250 nm. Furthermore, a separate study was performed to evaluate uptake from a primarily Pu(V) solution where it was observed that doubling the HDEHP loading in the film increased uptake of Pu by an order of magnitude. X-ray photoelectron spectroscopy (XPS) analysis revealed that HDEHP was highly concentrated within the first few nanometers of the film at the higher loading. XPS analysis also revealed that, in the presence of water, HDEHP was stripped from the surface layer of the film at circumneutral pH. While significant losses of ligand were seen in all samples, higher loadings of HDEHP resulted in measurable amounts of ligand retained after a 12-h soak in water. Findings of this study are being used to guide the development of thin-film composite membrane-based detection methods for the rapid, fieldable analysis of Pu in water.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Sweep-tracing algorithm: in silico slip crystallography and tension-compression asymmetry in BCC metals

Abstract Direct Molecular Dynamics (MD) simulations are being increasingly employed to model dislocation-mediated crystal plasticity with atomic resolution. Thanks to the dislocation extraction algorithm (DXA), dislocation lines can be now accurately detected and positioned in space and their Burgers vector unambiguously identified in silico, while the simulation is being performed. However, DXA extracts static snapshots of dislocation configurations that by themselves present no information on dislocation motion. Referred to as a sweep-tracing algorithm (STA), here we introduce a practical computational method to observe dislocation motion and to accurately quantify its important characteristics such as preferential slip planes (slip crystallography). STA reconnects pairs of successive snapshots extracted by DXA and computes elementary slip facets thus precisely tracing the motion of dislocation segments from one snapshot to the next. As a testbed for our new method, we apply STA to the analysis of dislocation motion in large-scale MD simulations of single crystal plasticity in BCC metals. We observe that, when the crystal is subjected to uniaxial deformation along its [001] axis, dislocation slip predominantly occurs on the {112} maximum resolved shear stress plane under tension, while in compression slip is non-crystallographic (pencil) resulting in asymmetric mechanical response. The marked contrast in the observed slip crystallography is attributed to the twinning/anti-twinning asymmetry of shears in the {112} planes relatively favoring dislocation motion in the twinning sense while hindering dislocations from moving in the anti-twinning directions.

36 MATERIALS SCIENCE↗