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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Enhancing Top-Down Proteomics of Brain Tissue with FAIMS

Proteomic investigations of Alzheimer’s and Parkinson’s disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein’s “intact” state. Top-down proteomics (TDP) overcomes this limitation, however it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, offline fractionation techniques are commonly used to reduce sample complexity, limiting throughput. A higher throughput alternative is online fractionation, such as gas phase high-field asymmetric waveform ion mobility spectrometry (FAIMS). Using a high complexity sample derived from Alzheimer’s disease brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS at -50 compensation voltage (CV) more than doubled the mean number of non-redundant proteoforms observed (1,833 ± 17, n = 3), compared to without (754 ± 35 proteoforms). We also found FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aß) proteoforms, including the aggregation-prone Aß1-42 variant which is strongly linked to Alzheimer’s disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Directional Cooling-Induced Fracturing: Directional Cooling Data Under True-Triaxial Stress State

Data includes Directional Cooling-Induced Fracturing (DCIF) testing data using westerly granite blocks. This submission includes data from two samples of westerly granite, lab sample 7 and 8. Files contain stress, temperature and acoustic emission data acquired during polyaxial, laboratory testing of westerly granite blocks for each sample. FILES: .tradb -- files containing acoustic emission waveforms; sqlite3 database .pridb -- files containing basic acoustic emission information (no waveforms); sqlite3 database .geom -- geometry of the AE sensor network (ASCII)

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Exploiting Multi-Domain Features for Detection of Unclassified Electromagnetic Signals

Deep Learning based classification techniques have shown excellent performance in static environments, where the training and testing samples are drawn from the same distribution. However, real world scenarios often present samples that do not belong to the known set of classes chosen during training. This is quite common for electromagnetic signals, where it is impractical to assume that all possible waveforms are known a-priori, specially in scenarios like warfare. To address this problem, we propose a deep learning based adversarial model where the generator learns to generate waveform features that can deceive the discriminator model as true samples. We introduce domain knowledge of wireless signals by decomposing the signal into a lower dimensional unique feature set, which is used for classifying known versus unknown signals. We further introduce multiple domain representations of the signal to extract features and combine them together to accurately classify new waveforms as an unknown class. Our results show that combined features from multiple domains outperform any single domain representation, especially at low SNR regimes with fewer number of samples to classify.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Exploiting Multi-Domain Features for Detection of Unclassified Electromagnetic Signals (Presentation)

Deep Learning based classification techniques have shown excellent performance in static environments, where the training and testing samples are drawn from the same distribution. However, real world scenarios often present samples that do not belong to the known set of classes chosen during training. This is quite common for electromagnetic signals, where it is impractical to assume that all possible waveforms are known a-priori, specially in scenarios like warfare. To address this problem, we propose a deep learning based adversarial model where the generator learns to generate waveform features that can deceive the discriminator model as true samples. We introduce domain knowledge of wireless signals by decomposing the signal into a lower dimensional unique feature set, which is used for classifying known versus unknown signals. We further introduce multiple domain representations of the signal to extract features and combine them together to accurately classify new waveforms as an unknown class. Our results show that combined features from multiple domains outperform any single domain representation, especially at low SNR regimes with fewer number of samples to classify.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Electro-optic sampling of classical and quantum light

Full characterization of electric-field waveforms in amplitude and phase is achieved across the terahertz to visible spectral range through interaction with an optical pulse shorter than a half-cycle period via the Pockels (linear electro-optic) effect. This technique of electro-optic sampling has become an indispensable tool in various areas, including ultrafast pump-probe, time-domain and frequency-comb spectroscopies, quantum optics, high-harmonic generation, and attosecond science, and holds great promise for further advances. Not only does it enable spectroscopic measurements with record dynamic range and temporal resolution, along with massively parallel real-time spectral data acquisition, but its remarkable sensitivity also allows the detection of vacuum fluctuations, i.e., “zero-point motion” of electric fields, profoundly impacting our understanding of the fundamental laws of nature.

Benea-Chelmus, Ileana-Cristina (ORCID:000000024814↗
DOE OSTI2023

Automated integration gate selection for Gaussian mixture model pulse shape discrimination

Pulse shapes differ between neutron and gamma particles when measured with detector devices employing pulse shape discriminating (PSD) scintillators. Digitized waveforms can be used in detection systems to perform pulse shape discrimination for this application. Prior Gaussian Mixture Model (GMM) methods require access to the pulse full-waveform. Reducing the waveform to a smaller set of combined samples reduces computational cost while affecting PSD performance. In this work, we develop a method for selecting the best performing combination of integration gates, or contiguous summed segments of the digitized pulse for PSD. The method uses a discrimination score based on the GMM PSD approach. Furthermore, the final selection is performed using Bayesian Optimization. PSD detection results are compared with varying numbers of selected gates on time-of-flight (TOF) data. This method can be used to fully automate the selection of gates in an unsupervised (without ground truth labels) setting.

42 ENGINEERING↗
DOE OSTI2021

In situ monitoring of microstructure evolution during thermal processing of uranium-zirconium alloys using laser-generated ultrasound

In this study, laser-generated ultrasound was used to monitor microstructure evolution during thermal processing of as-cast, polycrystalline binary uranium-zirconium metallic fuel alloys with compositions of U-20wt.%Zr (U-20Zr), U-50wt.%Zr (U-50Zr) and U-80wt.%Zr (U-80Zr). Ultrasonic waveforms were recorded during heating and cooling the samples from room temperature to >973 K and back. A phase transition temperature for all three compositions was estimated from the temperature at which an abrupt and rapid reduction in ultrasonic velocities was observed. Microstructural features on the length scale of tens of micrometers were inferred from the observation of scattering of ultrasonic waves by elastic heterogeneities above ~823 K in U-20Zr, while a hysteresis in the ultrasonic velocities of U-80Zr upon cooling was attributed to a partial retention of the high temperature phase following thermal annealing. The U-50Zr alloy exhibited a reversible viscoelastic response above 933 K, as evidenced by the observation of high frequency attenuation of the shear component of the waveforms at high temperature. Ultrasonic measurements were supplemented by in situ transmission electron microscopy (TEM). The TEM images revealed that the δ-U-Zr matrix in the three compositions underwent a spinodal decomposition above ~823 K into nanoscale regions. The ultrasonic measurements revealed larger, micron-scale structure evolution in the U-20Zr alloy at the same temperature. This large-scale structure is associated with heterogeneous regions having different Zr content. Our findings show the potential heating rate dependence of microstructural evolution in U-Zr alloys and highlight differences in the thermomechanical response and associated length scales during thermal annealing between single- and dual-phase compositions. These results demonstrate the utility of laser ultrasonics to rapidly and efficiently scan phase boundaries and monitor micrometer-scale structure evolution in metallic fuel alloys.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Blocker-tolerant and high-sensitivity $Δ$\!$Σ$ correlation digitizer for radar and coherent receiver applications

In this study, a coherent subsampling digitizer for pulsed Doppler radar systems is proposed. Prior to transmission, the radar system modulates the RF pulse with a known pseudorandom binary phase shift keying (BPSK) sequence. Upon reception, the radar digitizer uses a programmable sample-and-hold circuit to multiply the received waveform by a properly time-delayed version of the known a priori BPSK sequence. This operation demodulates the desired echo signal while suppressing the spectrum of all in-band noncorrelated interferers, making them appear as noise in the frequency domain. The resulting demodulated narrowband Doppler waveform is then subsampled at the IF frequency by a delta-sigma modulator. Because the digitization bandwidth within the delta-sigma feedback loop is much less than the input bandwidth to the digitizer, the thermal noise outside of the Doppler bandwidth is effectively filtered prior to quantization, providing an increase in signal-to-noise ratio (SNR) at the digitizer's output compared with the input SNR. In this demonstration, a delta-sigma correlation digitizer is fabricated in a 0.18-μm CMOS technology. The digitizer has a power consumption of 1.12 mW with an IIP3 of 7.5 dBm. The digitizer is able to recover Doppler tones in the presence of blockers up to 40 dBm greater than the Doppler tone.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data

Waveform enhancement methods generally explore lateral coherency in arrivals, often assuming a linear moveout across an array, as exhibited by plane waves. Here, we illustrate how unsupervised dictionary learning combined with orthogonal matching pursuit for feature extraction can be used for signal-to-noise ratio (SNR) enhancement. In this strategy, waveform characteristics are directly learned from provided data samples; the created dictionary is then used for signal extraction. This combination prevents the need to set a predefined dictionary, and it becomes computationally efficient because learning is only done on smaller data portions. Because the dictionary is learned from data, there is no assumption regarding wavefront shape or form. Tests on synthetic and field data demonstrate the better denoising performance in terms of SNR enhancement compared to other methods.

58 GEOSCIENCES↗
DOE OSTI2017

Band Excitation for Scanning Probe Microscopy

The Band Excitation (BE) technique for scanning probe microscopy uses a precisely determined waveform that contains specific frequencies to excite the cantilever or sample in an atomic force microscope to extract more information, and more reliable information from a sample. There are a myriad of details and complexities associated with implementing the BE technique. There is therefore a need to have a user friendly interface that allows typical microscopists access to this methodology. This software enables users of atomic force microscopes to easily: build complex band-excitation waveforms, set-up the microscope scanning conditions, configure the input and output electronics for generate the waveform as a voltage signal and capture the response of the system, perform analysis on the captured response, and display the results of the measurement.

Jesse, Stephen↗
DOE OSTI2019

Single-shot measurement of the free-space time domain terahertz (THz) waveforms by iterative frequency-domain interferometry technology

In this paper, a design of single-shot iterative frequency-domain interferometry (IFDI) technology is presented to measure ultrafast phase. Compared with frequency domain holography (FDH), IFDI technology has better accuracy and stability in some situations. Besides, combined with electro-optic sampling, comparative experiments on the free-space time domain terahertz waveforms measurement using FDH, IFDI, and femtosecond pump-probe technology are conducted, and the results confirm the feasibility and accuracy of the IFDI.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Development and Evaluation of a Cost-Effective Behind-the-Meter Synchronized Measurement Unit for Enhanced Grid Integration

This paper presents the development of the Inverter Based Resource Monitor (IBRM), an innovative behind-the-meter synchronized measurement unit (SMU) tailored for integration with inverter-based resources (IBRs). The IBRM distinguishes itself as a highly accurate and cost-effective SMU, offering facile deployment and connectivity to IBRs. It is equipped to conduct real-time voltage and current waveform analyses, serving as a phasor measurement unit (PMU) with exceptionally rapid synchrophasor transmission capabilities. The device incorporates a cutting-edge dual-core architecture designed to minimize sampling delays inherent to its microprocessor, thereby enhancing the precision of synchronized waveform measurements. Moreover, the IBRM is adept at recording high-fidelity waveform data, capturing nuances such as waveform distortions, high-order harmonics, and wide-band oscillations prevalent in power grids with substantial IBR presence. A prototype of the IBRM has been constructed and subjected to rigorous testing to assess its functional capabilities and measurement precision, utilizing both idealized signal generators and a real-world off-grid inverter setup as benchmarks.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗
DOE OSTI2018

Effect of modified periodic waveforms on current-induced spin polarization measurements

Applying a voltage to a semiconductor sample generates a current-induced electron spin polarization (CISP). Using an ultrafast mode-locked laser and lock-in detection scheme, we measure CISP on an indium gallium arsenide epilayer via Faraday rotation and extract the spin generation rate. While the measured spin polarization initially increases linearly with electric field as observed in previous work, larger applied voltages lead to a decreasing spin generation rate. Here we show that we can recover the linear dependence of spin generation rate with electric field even at larger applied voltages by modifying the applied voltage waveform to reduce heating and multiplying by an appropriate correction factor. Future CISP studies can utilize this technique to investigate CISP under larger applied electric fields.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Photoconductive and electro-optic effects in (Cd,Mg)Te single crystals measured in an experiment-on-chip configuration

We present our studies on both photoconductive (PC) and electro-optic (EO) responses of (Cd,Mg)Te single crystals. In an In-doped Cd 0.92 Mg 0.08 Te single crystal, subpicosecond electrical pulses were optically generated via a PC effect, coupled into a transmission line, and, subsequently, detected using an internal EO sampling scheme, all in the same (Cd,Mg)Te material. For photo-excitation and EO sampling, we used femtosecond optical pulses generated by the same Ti:sapphire laser with the wavelength 410 and 820 nm, respectively. The shortest transmission line distance between the optical excitation and EO sampling points was 75 μm. By measuring the transient waveforms at different distances from the excitation point, we calculated the transmission-line complex propagation factor, as well as the THz frequency attenuation factor and the propagation velocity, all of which allowed us to reconstruct the electromagnetic transient generated directly at the excitation point, showing that the original PC transient was subpicosecond in duration with a fall time of ~500 fs. Finally, the measured EO retardation, together with the amount of the electric-field penetration, allowed us to determine the magnitude of the internal EO effect in our (Cd,Mg)Te crystal. The obtained THz-frequency EO coefficient was equal to 0.4 pm/V, which is at the lower end among the values reported for CdTe-based ternaries, due to a twinned structure and misalignment of the tested (Cd,Mg)Te crystal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

MicroBooNE BNB Electron Neutrino Overlay Sample (No Wire Info)

MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is restricted to charged-current electron neutrino interactions within the argon active volume of the time projection chamber.The HDF5 files in this sample do not include the information at the wire waveform level ("NoWire" label), allowing for larger number of events to be included in the data set.More documentation, including detailed description of content, recipes, and example usage, at https://github.com/uboone/OpenSamples.Suggested text for acknowledgment is the following:We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].In addition, we request that software products resulting from the usage of the datasets are also made publicly available.

Abratenko, Polina↗
DOE OSTI2022

MicroBooNE BNB Inclusive Overlay Sample (No Wire Info)

MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is inclusive, i.e. it includes all types of neutrinos and interactions, with relative abundance matching our nominal flux and cross section models. Interactions are simulated in in the whole cryostat volume.The HDF5 files in this sample do not include the information at the wire waveform level ("NoWire" label), allowing for larger number of events to be included in the data set.More documentation, including detailed description of content, recipes, and example usage, at https://github.com/uboone/OpenSamples.Suggested text for acknowledgment is the following:We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].In addition, we request that software products resulting from the usage of the datasets are also made publicly available.

Abratenko, Polina↗
DOE OSTI2025

Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition

We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗