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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Empowering Machine Learning Forecasting of Labquake Using Event‐Based Features and Clustering Characteristics

Abstract Following recent advances of machine learning (ML), we present a novel approach to extract spatiotemporal seismo‐mechanical features from Acoustic Emission (AE) catalogs to empower ML‐based forecasting. The AE data were recorded during laboratory stick‐slip experiments on granite samples cut by rough faults. Based on the features computed for a past time window, a random forest (RF) classifier is used to forecast the occurrence of a large magnitude event ( M AE > 3.5) in the next time window. Event‐based features allow us to associate informative time‐space characteristics to each feature and nearest‐neighbor clustering analysis enables us to separate background and clustered seismicity and train individual models. The results show that the separation of AEs enhances the forecasting accuracy from 73.2% for the entire catalog up to 82.1% and 89.0% if background and clustered events are used separately. The presented new approach may be upscaled for applications to forecast tectonic earthquakes.

Karimpouli, Sadegh↗
DOE OSTI2025

Nickase fidelity drives EvolvR-mediated diversification in mammalian cells

Abstract In vivo genetic diversifiers have previously enabled efficient searches of genetic variant fitness landscapes for continuous directed evolution. However, existing genomic diversification modalities for mammalian genomic loci exclusively rely on deaminases to generate transition mutations within target loci, forfeiting access to most missense mutations. Here, we engineer CRISPR-guided error-prone DNA polymerases (EvolvR) to diversify all four nucleotides within genomic loci in mammalian cells. We demonstrate that EvolvR generates both transition and transversion mutations throughout a mutation window of at least 40 bp and implement EvolvR to evolve previously unreported drug-resistantMAP2K1variants via substitutions not achievable with deaminases. Moreover, we discover that the nickase’s mismatch tolerance limits EvolvR’s mutation window and substitution biases in a gRNA-specific fashion. To compensate for gRNA-to-gRNA variability in mutagenesis, we maximize the number of gRNA target sequences by incorporating a PAM-flexible nickase into EvolvR. Finally, we find a strong correlation between predicted free energy changes underlying R-loop formation and EvolvR’s performance using a given gRNA. The EvolvR system diversifies all four nucleotides to enable the evolution of mammalian cells, while nuclease and gRNA-specific properties underlying nickase fidelity can be engineered to further enhance EvolvR’s mutation rates.

Science & Technology - Other Topics↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2023

Highly transparent silanized cellulose aerogels for boosting energy efficiency of glazing in buildings

Abstract To maintain comfortable indoor conditions, buildings consume ~40% of the energy generated globally. In terms of passively isolating building interiors from cold or hot outdoors, windows and skylights are the least-efficient parts of the building envelope because achieving simultaneously high transparency and thermal insulation of glazing remains a challenge. Here we describe highly transparent aerogels fabricated from cellulose, an Earth-abundant biopolymer, by utilizing approaches such as colloidal self assembly and procedures compatible with roll-to-roll processing. The aerogels have visible-range light transmission of 97–99% (better than glass), haze of ~1% and thermal conductivity lower than that of still air. These lightweight materials can be used as panes inside multi-pane insulating glass units and to retrofit existing windows. We demonstrate how aerogels boost energy efficiency and may enable advanced technical solutions for insulating glass units, skylights, daylighting and facade glazing, potentially increasing the role of glazing in building envelopes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data

We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g., Extreme Gradient Boosting (EGB) and simple regression methods e.g., support vector regressors with linear kernel and radial kernel in terms of accurate predictions. R language has been used for the statistical analysis of radon time series (RTS) data. The results obtained show that the proposed methodology predicts SRGC more accurately when compared to different traditional boosting and regression methods. The best correlation is found between the actual and predicted radon concentration for window size of 2 i.e., two days before and after the start of seismic activities. RTS data was collected from 05 February 2017 to 16 February 2018, including 7 seismic events recorded during the study period. Findings of study show that the proposed methodology predicts the SRGC with more precision, for all the window sizes, by overlapping predicted with the actual radon time series concentrations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cost estimates of production scale semitransparent organic photovoltaic modules for building integrated photovoltaics

Building integrated photovoltaics (BIPVs) are attached to commercial and residential structures to enable solar energy harvesting. While conventional Si photovoltaics (PVs) are dominant in the current market, second and third generation thin film solar cells based on amorphous Si, CdTe, CIGS, perovskites or organic photovoltaics (OPVs) are often considered as an alternative for BIPV applications since they may offer reduced costs compared to Si PVs. Indeed, recent advances in performance suggest that lightweight, flexible and visibly transparent OPVs can potentially be integrated into windows or other applications to which Si PVs are less well suited. Here, we estimate the cost of high efficiency, semitransparent OPVs (ST-OPVs) based on solution processing in a roll-to-roll (R2R) manufacturing line. Assuming modules with 10% power conversion efficiency (PCE), a 70% geometric fill factor (GFF), and 95% inverter efficiency, we anticipate a %1.6 per Wp module manufacturing cost that includes the cost of the microinverter to condition the OPV dc output to be compatible with the ac line voltage of the building. The materials and inverter cost comprise ~90% of the total module cost. Hence, with simplified material synthesis and a lower inverter cost, including marginally improved PCE and GFF, we expect the cost can be as low as $0.47 per Wp. Here, while the module costs ~60% of the average (uninstalled) double-pane window, we expect the payback period can be as short as 2 to 6 years, suggesting that OPVs can be an economic and attractive candidate for BIPV applications.

14 SOLAR ENERGY↗
DOE OSTI2022

Improved uranium isotopic ratio determinations for the liquid sampling-atmospheric pressure glow discharge orbitrap mass spectrometer by use of moving average processing

The liquid sampling-atmospheric pressure glow discharge (LS-APGD) is a versatile combined atomic and molecular (CAM) ionization source capable of ionizing elemental species, small polar compounds, low-polarity polycyclic aromatic hydrocarbons, and proteins. While the LS-APGD has been proven capable of determining the 235U/238U isotope ratio in enriched and natural uranium, recent efforts have strived to reduce the analysis time of individual measurements by employing higher-order data processing techniques, specifically, moving average methods. Additionally, the use of a moving average (of data windows of various widths) improves the precision of the ratio measurements and reduces the number of scans needed to be collected to generate high-precision results. Additionally, use of the moving average minimizes the quantity of sample that needs to be analyzed while reducing the measurement time. These reductions have allowed isotope ratio (IR) determinations to be made using injections instead of direct infusion of the analyte. For example, employing a 40-point window width in the moving average to direct infusion data reduced the percent relative standard deviation (% RSD) of the 235U/238U values from 3.4% to 0.5%. For the injection of a 100 μL aliquot, the % RSD was reduced from 5.3% to 0.7%.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Co-intercalation-free ether electrolytes for graphitic anodes in lithium-ion batteries

Carbonate-based electrolytes are widely used in Li-ion batteries but are limited by a small operating temperature window and poor cycling with silicon-containing graphitic anodes. The lack of non-carbonate electrolyte alternatives such as ether-based electrolytes is due to undesired solvent co-intercalation that occurs with graphitic anodes. Here, we show that fluoroethers are the first class of ether solvents to intrinsically support reversible lithium-ion intercalation into graphite without solvent co-intercalation at conventional salt concentrations. In full cells using a graphite anode, they enable 10-fold higher energy densities compared to conventional ethers, and better thermal stability over carbonate electrolytes (operation up to 60 °C) by producing a robust solvent-derived solid electrolyte interphase (SEI). As single-solvent–single-salt electrolytes, they remarkably outperform carbonate electrolytes with fluoroethylene carbonate (FEC) and vinylene carbonate (VC) additives when cycled with graphite–silicon composite anodes. In conclusion, our molecular design strategy opens a new class of electrolytes that can enable next generation Li-ion batteries with higher energy density and a wider working temperature window.

25 ENERGY STORAGE↗
DOE OSTI2024

Miniature fluorescence sensor for quantitative detection of brain tumour

Fluorescence-guided surgery has emerged as a vital tool for tumour resection procedures. As well as intraoperative tumour visualisation, 5-ALA-induced PpIX provides an avenue for quantitative tumour identification based on ratiometric fluorescence measurement. To this end, fluorescence imaging and fibre-based probes have enabled more precise demarcation between the cancerous and healthy tissues. These sensing approaches, which rely on collecting the fluorescence light from the tumour resection site and its “remote” spectral sensing, introduce challenges associated with optical losses. In this work, we demonstrate the viability of tumour detection at the resection site using a miniature fluorescence measurement system. Unlike the current bulky systems, which necessitate remote measurement, we have adopted a millimetre-sized spectral sensor chip for quantitative fluorescence measurements. A reliable measurement at the resection site requires a stable optical window between the tissue and the optoelectronic system. This is achieved using an antifouling diamond window, which provides stable optical transparency. The system achieved a sensitivity of 92.3% and specificity of 98.3% in detecting a surrogate tumour at a resolution of 1 × 1 mm 2 . In conclusion, as well as addressing losses associated with collecting and coupling fluorescence light in the current ‘remote’ sensing approaches, the small size of the system introduced in this work paves the way for its direct integration with the tumour resection tools with the aim of more accurate interoperative tumour identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Probing the lattice structure of dynamically compressed and released single crystal iron through the alpha to epsilon phase transition

In this work, experiments using broadband Laue x-ray diffraction (XRD) were used to examine the lattice structure of dynamically compressed [100]-oriented single crystal iron samples at the Dynamic Compression Sector at the Advanced Photon Source. These experiments used 1 μm thick iron single crystal samples sandwiched between a polyimide ablator and a polycarbonate window. A 100 J, 10 ns duration laser pulse incident on the polyimide ablator was used to shock compress the iron samples to initial stresses greater than 25 GPa, exceeding the ~13 GPa alpha (body-centered-cubic or bcc structure) to epsilon (hexagonal-close-packed or hcp structure) phase transition stress. XRD measurements were performed at various times relative to the shock wave entering the iron sample: early times, <~150 ps while the initial shock waves propagated through the iron; intermediate times, after the iron equilibrated with the ablator and window reaching a plateau stress state (12 or 17 GPa) lasting several nanoseconds; and late times, during uniaxial strain release. The early time measurements show that in <~150 ps, the high-pressure hcp phase is relaxed with a c/a ratio of 1.61, contrary to previous laser shock experiments where a c/a ratio of 1.7 was inferred. In the plateau stress state and partially released states, XRD measurements showed that the hcp structure retained a c/a ratio of 1.61 with no observable changes in the microstructure. Upon stress release at ~1 GPa/ns release rate, the reverse phase transition (hcp to bcc) to the original single crystal orientation (implying a transformation memory effect) was observed to reach completion somewhere between 13 and 11 GPa, indicating little stress hysteresis under rapid uniaxial strain release. A similar memory effect for the reverse hcp to bcc transformation has been previously observed under hydrostatic compression. However, the bcc/hcp orientation relationships differ somewhat between dynamic and static compression experiments, implying that the transformation pathway under uniaxial dynamic strain differs from the Burgers mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Stochastic density functional theory: Real- and energy-space fragmentation for noise reduction

Stochastic density functional theory (sDFT) is becoming a valuable tool for studying ground-state properties of extended materials. The computational complexity of describing the Kohn-Sham orbitals is replaced by introducing a set of random (stochastic) orbitals leading to linear and often sub-linear scaling of certain ground-state observables at the account of introducing a statistical error. Schemes to reduce the noise are essential, for example, for determining the structure using the forces obtained from sDFT. Recently, we have introduced two embedding schemes to mitigate the statistical fluctuations in the electron density and resultant forces on the nuclei. Both techniques were based on fragmenting the system either in real space or slicing the occupied space into energy windows, allowing for a significant reduction in the statistical fluctuations. For chemical accuracy, further reduction of the noise is required, which could be achieved by increasing the number of stochastic orbitals. However, the convergence is relatively slow as the statistical error scales as 1 / N &#x3C7; according to the central limit theorem, where N χ is the number of random orbitals. In this paper, we combined the embedding schemes mentioned above and introduced a new approach that builds on overlapped fragments and energy windows. The new approach significantly lowers the noise for ground-state properties, such as the electron density, total energy, and forces on the nuclei, as demonstrated for a G-center in bulk silicon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Investigation of UV filaments and their applications

UV laser pulses at 266 nm with 170 ps duration and up to 300 mJ energy create UV filaments. The effects of different beam preparation scenarios on UV filamentation are discussed. Beam preparation by focusing in vacuum and launching into the atmosphere through an aerodynamic window establishes the existence of filaments as self-guided non-diffracting entities. In addition, focusing through the aerodynamic window converts the initial super-Gaussian into a parabolic beam profile at the edge of the nonlinear medium (air). The shock-wave generated by the UV filament is investigated via shadowgraphy techniques. The shock-wave is cylindrically symmetric, leaving a low density channel behind, which is well-sustained for 1 ms, as it propagates outward. This low density channel is a waveguide that can be used for guiding purposes. Applications of UV filaments including guiding, remote sensing, and drag reduction for supersonic aircrafts are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Experimentally probing the extremes of droplet-on-demand printability via liquid metals

The liquid metal jetting droplet-on-demand technology provides many potential benefits to metal additive manufacturing. However, much is left to understand the operational margins to reliably create stable, satellite-free droplets. We elucidate these margins by experimentally probing the unexplored extremities of a recently proposed printability window based on the jet Weber (Wejet) and Ohnesorge (Z = 1/Oh) numbers. We generated stable droplets for 2 ≲ Wejet ≲ 4 for all tested Z = 339 to 2526—well beyond the bounds of any previously published printability window—suggesting that additional physics may be needed to understand droplet-on-demand in the high Z regime.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Use of a Mylar filter to eliminate vacuum ultraviolet pulse pileup in low-energy x-ray measurements

We describe a method to reduce vacuum ultraviolet (VUV) pulse pileup (PPU) in x-ray pulse-height Silicon Drift Detector (SDD) signals. An Amptek FAST SDD, with C1 (Si 3 N 4 ) window, measures bremsstrahlung emitted from PFRC-2 plasma to extract the electron temperature (T e ) and density (n e ). The C1 window has low transmissivity for photons with energy below 200 eV though will transmit some VUV and soft x-ray photons, which PFRC-2 plasmas abundantly emit. Multi-VUV-photon PPU contaminates the interpretation of x rays with energy > 100 eV, particularly in a low-energy exponential tail. The predicted low transmissivity of ~1 μm thick Mylar [polyethylene terephthalate (PET)] to photons of energy <100 eV led to the selection of Mylar as the candidate filter to reduce VUV PPU. Experiments were conducted on an x-ray tube with a graphite target and on a quasi-Maxwellian tenuous plasma (n e ~ 10 9 cm –3 ) with effective temperatures reaching 1500 eV. A Mylar filter thickness of 850 nm is consistent with the results. The Mylar-filter-equipped SDD was then used on the PFRC-2 plasma, showing a substantial reduction in the low-energy x-ray signal, supporting our hypothesis of the importance of VUV PPU. Here, we describe the modeling and experiments performed to characterize the effect of the Mylar filter on SDD measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Portable diagnostic package for Thomson scattering and optical emission spectroscopy on Princeton field-reversed configuration 2 (PFRC 2)

An Advanced Research Projects Agency-Energy funded diagnostic system has been deployed to the Princeton field-reversed configuration 2 (PFRC-2) device, located at Princeton Plasma Physics Laboratory. The Portable Diagnostic Package (PDP), designed at Oak Ridge National Laboratory, allows for the measurement of Thomson Scattering (TS) for electron density and temperature and Optical Emission Spectroscopy (OES) for ion temperature, impurity density, and ion velocity. A tunable spectrometer on the PDP with three gratings provides the flexibility to measure low (1 eV) and high (1000 eV) electron temperature ranges from TS. Additionally, using a second spectrometer, the OES diagnostic can survey light emission from various ion excitation levels for wide wavelength ranges. The electron density (<2 × 10 19 m –3 ) of plasmas generated in PFRC-2 has been below the PDP TS discrimination threshold, which has made TS signal detection challenging against a high-background of laser stray light. The laser stray light was iteratively reduced by making modifications to the entrance and exit geometry on PFRC-2. Rayleigh scattering experiments on PFRC have yielded the TS discrimination sensitivity to be >1 × 10 20 m –3 for the PDP. A recently implemented narrow-band notch spectral filter that masks the second harmonic 532 nm Nd:YAG laser wavelength has increased the system’s TS light discrimination sensitivity 65 times compared to the instance when the notch filter was not implemented. The hardware implementation including design changes to the flight tubes and Brewster windows will be discussed, along with results from Rayleigh and rotational Raman scattering sensitivity analyses, which were used to establish a quantitative figure of merit on the system performance. Further, the Raman scattering calibration with the notch filter has improved the PDP electron density threshold to 1 ± 0.5 × 10 18 m –3 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Resonant dynamics of one-side multipactor on dielectric

Breakdown of dielectric radio frequency (RF) windows is an important issue for particle accelerators and high-power RF sources. One of the common reasons for RF window failure is the multipactor on a dielectric surface. The multipactor may be responsible for excessive heating of the dielectric and discharge of charges that accumulated in the ceramic due to secondary emission. In this study, comprehensive self-consistent particle-in-cell simulations with space charge effect were performed. This was to better understand the dynamic of one-side multipactor development and floating potential on the dielectric induced by the emission. The important correlations between the multipactor parameters at saturation, the secondary emission properties of dielectric, and the applied RF field parameters have been found, which led to the conclusion that the dynamics of one-side multipactor on dielectric is a resonant phenomenon.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Modeling of the metal–insulator transition temperature in alio-valently doped VO 2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO 2 ) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO 2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

36 MATERIALS SCIENCE↗