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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics

Abstract Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Boosting the interfacial superionic conduction of halide solid electrolytes for all-solid-state batteries

Designing highly conductive and (electro)chemical stable inorganic solid electrolytes using cost-effective materials is crucial for developing all-solid-state batteries. Here, we report halide nanocomposite solid electrolytes (HNSEs) ZrO 2 (-ACl)-A 2 ZrCl 6 (A = Li or Na) that demonstrate improved ionic conductivities at 30 °C, from 0.40 to 1.3 mS cm -1 and from 0.011 to 0.11 mS cm -1 for Li + and Na + , respectively, compared to A 2 ZrCl 6 , and improved compatibility with sulfide solid electrolytes. The mechanochemical method employing Li 2 O for the HNSEs synthesis enables the formation of nanostructured networks that promote interfacial superionic conduction. Via density functional theory calculations combined with synchrotron X-ray and 6 Li nuclear magnetic resonance measurements and analyses, we demonstrate that interfacial oxygen-substituted compounds are responsible for the boosted interfacial conduction mechanism. Compared to state-of-the-art Li 2 ZrCl 6 , the fluorinated ZrO 2 -2Li 2 ZrCl 5 F HNSE shows improved high-voltage stability and interfacial compatibility with Li 6 PS 5 Cl and layered lithium transition metal oxide-based positive electrodes without detrimentally affecting Li + conductivity. We also report the assembly and testing of a Li-In||LiNi 0.88 Co 0.11 Mn 0.01 O 2 all-solid-state lab-scale cell operating at 30 °C and 70 MPa and capable of delivering a specific discharge of 115 mAh g -1 after almost 2000 cycles at 400 mA g -1 .

36 MATERIALS SCIENCE↗
DOE OSTI2023

Storylines for unprecedented heatwaves based on ensemble boosting

Recent temperature extremes have shattered previously observed records, reaching intensities that were inconceivable before the events. Could the possibility of an event with such unprecedented intensity as the 2021 Pacific Northwest heatwave have been foreseen, based on climate model information available before the event? Could the scientific community have quantified its potential intensity based on the current generation of climate models? Here, we demonstrate how an ensemble boosting approach can be used to generate physically plausible storylines of a heatwave hotter than observed in the Pacific Northwest. We also show that heatwaves of much greater intensities than ever observed are possible in other locations like the Greater Chicago and Paris regions. In order to establish confidence in storylines of ‘black swan’-type events, different lines of evidence need to be combined along with process understanding to make this information robust and actionable for stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Human CD4-binding site antibody elicited by polyvalent DNA prime-protein boost vaccine neutralizes cross-clade tier-2-HIV strains

The vaccine elicitation of HIV tier-2-neutralization antibodies has been a challenge. Here, we report the isolation and characterization of a CD4-binding site (CD4bs) specific monoclonal antibody, HmAb64, from a human volunteer immunized with a polyvalent DNA prime-protein boost HIV vaccine. HmAb64 is derived from heavy chain variable germline gene IGHV1-18 and light chain germline gene IGKV1-39. It has a third heavy chain complementarity-determining region (CDR H3) of 15 amino acids. On a cross-clade panel of 208 HIV-1 pseudo-virus strains, HmAb64 neutralized 20 (10%), including tier-2 strains from clades B, BC, C, and G. The cryo-EM structure of the antigen-binding fragment of HmAb64 in complex with a CNE40 SOSIP trimer revealed details of its recognition; HmAb64 uses both heavy and light CDR3s to recognize the CD4-binding loop, a critical component of the CD4bs. This study demonstrates that a gp120-based vaccine can elicit antibodies capable of tier 2-HIV neutralization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Plasma-wakefield accelerator simultaneously boosts electron beam energy and brightness

High-energy particle colliders and X-ray free-electron lasers demand electron beams with qualities currently achieved only in kilometer-scale radio-frequency accelerators. Plasma accelerators promise a compact alternative but have faced challenges in delivering the needed beam quality at relevant energies. Here, we demonstrate that a plasma-wakefield accelerator operating in the nonlinear regime acts as a transformer to simultaneously boost the energy and brightness of an electron bunch injected from the plasma. Using a 10-GeV drive bunch and a three-stage meter-scale plasma source, we generated electron bunches exceeding 20 GeV with sub-percent energy spread, 2 mm·mrad normalized emittance, and multi-kA peak current. A significant number of drive-bunch electrons lost over 90% of their energy, a prerequisite for high energy-conversion efficiency. This demonstration of an energy transformer ratio exceeding two and a brightness enhancement over an order of magnitude opens a path towards cost-effective accelerators for future colliders and light sources.

experimental particle physics↗
DOE OSTI2021

Expansion of the high field-boosted superconductivity in UTe2 under pressure

Abstract Magnetic field-induced superconductivity is a fascinating quantum phenomenon, whose origin is yet to be fully understood. The recently discovered spin-triplet superconductor, UTe 2 , exhibits two such superconducting phases, with the second one reentering in the magnetic field of 45 T and persisting up to 65 T. More surprisingly, in order to induce this superconducting phase, the magnetic field has to be applied in a special angle range, not along any high symmetry crystalline direction. Here we investigated the evolution of this high-field-induced superconducting phase under pressure. Two superconducting phases merge together under pressure, and the zero resistance persists up to 45 T, the field limit of the current study. We also reveal that the high-field-induced superconducting phase is completely decoupled from the first-order field-polarized phase transition, different from the previously known example of field-induced superconductivity in URhGe, indicating superconductivity boosted by a different paring mechanism.

Materials Science↗
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

Boosting alkaline hydrogen evolution: the dominating role of interior modification in surface electrocatalysis

The alkaline hydrogen evolution reaction (A-HER) holds great promise for clean hydrogen fuel generation but its practical utilization is severely hindered by the sluggish kinetics for water dissociation in alkaline solutions. Traditional ways to improve the electrochemical kinetics for A-HER catalysts have been focusing on surface modification, which still can not meet the demanding requirements for practical water electrolysis because of catalyst surface deactivation. In this work, we report an interior modification strategy to significantly boost the A-HER performance. Specifically, a trace amount of Pt was doped in the interior Co 2 P (Pt–Co 2 P) to introduce a stronger dopant–host interaction than that of the surface-modified catalyst. Consequently, the local chemical state and electronic structure of the catalysts were adjusted to improve the electron mobility and reduce the energy barriers for hydrogen adsorption and H–H bond formation. As a proof-of-concept, the interior-modified Pt–Co 2 P shows a reduced onset potential at near-zero volts for the A-HER, low overpotentials of 2 mV and 58 mV to achieve 10 and 100 mA cm –2 , and excellent durability for long-term utilization. The interior-modified Pt–Co 2 P delivers superior A-HER performance to Pt/C and other state-of-the-art electrocatalysts. This work will open a new avenue for A-HER catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning”

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

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

Electron shuttle in the MOF derived TiO 2 /CuO heterojunction boosts light driven hydrogen evolution

Metal organic frameworks (MOFs) have emerged as a novel template to develop porous photocatalytic materials for solar fuel conversion. In this work, we report the synthesis, charge separation dynamics, and photocatalytic performance of the TiO 2 /CuO heterostructure derived from mixed-phase MOFs based on Ti and Cu metal nodes, which demonstrates significantly enhanced catalytic activity for the hydrogen evolution reaction (HER) compared to metal oxides derived from single node MOFs. More importantly, using transient absorption spectroscopy, we identified the specific role each component in the heterostructure plays and unravelled the key intermediate species that is responsible for the exceptional photocatalytic activity of the heterostructure. We found that the HER is initiated with ultrafast electron transfer (<150 fs) from the molecular photosensitizer to the conduction band of TiO 2 , where TiO 2 acts as an electron mediator and shuttles the electron to the CuO cocatalyst, facilitating charge separation and ultimately boosting the HER efficiency. Overall, these results not only demonstrate the great potential of using mixed-phase MOFs as templates to synthesize mesoporous heterostructure photocatalysts but also provide important insight into the HER mechanism.

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

End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use lowlevel detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves a ROC-AUC score of 0.975±0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier ROC-AUC score increases to 0.9824±0.0013, serving as the first performance benchmark for these CMS Open Data samples.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Investigating boosted decision trees as a guide for inertial confinement fusion design

Inertial confined fusion experiments at the National Ignition Facility have recently entered a new regime approaching ignition. Improved modeling and exploration of the experimental parameter space were essential to deepening our understanding of the mechanisms that degrade and amplify the neutron yield. The growing prevalence of machine learning in fusion studies opens a new avenue for investigation. Here in this paper, we have applied the Gradient-Boosted Decision Tree machine-learning architecture to further explore the parameter space and find correlations with the neutron yield, a key performance indicator. We find reasonable agreement between the measured and predicted yield, with a mean absolute percentage error on a randomly assigned test set of 35.5%. This model finds the characteristics of the laser pulse to be the most influential in prediction, as well as the hohlraum laser entrance hole diameter and an enhanced capsule fabrication technique. We used the trained model to scan over the design space of experiments from three different campaigns to evaluate the potential of this technique to provide design changes that could improve the resulting neutron yield. While these data-driven model cannot predict ignition without examples of ignited shots in the training set, it can be used to indicate that an unseen shot design will at least be in the upper range of previously observed neutron yields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Investigation of boosted proton energies through proton radiography of target normal sheath acceleration fields in the multi-ps regime

Multi-kilojoule, multi-picosecond short-pulse lasers, such as the National Ignition Facility-Advanced Radiographic Capability laser and the OMEGA-Extended Performance laser, which have been constructed over the last two decades, enable exciting opportunities to produce high-brightness, high-energy laser-driven proton sources for applications in high-energy-density science like proton fast ignition for inertial fusion energy, particle radiography, and materials science studies. Results on these platforms have demonstrated enhanced accelerated proton energies and electron temperatures when compared to established scaling laws. Recent work has developed a new scaling for proton TNSA in the multi-ps regime. However, this new physics in the multi-ps regime motivates the need to understand the origin of the enhancement in proton energies. Toward this goal, here this work presents the first measurements of the TNSA accelerating sheath field in the multi-ps regime for pulse durations of 0.6, 5, and 10 ps. This measurement was achieved by using a separate TNSA proton source to radiograph the spatiotemporal profile of the accelerating sheath that is responsible for proton acceleration. The use of stacked radiochromic film detectors allows for a discrete time profile of the radiographs, thus enabling the measurement of the temporal and spatial evolution of the accelerating field. In performing this measurement, we extract quantities such as the sheath strength as a function of time and pulse duration, which shows that longer pulse durations sustain a stronger electric field for a longer duration when compared to sub-ps laser pulses, which may enable the observed boosted proton energies and proton conversion efficiencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Kinematically boosted pairs from the nonlinear Breit–Wheeler process in small-angle laser collisions

Here we discuss that a scheme of nonperturbative pair production by high energy photons (ω≳m) in a strong external field is achievable at the next high intensity laser experiments. The pair momentum is boosted, and for ω≳1.2m, the pair yield is increased when the external field is formed by two laser pulses converging at a small angle. These characteristics are nonperturbative in origin and related to the presence of magnetic field in addition to electric field. By enhancing the signal over perturbative backgrounds, these features allow the employment of above-threshold photons ω > 2m, which further increases the pair yield. We note the close relation of this photon-pair conversion mechanism to spontaneous pair creation, recommending it as an accessible stepping stone experiment using state-of-the-art or soon-to-be laser technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new architecture called parallel decision paths that allows for deep decision trees with arbitrary number of input variables. It also features a new optimization scheme to use different numbers of bits for each input variable, which produces optimal physics results and ultraefficient FPGA resource utilization. Problems in high energy physics of proton collisions at the Large Hadron Collider (LHC) are considered. Estimation of missing transverse momentum (E T miss ) at the first level trigger system at the High Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by Delphes, is used to benchmark and characterize the firmware performance. The firmware implementation with a maximum depth of up to 10 using eight input variables of 16-bit precision gives a latency value of $\mathcal{O}$(10) ns, independent of the clock speed, and $\mathcal{O}$(0.1)% of the available FPGA resources without using digital signal processors.

Instruments & Instrumentation↗
DOE OSTI2024

Photon classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π + π 0 and π - π 0 , were analyzed, requiring the reconstruction of diphoton pairs. Here, in this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π 0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π 0 's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Boosts in leaf-level photosynthetic capacity aid Pinus ponderosa recovery from wildfire

Forests mitigate climate change by sequestering massive amounts of carbon, but recent increases in wildfire activity are threatening carbon storage. Currently, our understanding of wildfire impacts on forest resilience and the mechanisms controlling post-fire recovery remains unresolved due to a lack of empirical data on mature trees in natural settings. Here, we quantify the physiological mechanisms controlling carbon uptake immediately following wildfire in mature individuals of ponderosa pine (Pinus ponderosa), a wide-spread and canopy-dominant tree species in fire-prone forests. While photosynthetic capacity was lower in burned than unburned trees due to an overall depletion of resources, we show that within the burned trees, photosynthetic capacity increases with the severity of damage. Our data reveal that boosts in the efficiency of carbon uptake at the leaf-level may compensate for whole-tree damage, including the loss of leaf area and roots. We further show that heightened photosynthetic capacity in remaining needles on burned trees may be linked with reduced water stress and leaf nitrogen content, providing pivotal information about post-fire physiological processes. Our results have implications for Earth system modeling efforts because measurements of species-level physiological parameters are used in models to predict ecosystem and landscape-level carbon trajectories. Finally, current land management practices do not account for physiological resilience and recovery of severely burned trees. Our results suggest premature harvest may remove individuals that may otherwise survive, irrevocably altering forest carbon balance.

54 ENVIRONMENTAL SCIENCES↗