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DOE OSTI2024

Enhanced Cross-Shelf Exchange by the Eddies Associated With Plume Front

Broadened width of high chlorophyll concentration band with wavy structures, patches, and filaments are often observed along the western coastal next to the Pearl River Estuary over the northern South China Sea shelf during the transition period from winter to summer monsoon. Whereas, there is no such wide band in other seasons. By using a high-resolution numerical model, we reveal that the complex structure and wider band of high coastal chlorophyll concentration results from the smaller scale eddies (about 20–50 km in diameter) associated with buoyant plume-induced salinity front and density fronts, which are roughly along the 30 and 50 m isobaths, respectively. Two trains of eddies are formed along the fronts by the baroclinic instability triggered by frequently alternating winds over the fronts during the period of monsoon transition. The influences of these two trains of eddies are extended in the cross-shelf direction by their interactions, and they can temporally enhance the cross-shelf flow and material exchange. In conclusion, they serve as an efficient pathway to link the inner shelf toward the continental slope.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

An Investigation into High Voltage Spiral Generators Utilizing Thyristor Input Switches

High voltage nanosecond pulses are widely used in scientific research, but their wider adoption in industry requires compact, cost effective and easy to use generators to be developed. This paper presents modelling and experimental investigations into one method of producing such pulses – a spiral generator with a solid-state, thyristor-based input switch. It includes how the pulses are formed within the spiral, why a high speed input switch is required, how the geometry of the spiral dictates its output characteristics, and the effects of different loads. Using thyristors, often connected in series to increase the operating voltage of the spiral, enables the spiral generators to have low jitter, high repetition rate, and long lifetime. Modelling of the circuit used a combination of telegraph equations to account for the wave propagation along the spiral and a lumped circuit exchanging charge between the spiral, the input switch, and the load. The model is verified by detailed experimental results with the relative error being < 10% in most cases. Here, the output voltage pulse was often observed to have an initial peak of much lower magnitude than the subsequent peak(s) – which can only be fully explained by considering wave propagation effects. Lower input switch inductance, shorter switching time, larger mean diameter of the spiral, and increasing the width of the copper tape that makes up the spiral can all increase the voltage multiplication efficiency. Though increasing the number of turns that make up the spiral can increase the output voltage, it can also lower the multiplication efficiency. By understanding the effects of different geometries the spiral can be optimized to drive different loads - three applications of such spiral generators are then presented: pulses with 10 kV amplitude and 10 kHz repetition rate for driving DBD plasma, pulses with amplitude of 10 kV and 10 kV/ns rising rate for triggering of advanced solid state switches, and pulses with -50 kV amplitude and 50 ns rising time for triggering high current gas switches through field distortion.1 Index Terms—spiral generator, nanosecond pulses, telegraph equations, series-connected thyristor module, pulse forming process

42 ENGINEERING↗

DOE OSTI2026

Electrochemically mediated disproportionation for selective formaldehyde upcycling in acid

Formaldehyde (FA) electrolysis is attractive for paired production of value‑added chemicals. However, conventional electrolysis adopts alkaline electrolytes, which triggers FA self-disproportionation and severe feed loss. Here we introduce a sustainable and selective strategy for valorizing FA through electrochemically mediated disproportionation in acidic electrolytes. By leveraging a dual-electrode system consisting of a hydrophobic copper tetraminophthalocyanine layer (CuTAPc-layer) cathode and a Pt 2 Ru bimetallic anode, we efficiently convert FA into methanol and formic acid at high Faradaic efficiencies of 93.2% and 91.3%, respectively. Compared with alkaline FA oxidation, which can lose up to 76% FA and complicate downstream separation, the acidic system suppresses side reactions to ensure high product purity. Mechanism studies reveal that the hydrophobic microenvironment of CuTAPc-layer suppresses hydrogen evolution, while the stronger oxophilicity of Pt 2 Ru enhances FA activation and lowers the key deprotonation barrier for FA oxidation. The integrated device demonstrates application potential in polyoxymethylene upgrading, delivering 374.2 mA at 4 V with ~90% single-pass conversion, establishing a scalable and eco-friendly electrochemical pathway for chemical upcycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2002

Advanced Thin Ionization Calorimeter (ATIC) Update

The Advanced Thin Ionization Calorimeter (ATIC) experiment is designed to measure the composition and energy spectra of Z = 1 to 28 cosmic rays over the energy range of approximately 10 GeV - 100 TeV. ATIC is comprised of an eight-layer, 18 radiation length deep Bismuth Germanate (BGO) calorimeter, downstream of a 0.75 nuclear interaction length graphite target and an approximately 1 sq m finely segmented silicon charge detector. Interleaved with the graphite layers are three scintillator strip hodoscopes for pre-triggering and tracking. ATIC flew for the first time on a Long Duration Balloon (LDB) launched from McMurdo, Antarctica in January 2001. During its 16-day flight ATIC collected more than 30 million science events, along with housekeeping, calibration, and rate data. This presentation will describe the ATIC data processing, including calibration and efficiency corrections, and show results from analysis of this dataset. The next launch is planned for December 2002.

Ahn, H. S.↗

DOE OSTI2020

Aluminum Metal–Organic Framework Triggers Carbon Dioxide Reduction Activity

Confinement of metal centers is a powerful tool to manipulate reactivity and tune selectivity in chemical transformations. While aluminum as a foil is inactive for carbon dioxide reduction and shows high selectivity for the hydrogen evolution reaction, here we show that aluminum confined in a metal-organic framework (MOF), MIL-53(Al), suppresses hydrogen evolution reaction activity and enhances carbon dioxide reduction. This aluminum MOF can produce up to 40% faradaic efficiency for carbon monoxide and formic acid. Here, this study demonstrates that the unique reaction environment created by the MOF enables changes in reaction selectivity and can impart atypical catalytic capabilities to metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Measurement of the background in the CMS muon detector in ${p}{p}$-collisions at $\sqrt{s} = 13$ TeV

The CMS detector, including its muon system, has been operating at the CERN LHC in increasingly challenging conditions for about 15 years. The muon detector was designed to provide excellent triggering and track reconstruction for muons produced in proton–proton collisons at an instantaneous luminosity ($\mathcal{L}$) of 1 x 10 34 cm –2 s –1 . During the Run 2 data-taking period (2015–2018), the LHC achieved an instantaneous luminosity of twice its design value, resulting in larger background rates and making the efficient detection of muons more difficult. While some backgrounds result from natural radioactivity, cosmic rays, and interactions of the circulating protons with residual gas in the beam pipe, the dominant source of background hits in the muon system arises from proton–proton interactions themselves. Charged hadrons leaving the calorimeters produce energy deposits in the muon chambers. In addition, high-energy particles interacting in the hadron calorimeter and forward shielding elements generate thermal neutrons, which leak out of the calorimeter and shielding structures, filling the CMS cavern. We describe the method used to measure the background rates in the various muon subsystems. These rates, in conjunction with simulations, can be used to estimate the expected backgrounds in the High-Luminosity LHC. This machine will run for at least 10 years starting in 2029 reaching an instantaneous luminosity of $\mathcal{L}$ = 5 x 10 34 cm –2 s –1 and increasing ultimately to $\mathcal{L}$ = 7.5 x 10 34 cm –2 s –1 . These background estimates have been a key ingredient for the planning and design of the muon detector upgrade.

Tytgat, M. (ORCID:0000000239902074)↗

NASA NTRS1988

The global Cretaceous-Tertiary fire: Biomass or fossil carbon

The global soot layer at the K-T boundary indicates a major fire triggered by meteorite impact. However, it is not clear whether the principal fuel was biomass or fossil carbon. Forests are favored by delta value of C-13, which is close to the average for trees, but the total amount of elemental C is approximately 10 percent of the present living carbon, and thus requires very efficient conversion to soot. The PAH was analyzed at Woodside Creek, in the hope of finding a diagnostic molecular marker. A promising candidate is 1-methyl-7-isopropyl phenanthrene (retene,), which is probably derived by low temperature degradation of abietic acid. Unlike other PAH that form by pyrosynthesis at higher temperatures, retene has retained the characteristic side chains of its parent molecule. A total of 11 PAH compounds were identified in the boundary clay. Retene is present in substantial abundance. The identification was confirmed by analysis of a retene standard. Retene is characteristic of the combustion of resinous higher plants. Its formation depends on both temperature and oxygen access, and is apparently highest in oxygen-poor fires. Such fires would also produce soot more efficiently which may explain the high soot abundance. The relatively high level of coronene is not typical of a wood combustion source, however, though it can be produced during high temperature pyrolysis of methane, and presumably other H, C-containing materials. This would require large, hot, low O2 zones, which may occur only in very large fires. The presence of retene indicates that biomass was a significant fuel source for the soot at the Cretaceous-Tertiary boundary. The total amount of elemental C produced requires a greater than 3 percent soot yield, which is higher than typically observed for wildfires. However, retene and presumably coronene imply limited access of O2 and hence high soot yield.

Gilmour, Iain↗

NASA NTRS1994

Why galactic gamma-ray bursts might depend on environment: Blast waves around neutron stars

Although galactic models for gamma-ray bursts are hard to reconcile with the isotropy data, the issue is still sufficiently open that both options should be explored. The most likely 'triggers' for bursts in our Galaxy would be violent disturbances in the magnetospheres of neutron stars. Any event of this kind is likely to expel magnetic flux and plasma at relativistic speed. Such ejecta would be braked by the interstellar medium (ISM), and a gamma-ray flash may result from this interaction. The radiative efficiency, of this mechanism would depend on the density of the circumstellar ISM. Therefore, even if neutron stars were uniformly distributed in space (at least within 1-2 kpc of the Sun), the observed locations of bursts would correlate with regions of above-average ISM density.

Rees, Martin J.↗

DOE OSTI2024

Heralded Generation of Correlated Photon Pairs from CdS/CdSe/CdS Quantum Shells

Quantum information processing demands efficient quantum light sources (QLS) capable of producing high-fidelity single photons or entangled photon pairs. Single epitaxial quantum dots (QDs) have long been proven to be efficient sources of deterministic single photons; however, their production via molecular-beam epitaxy presents scalability challenges. Conversely, colloidal semiconductor QDs offer scalable solution processing and tunable photoluminescence, but suffer from broader linewidths and unstable emissions. This leads to spectrally inseparable emission from exciton (X) and biexciton (XX) states, complicating the production of single photons and triggered photon pairs. Here, in this work, we demonstrate that colloidal semiconductor quantum shells (QSs) achieve significant spectral separation (∼75–80 meV) and long temporal stability of X and XX emissive states, enabling the observation of exciton-biexciton bunching in colloidal QDs. Our low-temperature single-particle measurements show cascaded XX-X emission of single photon pairs for over 200 s, with minimal overlap between X and XX features. The X-XX distinguishability allows for an in-depth theoretical characterization of cross-correlation strength, placing it in perspective with photon pairs of epitaxial counterparts. These findings highlight a strong potential of semiconductor quantum shells for applications in quantum information processing.

biexciton↗

DOE OSTI2022

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.

47 OTHER INSTRUMENTATION↗

NASA NTRS1992

Operator assistant to support deep space network link monitor and control

Preparing the Deep Space Network (DSN) stations to support spacecraft missions (referred to as pre-cal, for pre-calibration) is currently an operator and time intensive activity. Operators are responsible for sending and monitoring several hundred operator directivities, messages, and warnings. Operator directives are used to configure and calibrate the various subsystems (antenna, receiver, etc.) necessary to establish a spacecraft link. Messages and warnings are issued by the subsystems upon completion of an operation, changes of status, or an anomalous condition. Some points of pre-cal are logically parallel. Significant time savings could be realized if the existing Link Monitor and Control system (LMC) could support the operator in exploiting the parallelism inherent in pre-cal activities. Currently, operators may work on the individual subsystems in parallel, however, the burden of monitoring these parallel operations resides solely with the operator. Messages, warnings, and directives are all presented as they are received; without being correlated to the event that triggered them. Pre-cal is essentially an overhead activity. During pre-cal, no mission is supported, and no other activity can be performed using the equipment in the link. Therefore, it is highly desirable to reduce pre-cal time as much as possible. One approach to do this, as well as to increase efficiency and reduce errors, is the LMC Operator Assistant (OA). The LMC OA prototype demonstrates an architecture which can be used in concert with the existing LMC to exploit parallelism in pre-cal operations while providing the operators with a true monitoring capability, situational awareness and positive control. This paper presents an overview of the LMC OA architecture and the results from initial prototyping and test activities.

Cooper, Lynne P.↗

NASA NTRS2009

Complex Event Recognition Architecture

Complex Event Recognition Architecture (CERA) is the name of a computational architecture, and software that implements the architecture, for recognizing complex event patterns that may be spread across multiple streams of input data. One of the main components of CERA is an intuitive event pattern language that simplifies what would otherwise be the complex, difficult tasks of creating logical descriptions of combinations of temporal events and defining rules for combining information from different sources over time. In this language, recognition patterns are defined in simple, declarative statements that combine point events from given input streams with those from other streams, using conjunction, disjunction, and negation. Patterns can be built on one another recursively to describe very rich, temporally extended combinations of events. Thereafter, a run-time matching algorithm in CERA efficiently matches these patterns against input data and signals when patterns are recognized. CERA can be used to monitor complex systems and to signal operators or initiate corrective actions when anomalous conditions are recognized. CERA can be run as a stand-alone monitoring system, or it can be integrated into a larger system to automatically trigger responses to changing environments or problematic situations.

Fitzgerald, William A.↗

DOE OSTI2020

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

field testing↗

DOE OSTI2020

Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Conceptual Design Report for the MATHUSLA Long-Lived Particle Detector near CMS

We present the Conceptual Design Report (CDR) for the MATHUSLA (MAssive Timing Hodoscope for Ultra-Stable neutraL pArticles) long-lived particle detector at the HL-LHC, covering the design, fabrication and installation at CERN Point 5. MATHUSLA is a 40 m-scale detector with an air-filled decay volume that is instrumented with scintillator tracking detectors, to be located near CMS. Its large size, close proximity to the CMS interaction point and about 100 m of rock shielding from HL-LHC backgrounds allows it to detect LLP production rates and lifetimes that are one to two orders of magnitude beyond the ultimate sensitivity of the HL-LHC main detectors for many highly motivated LLP signals. Data taking is projected to commence with the start of HL-LHC operations. We present a new 40m design for the detector: its individual scintillator bars and wavelength-shifting fibers, their organization into tracking layers, tracking modules, tower modules and the veto detector; define a high-level design for the supporting electronics, DAQ and trigger system, including supplying a hardware trigger signal to CMS to record the LLP production event; outline computing systems, civil engineering and safety considerations; and present preliminary cost estimates and timelines for the project. We also conduct detailed simulation studies of the important cosmic ray and HL-LHC muon backgrounds, implementing full track/vertex reconstruction and background rejection, to ultimately demonstrate high signal efficiency and $\ll 1$ background event in realistic LLP searches for the main physics targets at MATHUSLA. This sensitivity is robust with respect to detector design or background simulation details. Appendices provide various supplemental information.

Aitken, Branden [Victoria U.]↗

NASA NTRS2023

Ames Contrail Simulation Model: Modeling Aviation Induced Contrails and the Computation of Contrail Radiative Forcing Using Air Traffic Data

NASA is developing traffic flow management strategies to reduce the impact of aviation on climate and improve efficiency of aircraft routes in the presence of limited airport and airspace capacity constraints, which are partly due to convective weather and natural calamities. Development of Strategic Traffic Flow Management (STFM) requires models of aircraft emissions and contrails, and models of their effect on climate. There is extensive literature on the modeling of contrails and the Radiative Forcing (RF) associated with contrails. This report captures the latest NASA developments in this research area and describes the integrated modeling, analysis, and software development to support STFM. Aircraft contrails are long, thin and often linear clouds triggered by aircraft engine exhausts in the high-altitude ice-saturated atmosphere. Contrails, similar to that of natural cirrus clouds, can impact global climate by reflecting shortwave radiation and trapping longwave radiation. Recent studies from the Intergovernmental Panel on Climate Change (IPCC) have shown that aircraft contrails are estimated to have greater impact on global warming than aircraft CO 2 emissions. The Ames Contrail Simulation Model (ACSM) presented in this report simulates the full life cycle of aircraft contrails, including their formation, dynamic evolution, and dissipation, and calculates the associated RF with actual meteorological and air traffic data. ACSM combines models from previous studies that focus on contrail formation and persistence based on the Schmidt-Appleman theoretical criteria, and it incorporates elements of cloud dynamics, microphysics, and climate modeling found in other surveyed contrail models, while also making adjustment for improved computational efficiency. In addition, ACSM is integrated with NASA's state-of-the-art flight simulation software for rapid assessment of aircraft contrail impacts. Applications include assessments of long-term global climate impact resulting from aviation-induced contrails and the design of optimal contrail-mitigation aircraft operation strategies.

aviation contrails↗

DOE OSTI2019

Photoenzymatic Enantioselective Intermolecular Radical Hydroalkylation: Experimental Crystal Structure Determination

Enzymes are increasingly explored for use in asymmetric synthesis, but their applications are generally limited by the reactions available to naturally occurring enzymes. Recently, interest in photocatalysis has spurred the discovery of novel reactivity from known enzymes. However, so far photoinduced enzymatic catalysis has not been used for the cross-coupling of two molecules. For example, the intermolecular coupling of alkenes with α-halo carbonyl compounds through a visible-light-induced radical hydroalkylation, which could provide access to important γ-chiral carbonyl compounds, has not yet been achieved by enzymes. The major challenges are the inherent poor photoreactivity of enzymes and the difficulty in achieving stereochemical control of the remote prochiral radical intermediate. Here we report a visible-light-induced intermolecular radical hydroalkylation of terminal alkenes that does not occur naturally, catalysed by an ‘ene’ reductase using readily available α-halo carbonyl compounds as reactants. This method provides an efficient approach to the synthesis of various carbonyl compounds bearing a γ-stereocentre with excellent yields and enantioselectivities (up to 99 percent yield with 99 percent enantiomeric excess), which otherwise are difficult to access using chemocatalysis. Mechanistic studies suggest that the formation of the complex of the substrates (α-halo carbonyl compounds) and the ‘ene’ reductase triggers the enantioselective photoinduced radical reaction. Our work further expands the reactivity repertoire of biocatalytic, synthetically useful asymmetric transformations by the merger of photocatalysis and enzyme catalysis.

Huang, Xiaoqiang↗

NASA NTRS2016

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.↗