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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Fast Muon Capture Monitoring in Mu2e with the CAPHRI Detector

The Mu2e experiment at Fermilab will search for the charged lepton flavor violating (CLFV) process of a neutrinoless muon-to-electron conversion in the field of an aluminum nucleus. Reaching the experiment’s target sensitivity requires precise normalization of the physics signal through accurate monitoring of the muon capture rate on the stopping target. For this purpose, the Calorimeter Precise High-Resolution Intensity detector (CAPHRI) has been developed. The detector is composed of four LYSO crystals installed in the upstream disk of the Mu2e calorimeter and read out with the standard calorimeter readout. CAPHRI measures the muon capture rate by detecting the characteristic 1.8~MeV gamma emission line of the $^{27}Al(\mu^−, \nu n \gamma) ^{26}Mg$ nuclear reaction. The fast, precise response enables injection-by-injection monitoring of proton beam intensity fluctuations. We report on the commissioning and performance characterization of CAPHRI. The response of each channel is calibrated at two SiPM overvoltages using both the intrinsic self-emission of the LYSO crystals and cosmic ray signals. In parallel, Monte Carlo simulations are used to evaluate the detector acceptance and the expected signal-to-background ratio under realistic running conditions. Preliminary results show a crystal light yield consistent with expectations and a channel inter-calibration at the 2--4% level. Simulation studies indicate that the detector acceptance and background rejection satisfy the requirements for physics operations, with about 1000 detected events per beam injection at a beam power of 1.5~kW. These results demonstrate that CAPHRI is an effective tool for beam monitoring and signal normalization in Mu2e.

Ciccarella, V. [Frascati; U. Rome La Sapienza (mai↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
DOE OSTI2023

Risk Ratio and Risk Difference Estimation in Case-cohort Studies

Background: In case-cohort studies with binary outcomes, ordinary logistic regression analyses have been widely used because of their computational simplicity. However, the resultant odds ratio estimates cannot be interpreted as relative risk measures unless the event rate is low. The risk ratio and risk difference are more favorable outcome measures that are directly interpreted as effect measures without the rare disease assumption. Methods: We provide pseudo-Poisson and pseudo-normal linear regression methods for estimating risk ratios and risk differences in analyses of case-cohort studies. These multivariate regression models are fitted by weighting the inverses of sampling probabilities. Also, the precisions of the risk ratio and risk difference estimators can be improved using auxiliary variable information, specifically by adapting the calibrated or estimated weights, which are readily measured on all samples from the whole cohort. Finally, we provide computational code in R (R Foundation for Statistical Computing, Vienna, Austria) that can easily perform these methods. Results: Through numerical analyses of artificially simulated data and the National Wilms Tumor Study data, accurate risk ratio and risk difference estimates were obtained using the pseudo-Poisson and pseudo-normal linear regression methods. Also, using the auxiliary variable information from the whole cohort, precisions of these estimators were markedly improved. Conclusion: The ordinary logistic regression analyses may provide uninterpretable effect measure estimates, and the risk ratio and risk difference estimation methods are effective alternative approaches for case-cohort studies. These methods are especially recommended under situations in which the event rate is not low.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Global horizontal spectral irradiance in Albuquerque - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has measured global horizontal spectral irradiance nearly continuously from September 2019 to March 2022. During this time other broadband irradiance measurements (global horizontal, direct normal, diffuse horizontal and global normal) and weather variables were also recorded. For this dataset PV Performance Labs (PVPL) has pulled together data from both sources to assemble a calendar year spectral data set for use in photovoltaic research. It is composed of twelve segments of 15 days each, beginning with January 1-15 and ending with December 1-15. The segments are all taken from the two-year period October 2019 to September 2021.

14 SOLAR ENERGY↗
DOE OSTI2021

Importance of Multiplicity Fluctuations in Entropy Scaling

One of the greatest uncertainties in heavy-ion collisions is the description of the initial state. Different models predict a wide range of initial energy density distributions based on their underlying assumptions. Final flow harmonics are sensitive to these differences in the initial state due to the nearly linear mapping between eccentricities and anisotropic flow harmonics. The Trento code uses a model-agnostic approach by phenomenologically parameterizing the initial state and constraining those parameters from a Bayesian analysis. There the multiplicity fluctuations were determined by a one parameter Γ distribution. However, initial-state models arising from the Color-Glass Condensate (CGC) framework lead to an initial energy density which is outside the functional form considered in Trento and its later Bayesian analyses because they rely on log-normal multiplicity fluctuations. We compare T A T B scaling (CGC-like) to √T A T B scaling (preferred from a Trento Bayesian analysis) and find that the T A T B form together with log-normal fluctuations is a reasonable candidate to describe the multiplicity fluctuations but leads to larger eccentricities, which would affect the extraction of viscosity in small systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

New helium recapture and reliquefier system for Dynamic Nuclear Polarization at UNH.

A new helium recapture and reliquefaction system, consisting of a gas bag, recapture manifold, cooling chiller, cryogenic purifier and, a gas storage cylinder banks, has been installed in the dynamic nuclear polarized target lab at the University of New Hampshire. This new system has the capability to improve the efficiency of our capacity to do target polarization runs by recycling our cryogenic helium which would otherwise be lost during polarization operations. Our helium liquefier is rated upto 40 L/Day under normal full-capacity operations, and is rated for a capacity of 500 liquid liters of helium. I explain the initial installation process followed by a discussion of the challenges with installation, including issues of impurities which appeared during the initial operation of the system. Then I discuss how we overcame these difficulties. Finally, I discuss the function of the system during normal operation and the present situation of the helium recapture and reliquefaction processing in the UNH Lab.

Lama, Chhetra [Univ. of New Hampshire, Durham, NH ↗
DOE OSTI2023

Nearly-frustration-free ground state preparation

Solving for quantum ground states is important for understanding the properties of quantum many-body systems, and quantum computers are potentially well-suited for solving for quantum ground states. Recent work [1] has presented a nearly optimal scheme that prepares ground states on a quantum computer for completely generic Hamiltonians, whose query complexity scales as δ − 1 , i.e. inversely with their normalized gap. Here we consider instead the ground state preparation problem restricted to a special subset of Hamiltonians, which includes those which we term "nearly-frustration-free": the class of Hamiltonians for which the ground state energy of their block-encoded and hence normalized Hamiltonian α − 1 H is within δ y of -1, where δ is the spectral gap of α − 1 H and 0 ≤ y ≤ 1 . For this subclass, we describe an algorithm whose dependence on the gap is asymptotically better, scaling as δ y / 2 − 1 , and show that this new dependence is optimal up to factors of log ⁡ δ . In addition, we give examples of physically motivated Hamiltonians which live in this subclass. Finally, we describe an extension of this method which allows the preparation of excited states both for generic Hamiltonians as well as, at a similar speedup as the ground state case, for those which are nearly frustration-free.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Mesh-Sequenced Realizations for Evaluation of Subgrid-Scale Models for Turbulent Combustion

This paper develops a new approach for analysis of subgrid closures for turbulent combustion as modeled using direct quadrature (finite-rate chemistry) techniques. The approach, termed multiresolution analysis through mesh-sequenced realizations (MRA-MSR), conducts simultaneous, constrained large-eddy simulations on a set of hierarchically coarsened meshes. Furthermore, the availability of underlying fine-mesh (subgrid) data corresponding to coarse-mesh locations allows a clearer assessment of the effects of unresolved fluctuations on apparent reactivity. A key to MRA-MSR is the correlation of eddy structures at coarser mesh levels, which is facilitated by the transfer of filtered fine-mesh velocity information. A seven-mesh MRA-MSR hierarchy using three resolution levels is applied to one of the Sydney bluff-body stabilized methane–hydrogen flames. Analysis of the simultaneously evolved data at different resolution levels reveals several interesting trends. First, at high Damköhler numbers, there is clear evidence of attenuation of apparent reactivity due to the effects of unresolved fluctuations. Secondly, single-point, single-time filtered density functions of a normalized subgrid Damköhler number show a characteristic beta probability density function (PDF) form and display evidence of scale similarity. Interrogation of the MRA-MSR database also shows that the recently-proposed least-squares minimization (LSM) turbulence–chemistry interaction model can account for the observed diminishment in reactivity at high Damköhler numbers but cannot reduce scatter significantly. A new form of the LSM model, which makes use of the normalized subgrid Damköhler number beta PDF distribution, performs slightly better than the original model, illustrating the potential of MRA-MSR both in assessing existing closure concepts and in developing new ones.

42 ENGINEERING↗
DOE OSTI2022

Metatransciptomic Analysis Data for Interactive effects of depth and differential irrigation on soil microbiome composition and functioning

RNA was collected from soil at different depths and after three different levels of irrigation T1 100% of normal field irrigation, T4: 18.75% or normal irrigation and T5: unirrigated controls. Total RNA was isolated using the Zymo Quick-RNA fecal/soil microbe miniprep (catalog no. R2040), incorporating the DNase I treatment using Zymo’s DNase I kit (catalog no. E1010). To increase the yield of RNA, we modified the manufacturer’s instructions by first doubling the amount of soil per extraction (from 0.25 g to 0.5 g) and by performing extractions in triplicate before pooling separate extractions together. Certain soil samples (largely those from deeper soil layers) had low yield (< 100 ng per extraction) so additional rounds of extraction were performed to obtain sufficient RNA. RNA concentration was assessed using a Qubit RNA HS assay kit (Thermo Fisher) and RNA quality was determined using an Agilent 2100 BioAnalyzer (Agilent; Santa Clara, CA). The resultant RNA samples were then sequenced by GENEWIZ using Illumina technology (GENEWIZ; South Plainfield, NJ). Sequences were then aligned to a soil metagenome previously obtained from the same site using the Burrows-Wheeler aligner (BWA). SAM files were then converted to raw counts using HTSeq.

Soil microbiome, metatranscriptomics↗
DOE OSTI2021

Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis

Background: Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals. Objective: Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis. Methods: Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed. Results: Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08). Conclusion: The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Analysis of the 2021 Milford, Utah earthquake swarm: Enhanced earthquake catalog and migration patterns

An earthquake swarm occurred in Spring 2021 in south-central Utah near the town of Milford. The University of Utah Seismograph Stations located 125 earthquakes between March 19 and May 10 with magnitudes ranging from 0.5 to 3.2. We implement a matched-filter technique in order to identify additional earthquakes that went undetected during the routine network location. The 125 network-located earthquakes are used as templates and are cross-correlated with continuous data for the dates Feb 17—June 10. This time period corresponds to approximately 1 month before the earthquake swarm began through 1 month after it ended. For the matched-filter analysis, we rely heavily on station FOR1, which is located within 5 km of most template events. Four other stations within 20–30 km of most template events provide a supplement to the closest station. The matched-filter implementation results in the detection of over 600 earthquakes in addition to the original 125 catalog events. This is one of the largest swarms ever recorded in Utah, and no previous large swarms have been recorded in this location. We use HypoDD to obtain relative double-difference locations of the catalog events. Both routine locations and HypoDD relocations of the catalog events suggest a fault dipping west beneath the mountain range, opposite of typical Basin and Range normal faults that dip beneath alluvial valleys. Moment tensors for the largest five events show normal faulting consistent with the west-dipping fault seen in the seismicity. Hydrothermal features in the area, including a geothermal power plant, suggest that fluids may be a contributing factor to the earthquake swarm triggering. We examine the role of fluids by exploring bounds on diffusion parameters and investigating spatial migration characteristics of the swarm seismicity. We conclude that this swarm is the result of heterogenous stress conditions in a prefractured region.

58 GEOSCIENCES↗
DOE OSTI2023

Dynamic subcanopy leaf traits drive resistance of net primary production across a disturbance severity gradient

Across the globe, the forest carbon sink is increasingly vulnerable to an expanding array of low- to moderate-severity disturbances. However, some forest ecosystems exhibit functional resistance (i.e., the capacity of ecosystems to continue functioning as usual) following disturbances such as extreme weather events and insect or fungal pathogen outbreaks. Unlike severe disturbances (e.g., stand-replacing wildfires), moderate severity disturbances do not always result in near-term declines in forest production because of the potential for compensatory growth, including enhanced subcanopy production. Community-wide shifts in subcanopy plant functional traits, prompted by disturbance-driven environmental change, may play a key mechanistic role in resisting declines in net primary production (NPP) up to thresholds of canopy loss. However, the temporal dynamics of these shifts, as well as the upper limits of disturbance for which subcanopy production can compensate, remain poorly characterized. In this study, we leverage a 4-year dataset from an experimental forest disturbance in northern Michigan to assess subcanopy community trait shifts as well as their utility in predicting ecosystem NPP resistance across a wide range of implemented disturbance severities. Through mechanical girdling of stems, we achieved a gradient of severity from 0% (i.e., control) to 45, 65, and 85% targeted gross canopy defoliation, replicated across four landscape ecosystems broadly representative of the Upper Great Lakes ecoregion. We found that three of four examined subcanopy community weighted mean (CWM) traits including leaf photosynthetic rate (p = 0.04), stomatal conductance (p = 0.07), and the red edge normalized difference vegetation index (p < 0.0001) shifted rapidly following disturbance but before widespread changes in subcanopy light environment triggered by canopy tree mortality. Surprisingly, stimulated subcanopy production fully compensated for upper canopy losses across our gradient of experimental severities, achieving complete resistance (i.e., no significant interannual differences from control) of whole ecosystem NPP even in the 85% disturbance treatment. Additionally, we identified a probable mechanistic switch from nutrient-driven to light-driven trait shifts as disturbance progressed. Our findings suggest that remotely sensed traits such as the red edge normalized difference vegetation index (reNDVI) could be particularly sensitive and robust predictors of production response to disturbance, even across compositionally diverse forests. The potential of leaf spectral indices to predict post-disturbance functional resistance is promising given the capabilities of airborne to satellite remote sensing. We conclude that dynamic functional trait shifts following disturbance can be used to predict production response across a wide range of disturbance severities.

resistance↗
DOE OSTI2024

Phase equilibria of advanced technology uranium silicide-based nuclear fuel

The phases in uranium-silicide binary system were evaluated in regards to their stabilities, phase boundaries, crystal structures, and phase transitions. The results from this study were used in combination with a well assessed literature to optimize the U-Si phase diagram using the CALPHAD method. A thermodynamic database was developed, which could be used to guide nuclear fuel fabrication, could be incorporated into other nuclear fuel thermodynamic databases, or could be used to generate data required by fuel performance codes to model fuel behavior in normal or off-normal reactor operations. The U 3 Si 2 and U 3 Si 5 phases were modeled using the Compound Energy Formalism model with 3 sublattices to account for the variation in composition. The crystal structure used for the USi phase was the tetragonal with an I4/mmm space. Above 450°C, the U 3 Si 5 phase was modeled. The composition of the USi 2 phase was adjusted to USi 1.84 . The calculated invariant reactions and the enthalpy of formation for the stoichiometric phases were in agreement with experimental data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Overview of CW electron guns and LCLS-II RF gun performance

Various continuous-wave (CW) electron gun technologies are reviewed, including DC, superconducting radio frequency RF (SRF), hybrid DC-SRF and normal-conducting RF. Also, the SLAC Linac Coherent Light Source II (LCLS-II) normal-conducting RF gun and injector are described, and the performance to date, including the bunch emittance achieved and the dark current observed, is presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Impacts of corn intercropping with soybean, peanut and millet through different planting patterns on population dynamics and community diversity of insects under fertilizer reduction

Corn is one of the key grain crops in China and the excessive use of chemical fertilizers and pesticides seriously damages the ecological environment in fields. To explore a more scientific and reasonable way to plant corn and simultaneously reduce the overuse of chemical fertilizers and pesticides, the impact of corn intercropping with soybean, peanut, and millet, respectively, through five planting patterns, including three intercropping patterns (2 corn rows to 2, 3 and 4 rows of soybean/peanut or 2, 4 and 6 millet rows, respectively) and two monoculture patterns of corn and soybean, peanut or millet under normal (600 kg/ha) and reduced (375 kg/ha) levels of NPK (N:P 2 O 5 :K 2 O = 15:15:15) fertilization on the population abundance and community diversity of insects, leaf nutrients, and induced plant hormones jasmonic acid (JA) and salicylic acid (SA) was studied in 2018 and 2019. The results showed that the insect community indexes of the species number ( S ), the diversity index ( H ), and the uniformity index ( E ) generally increased under intercropping and were significantly higher than those under corn monoculture. The prevalence of Asian corn borer ( Ostrinia furnacalis ) on the intercropping corn plants decreased by based on the average of seven surveys per year for each treatment 2.9 to 17 heads per 30 plants compared with that on the monoculture corn plants. The number of natural enemy insect species on corn plants under intercropping was significantly higher than that under corn monoculture. That is, intercropping may decrease the population of Asian corn borers by increasing S , H , E , and natural enemy insect species (NEI). Moreover, intercropping type and fertilizer level significantly affected corn leaf nutrient contents. Compared with the normal fertilizer level, fertilizer reduction significantly reduced the foliar contents of amino acids, soluble protein, and soluble sugar in corn plants. In addition, corn–soybean and corn–peanut intercropping significantly increased the three nutrient contents in corn leaves compared with corn monoculture. In terms of corn nutrients, intercropping could compensate for the effects of fertilizer reduction. The foliar content of JA in corn–soybean intercropping was significantly higher than in corn monoculture. Under corn–soybean and corn–peanut intercropping, SA was significantly lower than under corn monoculture. Overall, intercropping, not fertilizer reduction, can significantly increase insect community diversity while reducing the population abundances of the key insect pest species on corn plants. Intercropping reduced the SA content, increased amino acids and thus reduced the susceptibility of corn to the pest insects.

Li, Likun↗
DOE OSTI2020

Dispersible Colloid Facilitated Release of Organic Carbon From Two Contrasting Riparian Sediments

In aqueous systems, including groundwater, nano-colloids (1–100 nm diameter) and small colloids (<450 nm diameter) provide a vast store of surfaces to which organic carbon (OC) can sorb, precluding its normal bioavailability. Because nanomaterials are ubiquitous and abundant throughout Earth systems, it is reasonable that they would play a significant role in biogeochemical cycles. As such, mineral nano-colloids (MNC) and small colloids, formed through mineral weathering and precipitation processes, are both an unaccounted-for reservoir and unquantified vector for transport of OC and nutrients and contaminants within watersheds. Water extractions and leaching experiments were conducted under (1) aerobic (ambient) and (2) anaerobic (environmental chamber) conditions for each of two contrasting riparian sediments from (1) Columbia River, Washington and (2) Tims Branch, South Carolina. Water dispersible colloid-adsorbed OC was as high as 48% of OC for Tims Branch anaerobic batch water extraction and as low as 0% for Columbia River aerobic batch water extractions. Anaerobic leaching from column experiments yielded higher colloid and OC release rates. Transmission electron microscopy with electron dispersive spectroscopy mapping revealed organic carbon associated with aggregations of nano-particulate silicate minerals and Mossbauer identified nano-particulate goethite. This exploratory study demonstrates that mineral facilitated release of OC in riparian sediments is both significant and variable between locations.

Rod, Kenton A.↗
DOE OSTI2022

Evaluation of intracoronary hemodynamics identifies perturbations in vorticity

Coronary artery disease (CAD) is highly prevalent and associated with adverse events. Challenges have emerged in the treatment of intermediate coronary artery stenoses. These lesions are often interrogated with fractional flow reserve (FFR) testing to determine if a stenosis is likely to be causative for ischemia in a cardiac territory. This invasive test requires insertion of a pressure wire into a coronary vessel. Recently computational fluid dynamics (CFD) has been used to noninvasively assess fractional flow reserve in vessels reconstructed from medical imaging data. However, many of these simulations are unable to provide additional information about intravascular hemodynamics, including velocity, endothelial shear stress (ESS), and vorticity. We hypothesized that vorticity, which has demonstrated utility in the assessment of ventricular and aortic diseases, would also be an important hemodynamic factor in CAD. Three-dimensional (3D), patient-specific coronary artery geometries that included all vessels >1 mm in diameter were created from angiography data obtained from 10 patients who underwent diagnostic angiography and FFR testing (n = 9). A massively parallel CFD solver (HARVEY) was used to calculate coronary hemodynamic parameters including pressure, velocity, ESS, and vorticity. These simulations were validated by comparing velocity flow fields from simulation to both velocities derived from in vitro particle image velocimetry and to invasively acquired pressure wire-based data from clinical testing. There was strong agreement between findings from CFD simulations and particle image velocimetry experimental testing (p < 0.01). CFD-FFR was also highly correlated with invasively measured FFR (ρ = 0.77, p = 0.01) with an average error of 5.9 ± 0.1%. CFD-FFR also had a strong inverse correlation with the vorticity (ρ = -0.86, p = 0.001). Simulations to determine the effect of the coronary stenosis on intravascular hemodynamics demonstrated significant differences in velocity and vorticity (both p < 0.05). Further evaluation of an angiographically normal appearing non-FFR coronary vessel in patients with CAD also demonstrated differences in vorticity when compared with FFR vessels (p < 0.05). The use of highly accurate 3D CFD-derived intravascular hemodynamics provides additional information beyond pressure measurements that can be used to calculate FFR. Vorticity is one parameter that is modified by a coronary stenosis and appears to be abnormal in angiographically normal vessels in patients with CAD, highlighting a possible use-case in preventative screening for early coronary disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Electronic Structure Correspondence of Singlet-Triplet Scale Separation in Strained Sr2RuO4

At a temperature of roughly 1 K, Sr 2 RuO 4 undergoes a transition from a normal Fermi liquid to a superconducting phase. Even while the former is relatively simple and well understood, the superconducting state has not even been understood after 25 years of study. More recently, it has been found that critical temperatures can be enhanced by the application of uniaxial strain, up to a critical strain, after which it falls off. In this work, we take an “instability” approach and seek divergences in susceptibilities. This provides an unbiased way to distinguish tendencies to competing ground states. We show that in the unstrained compound, the singlet and triplet instabilities of the normal Fermi liquid phase are closely spaced. Under uniaxial strain, electrons residing on all orbitals contributing to the Fermiology become more coherent, while the electrons of the Ru-dxy character become heavier, and the electrons of the Ru d x z , y z characters become lighter. In the process, Im χ(q,ω) increases rapidly around q = ( 0.3 , 0.3 , 0 ) 2 π / a and q = ( 0.5 , 0.25 , 0 ) 2 π / a , while it gets suppressed at all other commensurate vectors, in particular at q = 0, which is essential for spin-triplet superconductivity. We observe that the magnetic anisotropy under strain drops smoothly, which is concomitant with the increment in singlet instability. Thus, the triplet superconducting instability remains the lagging instability of the system, and the singlet instability enhances under strain, leading to a large energy-scale separation between these competing instabilities. However, since this happens even without spin-orbit coupling, we believe it is primarily the enhancement in the spin fluctuation glue around quasi-anti-ferromagnetic vectors that drives the Cooper pairing instead of the magnetic anisotropy. At large strain, an instability to a spin density wave overtakes the superconducting one. The analysis relies on a high-fidelity, ab initio description of the one-particle properties and two-particle susceptibilities, based on the quasiparticle self-consistent GW approximation augmented by dynamical mean field theory. This approach is described and its high fidelity confirmed by comparing to observed one- and two-particle properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗