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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dark Energy Survey year 6 results: Magnification modeling and its impact on galaxy clustering and galaxy-galaxy lensing cosmology

Gravitational lensing magnification alters the observed spatial distribution of galaxies and must be accounted for to prevent biases in cosmological probes of the large-scale structure. We investigate its effects on the Dark Energy Survey Year 6 galaxy clustering and galaxy-galaxy lensing analyses using the fiducial lens (position tracer) sample M ag L im++. Magnification bias is parameterized by a coefficient that describes the response of the number of selected objects per unlensed area element to a change in the lensing convergence. We quantify this coefficient using the BALROG synthetic source injection catalog to account for the complexity of the selection function, and compare these results with simplified estimates. The resulting values of the magnification coefficients for each redshift bin are [3.16 ± 0.08, 2.76 ± 0.21, 4.09 ± 0.15, 4.42 ± 0.16, 4.90 ± 0.29, 4.83 ± 0.25]. Relative to Year 3, this analysis provides more precise and accurate magnification bias estimates through a larger BALROG area and reweighting to better match the data properties. Here, the cosmological results are robust when tested against various magnification parameter prior choices and also when adding cross-clustering between lens redshift bins. Neglecting magnification, however, introduces significant systematic shifts: relative to the fiducial analysis with Gaussian priors centered on the BALROG -derived estimates, we observe shifts of 1.37σ in S 8 and -0.84σ in Ω m (with cosmic shear included: -0.61σ in S 8 and -0.71σ in Ω m ), in agreement with findings from simulated data, demonstrating that magnification must be modeled to avoid biases. Freeing the magnification bias in lens bin 2 leads to unphysical negative values, further justifying its exclusion from the fiducial Year 6 analysis.

Cosmological parameters↗
DOE OSTI2022

Longitudinal Trajectories of Memory Performance in Patients with Early-Stage Breast Cancer

Background. While breast cancer and its treatments may affect cognition, the longitudinal trajectories of cognition among those receiving differing cancer treatment types remain poorly understood. Prior research suggests hippocampal-prefrontal cortex network integrity may influence cognition, although how this network predicts performance over time remains unclear. Methods. We conducted a prospective trial including 69 patients with early-stage breast cancer receiving adjuvant therapy and 12 controls. Longitudinal cognitive testing was conducted at four visits: pretreatment-baseline, 6-7 months, 14-15 months, and 23-24 months. Cognitive composite scores of episodic memory, executive functioning, and processing speed were assessed at each timepoint. Baseline structural MRI was obtained in a subset of these participants, and hippocampal and prefrontal cortex regional volumes were extracted. Results. Longitudinal linear mixed modeling revealed significant group by time interactions on memory performance, controlling for age and education. Post hoc analyses revealed this effect was driven by patients treated with chemotherapy or chemotherapy plus hormone therapy, who demonstrated the least improvement in memory scores over time. Treatment group did not significantly influence the relationship between time and processing speed or executive functioning. Neither pretreatment hippocampal nor prefrontal volume differed between groups, and there were no significant group by time by baseline regional volume effects on cognition. Conclusion. Patients with early-stage breast cancer treated with chemotherapy or chemotherapy plus hormone therapy benefit less from practice effects seen in healthy controls on memory tests. Loss of longitudinal practice effect may be a new and clinically relevant measure for capturing patients’ experience of cognitive difficulties after treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A workflow to assess the efficacy of brine extraction for managing injection-induced seismicity potential using data from a CO 2 injection site near Decatur, Illinois

Injection of CO 2 for storage in a geologic formation increases pore pressure and alters in situ stresses. Depending on the orientation of any existing fault and fracture planes, such as critically stressed planes, this stress alteration will modify normal stresses acting on planes and could result in frictional sliding and release stored energy in the form of seismicity. Brine extraction (BE) is a technique that can be applied prior to, or during, CO 2 injection to reduce pore pressure for increasing storage capacity and, potentially, for reducing the likelihood of frictional sliding. Here a workflow is described to assess the efficacy of BE for mitigating frictional sliding (i.e., seismicity) during injection and entails: site characterization, stress calculations and failure assessment, static and dynamic modeling, and BE operational planning. Site characterization describes the stress field used to calculate the Coulomb Failure Function (CFF) that constrains allowable pore pressure changes and injection rates in the numerical simulation of CO 2 injection scenarios. The inclusion of BE in the workflow allows for determination of the potential need for pressure reduction, and evaluation of the effectiveness of this operation. Example application of the workflow using an injection field dataset near Decatur, IL, provides insight on fracture planes and stresses at the site, formation properties and the impact of variable CO 2 injection-rate targets on whether BE plans are required. The study workflow indicates that BE could enhance CO 2 injection rate by 39% and correspondingly reduce the potential for injection-induced seismicity as indicated by a reduction in CFF.

58 GEOSCIENCES↗
DOE OSTI2025

Overcoming the Conductance versus Crossover Trade-off in State-of-the-Art Proton Exchange Fuel-Cell Membranes by Incorporating Atomically Thin Chemical Vapor Deposition Graphene

Permeance–selectivity trade-offs are inherent to polymeric membranes. In fuel cells, thinner proton exchange membranes (PEMs) could enable higher proton conductance and increased power density with lower area-specific resistance (ASR), smaller ohmic losses, and lower ionomer cost. However, reducing thickness is accompanied by an increase in undesired species crossover harming performance and long-term efficiency. Here, we show that incorporating atomically thin monolayer graphene synthesized via scalable chemical vapor deposition (CVD) and tunable defect density into PEMs (Nafion, ~5–25 μm thick) can allow for reduced H 2 crossover (~34–78% of Nafion of a similar thickness) while maintaining adequate areal proton conductance for applications (>4 S cm –2 ). In contrast to most prior work using >50 μm symmetric Nafion sandwich structures, we elucidate the interplay of graphene defect density and Nafion proton transport resistance on the performance of Nafion|graphene composite membranes and find high-quality low-defect density CVD graphene (G) supported on Nafion 211 (~25 μm); i.e., N211|G has a high areal proton conductance (~6.1 S cm –2 ) and the lowest H 2 crossover (~0.7 mA cm –2 ). Fully functional centimeter-scale N211|G fuel-cell membranes demonstrate performance comparable to that of state-of-the-art Nafion N211 at room temperature as well as standard operating conditions (~80 °C, ~150–250 kPa-abs) with H 2 /air (power density ~0.57–0.63 W cm –2 ) and H 2 /O 2 feed (power density ~1.4–1.62 W cm –2 ) and markedly reduced H 2 crossover (~53–57%).

25 ENERGY STORAGE↗
DOE OSTI2023

Striatal dopamine synthesis and cognitive flexibility differ between hormonal contraceptive users and nonusers

In rodents and nonhuman primates, sex hormones are powerful modulators of dopamine (DA) neurotransmission. Yet less is known about hormonal regulation of the DA system in the human brain. Using positron emission tomography (PET), we address this gap by comparing hormonal contraceptive users and nonusers across multiple aspects of DA function: DA synthesis capacity via the PET radioligand 6-[ 18 F]fluoro-m-tyrosine ([ 18 F]FMT), baseline D2/3 receptor binding potential using [ 11 C]raclopride, and DA release using methylphenidate-paired [ 11 C]raclopride. Participants consisted of 36 healthy women (n = 15 hormonal contraceptive users; n = 21 naturally cycling/non users of hormonal contraception), and men (n = 20) as a comparison group. A behavioral index of cognitive flexibility was assessed prior to PET imaging. Hormonal contraceptive users exhibited greater DA synthesis capacity than NC participants, particularly in dorsal caudate, and greater cognitive flexibility. Furthermore, across individuals, the magnitude of striatal DA synthesis capacity was associated with cognitive flexibility. No group differences were observed in D2/3 receptor binding or DA release. Analyses by sex alone may obscure underlying differences in DA synthesis tied to women’s hormone status. Hormonal contraception (in the form of pill, shot, implant, ring, or intrauterine device) is used by ~400 million women worldwide, yet few studies have examined whether chronic hormonal manipulations impact basic properties of the DA system. Findings from this study begin to address this critical gap in women’s health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Assessing the Ability of Developmentally Precocious Estrogen Signaling to Recapitulate Ovarian Transcriptomes and Follicle Dynamics in Alligators from a Contaminated Lake

Concern has grown in recent decades over anthropogenic contaminants that interfere with the functioning of endocrine hormones. However, mechanisms connecting developmental processes to pathologies associated with endocrine-disrupting chemical (EDC) exposure are poorly understood in naturally exposed populations. We sought to a) characterize divergence in ovarian transcriptomic and follicular profiles between alligators originating from a historically EDC-contaminated site, Lake Apopka, and a reference site; b) test the ability of developmentally precocious estrogen exposure to recapitulate site-associated patterns of divergence; and c) test whether treatment with exogenous follicle-stimulating hormone (FSH) is capable of rescuing phenotypes associated with contaminant exposure and/or embryonic estrogen treatment. Alligators eggs were collected from a contaminated site and a reference site, and a subset of eggs from the reference site were treated with estradiol (E2) during embryonic development prior to gonadal differentiation. After hatching, alligators were raised under controlled laboratory settings for 5 months. Juveniles from both sites were divided and treated with exogenous FSH. Histological analyses and RNA-sequencing were conducted to characterize divergence in ovarian follicle dynamics and transcriptomes between sites, between reference and E2-treated animals, and between FSH-treated and nontreated animals. We observed broad site-of-origin divergence in ovarian transcriptomes and reductions in ovarian follicle density between juvenile alligators from Lake Apopka and the reference site. Treating embryos from the reference site with E2 overwhelmingly recapitulated transcriptional and histological alterations observed in Lake Apopka juveniles. Ovarian phenotypes observed in Lake Apopka alligators or resulting from estrogen treatment were only partially rescued by treatment with exogenous FSH. Recapitulation of ovarian abnormalities by precocious E2 revealed a relatively simple mechanism underlying contaminant-induced pathologies in a historical example of environmental endocrine disruption. Findings reported here support a model where the developmental timing of estrogen signaling has the potential to permanently alter ovarian organization and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Optimization-based control using input convex neural networks

Input convex neural networks (ICNNs) are a family of deep learning models where the outputs are constructed to be convex functions of the inputs. By parameterizing system models using ICNNs, optimization-based control problems can be solved as convex optimization problems, leading to improved performance and robustness. This work proposes a novel framework where the control objective function and constraints are modelled using ICNNs. A case study of optimization-based control with output constraints is conducted on a process with input multiplicity and nonminimum phase behavior. The simulation results demonstrate improved economic yield compared with normal neural networks. Additionally, the input convexity formulation is compared with simple regularization techniques, and unique benefits such as improved data efficiency and robustness of the proposed formulation are shown. Finally, by explicitly incorporating prior knowledge about convexity, this framework provides a good balance between the universal approximation power of deep learning and computational feasibility required by control.

42 ENGINEERING↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2024

Unifying thermochemistry concepts in computational heterogeneous catalysis

Thermophysical properties of adsorbates and gas-phase species define the free energy landscape of heterogeneously catalyzed processes and are pivotal for an atomistic understanding of the catalyst performance. These thermophysical properties, such as the free energy or the enthalpy, are typically derived from density functional theory (DFT) calculations. Enthalpies are species-interdependent properties that are only meaningful when referenced to other species. The widespread use of DFT has led to a proliferation of new energetic data in the literature and databases. However, there is a lack of consistency in how DFT data is referenced and how the associated enthalpies or free energies are stored and reported, leading to challenges in reproducing or utilizing the results of prior work. Additionally, DFT suffers from exchange–correlation errors that often require corrections to align the data with other global thermochemical networks, which are not always clearly documented or explained. In this review, we introduce a set of consistent terminology and definitions, review existing approaches, and unify the techniques using the framework of linear algebra. This set of terminology and tools facilitates the correction and alignment of energies between different data formats and sources, promoting the sharing and reuse of ab initio data. Standardization of thermochemistry concepts in computational heterogeneous catalysis reduces computational cost and enhances fundamental understanding of catalytic processes, which will accelerate the computational design of optimally performing catalysts.

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

Conservative Release Fraction Correlation for Spray Releases

Washington River Protection Solutions, LLC (WRPS) is planning to conduct repairs in the Liquid Effluent Retention Facility (LERF), and prior to conducting this work they will re-evaluate the hazard category designation for LERF. As part of this evaluation, WRPS needs estimates of the release fraction of potential accidental sprays, because a spray release is a potential bounding accident. Pacific Northwest National Laboratory previously developed a conservative correlation for the cumulative generation rate (volume per time) of aerosol droplets less than or equal to specific sizes from spray releases as a function of the spray pressure, orifice area, and droplet diameter. To obtain release fraction estimates from the conservative generation rate correlation, the correlation needs to be recast by dividing the aerosol generation rate by the flow rate of a spray. In the current study, a conservative release fraction correlation was developed from the conservative aerosol generation rate correlation developed previously by dividing the generation rate correlation by the flow rate of a spray using an orifice flow rate equation and then this correlation was adjusted to be conservative. Test data were also extrapolated to a lower pressure than the lowest pressure tested (100 psig) for the spray data used for developing the previous conservative generation rate correlation. The new conservative release fraction correlation was then used to predict release fractions of respirable droplets (droplets = 10 µm) for four specific pipes that are part of LERF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Precursor Analysis Report: SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003

The SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003 Precursor Analysis Report leverages publicly available information about Davis-Besse’s 2003 cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. On 25 January 2003, the SQL Slammer worm infected more than 90% of vulnerable hosts and crashed the internet in 10 to 15 minutes, making it one of the fastest spreading worms in history. SQL Slammer is a fileless, memory-resident worm that remotely exploits a stack-based buffer overflow vulnerability on local hosts to intensively scan and rapidly self-propagate across the internet. The worm infected approximately 300,000 unpatched hosts running Microsoft Structured Query Language (SQL) Server 2000 or Microsoft Desktop Engine (MSDE) 2000 with SQL Server Resolution Service. The SQL Slammer worm indirectly infected FirstEnergy’s Davis-Besse nuclear power plant by first infecting a consultant’s company network server and then propagating through an external misconfigured connection into Davis-Besse’s site network. The infection caused major network congestion, slow performance, data overloads, and the inability of local hosts to communicate with each other, which eventually caused a loss of availability and a loss of view when the Safety Parameter Display System (SPDS) and Plant Process Computer (PPC) crashed. At the time of the infection, the plant was already offline, the digital monitoring systems had redundant analog backups, and the plant control and safety functions were not affected, so there were no concerns of a safety breach. However, this incident resulted in many lessons learned and spawned important discussions about cybersecurity’s role in nuclear safety and electric power reliability regulation, policy, and guidance. Researchers and analysts identified 10 unique techniques utilized during the attack with a total of 640 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Eight of the identified techniques used during Davis-Besse cyber attack were precursors to the triggering event. Analysis identified 596 observables associated with these precursor techniques, 428 of which were assessed to have an increased likelihood of being perceived in the 331 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Plant Nutrition Influences Resistant Maize Defense Responses to the Fall Armyworm ( Spodoptera frugiperda )

Plants are often confronted by different groups of herbivores, which threaten their growth and reproduction. However, they are capable of mounting defenses against would-be attackers which may be heightened upon attack. Resistance to insects often varies among plant species, with different genotypes exhibiting unique patterns of chemical and physical defenses. Within this framework, plant access to nutrients may be critical for maximal functioning of resistance mechanisms and are likely to differ among plant genotypes. In this study, we aimed to test the hypothesis that access to nutrition would alter the expression of plant resistance to insects and alter insect performance in a manner consistent with fertilization regime. We used two maize (Zea mays) genotypes possessing different levels of resistance and the fall armyworm (Spodoptera frugiperda) as model systems. Plants were subjected to three fertilization regimes prior to assessing insect-mediated responses. Upon reaching V4 stage, maize plants were separated into two groups, one of which was infested with fall armyworm larvae to induce plant defenses. Plant tissue was collected and used in insect bioassays and to measure the expression of defense-related genes and proteins. Insect performance differed between the two plant genotypes substantially. For each genotype, fertilization altered larval performance, where lower fertilization rates hindered larval growth. Induction of plant defenses by prior herbivory substantially reduced naïve fall armyworm growth in both genotypes. The effects between fertilization and induced defenses were complex, with low fertilization reducing induced defenses in the resistant maize. Gene and protein expression patterns differed between the genotypes, with herbivory often increasing expression, but differing between fertilization levels. The soluble protein concentrations did not change across fertilization levels but was higher in the susceptible maize genotype. These results demonstrate the malleability of plant defenses and the cascading effects of plant nutrition on insect herbivory.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Silicon nitride stress-optic microresonator modulator for optical control applications

Modulation-based control and locking of lasers, filters and other photonic components is a ubiquitous function across many applications that span the visible to infrared (IR), including atomic, molecular and optical (AMO), quantum sciences, fiber communications, metrology, and microwave photonics. Today, modulators used to realize these control functions consist of high-power bulk-optic components for tuning, sideband modulation, and phase and frequency shifting, while providing low optical insertion loss and operation from DC to 10s of MHz. In order to reduce the size, weight and cost of these applications and improve their scalability and reliability, modulation control functions need to be implemented in a low loss, wafer-scale CMOS-compatible photonic integration platform. The silicon nitride integration platform has been successful at realizing extremely low waveguide losses across the visible to infrared and components including high performance lasers, filters, resonators, stabilization cavities, and optical frequency combs. Yet, progress towards implementing low loss, low power modulators in the silicon nitride platform, while maintaining wafer-scale process compatibility has been limited. Here we report a significant advance in integration of a piezo-electric (PZT, lead zirconate titanate) actuated micro-ring modulation in a fully-planar, wafer-scale silicon nitride platform, that maintains low optical loss (0.03 dB/cm in a 625 µm resonator) at 1550 nm, with an order of magnitude increase in bandwidth (DC - 15 MHz 3-dB and DC - 25 MHz 6-dB) and order of magnitude lower power consumption of 20 nW improvement over prior PZT modulators. The modulator provides a >14 dB extinction ratio (ER) and 7.1 million quality-factor (Q) over the entire 4 GHz tuning range, a tuning efficiency of 162 MHz/V, and delivers the linearity required for control applications with 65.1 dB·Hz 2/3 and 73.8 dB·Hz 2/3 third-order intermodulation distortion (IMD3) spurious free dynamic range (SFDR) at 1 MHz and 10 MHz respectively. We demonstrate two control applications, laser stabilization in a Pound-Drever Hall (PDH) lock loop, reducing laser frequency noise by 40 dB, and as a laser carrier tracking filter. This PZT modulator design can be extended to the visible in the ultra-low loss silicon nitride platform with minor waveguide design changes. This integration of PZT modulation in the ultra-low loss silicon nitride waveguide platform enables modulator control functions in a wide range of visible to IR applications such as atomic and molecular transition locking for cooling, trapping and probing, controllable optical frequency combs, low-power external cavity tunable lasers, quantum computers, sensors and communications, atomic clocks, and tunable ultra-low linewidth lasers and ultra-low phase noise microwave synthesizers.

Wang, Jiawei (ORCID:0000000257965220)↗
DOE OSTI2020

Enhanced multi-carbon alcohol electroproduction from CO via modulated hydrogen adsorption

Multi-carbon alcohols such as ethanol are valued as fuels in view of their high energy density and ready transport. Unfortunately, the selectivity toward alcohols in CO 2 /CO electroreduction is diminished by ethylene production, especially when operating at high current densities (>100 mA cm –2 ). Here we report a metal doping approach to tune the adsorption of hydrogen at the copper surface and thereby promote alcohol production. Using density functional theory calculations, we screen a suite of transition metal dopants and find that incorporating Pd in Cu moderates hydrogen adsorption and assists the hydrogenation of C 2 intermediates, providing a means to favour alcohol production and suppress ethylene. We synthesize a Pd-doped Cu catalyst that achieves a Faradaic efficiency of 40% toward alcohols and a partial current density of 277 mA cm –2 from CO electroreduction. The activity exceeds that of prior reports by a factor of 2.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Constraining maximum event magnitude during injection-triggered seismicity

Abstract Understanding mechanisms controlling fluid injection-triggered seismicity is key in defining strategies to ameliorate it. Recent triggered events (e.g. Pohang, Mw 5.5) have exceeded predictions of average energy release by a factor of >1000x, necessitating robust methodologies to both define critical antecedent conditions and to thereby constrain anticipated event size. We define maximum event magnitudes resulting from triggering as a function of pre-existing critical stresses and fluid injection volume. Fluid injection experiments on prestressed laboratory faults confirm these estimates of triggered moment magnitudes for varied boundary conditions and injection rates. In addition, observed ratios of shear slip to dilation rates on individual faults signal triggering and may serve as a measurable proxy for impending rupture. This new framework provides a robust method of constraining maximum event size for preloaded faults and unifies prior laboratory and field observations that span sixteen decades in injection volume and four decades in length scale.

58 GEOSCIENCES↗
DOE OSTI2024

Early-stage oxidation and subsequent damage of the used nuclear fuel extractant TODGA; electron pulse radiolysis and theoretical insights

Radiation induced damage of extractant molecules is a well-known phenomenon responsible for reducing efficiency and increasing the waste and cost of reprocessing used nuclear fuel (UNF). As such, understanding early-stage (pico- to nanoseconds) radiation-induced reaction mechanisms is essential for informing the design of next generation extractants with enhanced radiation robustness. Here, in this work, we utilized picosecond and nanosecond electron pulse radiolysis experiments to probe the early-stage radioactive environment experienced by the organic phase extractant N,N,N',N'-tetraoctyldiglycolamide (TODGA), proposed for separating highly radioactive trivalent minor actinides (specifically americium and curium) from the trivalent lanthanides. Using comparisons to the similar ionization potential (IP) solute p-xylene, this work determined the mechanism of reaction with the ionized diluent (i.e., n-dodecane radical cation, DD˙ + ) is hole transfer to produce TODGA˙ + . At high TODGA concentrations (>100 mM), the majority of this transfer occurs faster than 10 ps via the capture of DD˙ + holes prior to their solvation with a C 37 = 300 mM. The surviving solvated holes were captured with k = (2.38 ± 0.15) × 10 10 M -1 s -1 . Attempts at subsequent hole transfer to lower IP solutes found that only 10% of holes were transferred, indicating bond rupture of TODGA˙ + occurs within 2.6 ns at 200 mM TODGA. Possible reaction pathways for the rapid decomposition of TODGA˙ + were explored using a combination of experiments and density functional theory (DFT) calculations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

GPU acceleration of rank-reduced coupled-cluster singles and doubles

Here, we have developed a graphical processing unit (GPU) accelerated implementation of our recently introduced rank-reduced coupled-cluster singles and doubles (RR-CCSD) method. RR-CCSD introduces a low-rank approximation of the doubles amplitudes. This is combined with a low-rank approximation of the electron repulsion integrals via Cholesky decomposition. The result of these two low-rank approximations is the replacement of the usual fourth-order CCSD tensors with products of second- and third-order tensors. In our implementation, only a single fourth-order tensor must be constructed as an intermediate during the solution of the amplitude equations. Owing in large part to the compression of the doubles amplitudes, the GPU-accelerated implementation shows excellent parallel efficiency (95% on eight GPUs). Our implementation can solve the RR-CCSD equations for up to 400 electrons and 1550 basis functions—roughly 50% larger than the largest canonical CCSD computations that have been performed on any hardware. In addition to increased scalability, the RR-CCSD computations are faster than the corresponding CCSD computations for all but the smallest molecules. We test the accuracy of RR-CCSD for a variety of chemical systems including up to 1000 basis functions and determine that accuracy to better than 0.1% error in the correlation energy can be achieved with roughly 95% compression of the ov space for the largest systems considered. We also demonstrate that conformational energies can be predicted to be within 0.1 kcal mol -1 with efficient compression applied to the wavefunction. Finally, we find that low-rank approximations of the CCSD doubles amplitudes used in the similarity transformation of the Hamiltonian prior to a conventional equation-of-motion CCSD computation will not introduce significant errors (on the order of a few hundredths of an electronvolt) into the resulting excitation energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗