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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Load Flexibility Analysis of Residential HVAC and Water Heating and Commercial Refrigeration

The emergence of connected devices and the Internet of Things has opened the door to the concept of widespread use of building equipment for load-side flexibility. This stems from the increased use of distributed and clean energy resources for electric generation and the growing demand for a more resilient and reliable grid. A significant challenge associated with the use of building equipment as a distributed energy resource is quantifying the magnitude of power or energy flexibility. Unlike batteries that have well-defined and relatively constant capacity and maximum charge/discharge rates, building equipment often has characteristics that vary based on external factors (e.g., weather or occupant behavior). This makes quantifying the availability and flexibility of these resources difficult. Controlling a fleet of equipment while minimizing any adverse impacts to the occupants (e.g., discomfort or food spoilage) is a nontrivial matter. Although there are many different control methodologies available, this study focuses on two: priority-based control (PBC) and model-predictive control (MPC). These were selected because they represent two ends of the control spectrum. PBC is a relatively simple control strategy that requires only the current state of the devices to be controlled and an estimate of how much power each device consumes when turned on. MPC is much more complex and requires a model of the system and forecasts of any disturbances that influence the system. Because MPC has a predictive element, it can prepare for a desired response before the event and therefore is expected to provide a more optimal control relative to PBC. Both MPC and PBC are compared with a baseline simulation that uses feedback control typical of the thermostatically controlled devices being evaluated. This report investigates the load flexibility of three building systems: residential air conditioners, residential water heaters, and commercial refrigeration. Air heating and cooling and water heating account for 45% of residential electricity use, making them good candidates for providing load flexibility. Refrigeration in commercial buildings accounts for 16% of commercial building electricity use, making it another good candidate. Simplified models of each system were generated and validated based on measured data from actual equipment. These models were used to simulate the thermal and power response of the systems using PBC and MPC for a fleet of devices trying to achieve a variety of different responses, including hourly load shed, hourly load up, and daily peak demand reduction. Significant findings from each piece of equipment follow.

42 ENGINEERING↗
DOE OSTI2025

Development and Validation of MALAMUTE model for Electric Field Assisted Sintering of Structural Materials

Fusion power plant designs feature extreme material performance requirements for structural material candidates. In addition to conventional alloys, more advanced composites and oxide dispersion strengthened (ODS) alloys are being explored, however, achieving the desired microstructures to maximize performance using traditional manufacturing methods can be challenging. The advanced manufacturing (AM) electric field-assisted sintering (EFAS) technique offers improved control over the final microstructure through higher heating and cooling rates and moderate pressures. Modeling and simulation tools show promise in elucidating the process-structure-property-performance (PSPP) correlation for AM-produced parts, including the EFAS process. An inherently multiscale process, the EFAS technique aligns well with the multiscale modeling capability of the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE)[cite]. We present here an electro-thermo-mechanical approach to modeling the EFAS process using the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) code. Prediction of the field and gradient distributions across the EFAS tooling is required to accurately describe the conditions for the lower-scale microstructural evolution models. In this work we present the MALAMUTE model developed to predict the electrical potential, temperature, and mechanical stress distribution across the EFAS graphite tooling and part at the larger engineering-scale. Validation of the MALAMUTE engineering-scale model is completed using data from experimental densification and pre-densified runs of iron powder via EFAS at 1000oC. These runs were conducted using a Thermal Technology DCS-5 EFAS system. Data collected during the experiment runs include the direct current (DC) supplied to the graphite tooling, the temperature of the graphite tooling as measured with a pyrometer, and the force applied to the top of the graphite tooling stack, and the data were recorded every 10 seconds. Our validation approach used the current and force data from the EFAS run as boundary condition inputs to the MAMALUTE simulation; the temperature data were used to evaluate the MALAMUTE EFAS model prediction. Results of the MALAMUTE simulations are employed to connect the external pyrometer temperature measurement to the temperature profile across the part undergoing consolidation. We investigate the impact of material property variation and mesh deformation on the temperature profile as predicted by MALAMUTE. We conclude by highlighting projects where the MALAMUTE EFAS modeling and simulation capabilities will be used to assist experimental design.

36 - MATERIALS SCIENCE↗
DOE OSTI2011

IMPROVED MODELING OF THE MASS DISTRIBUTION OF DISK GALAXIES BY THE EINASTO HALO MODEL

Analysis of the rotation curves (RCs) of spiral galaxies provides an efficient diagnostic for studying the properties of dark matter halos and their relations with baryonic material. Since the cored pseudo-isothermal (Iso) model usually provides a better description of observed RCs than does the cuspy Navarro-Frenk-White (NFW) model, there have been concerns that the {Lambda}CDM primordial density fluctuation spectrum may not be the correct one. We have modeled the RCs of galaxies from The H I Nearby Galaxy Survey (THINGS) with the Einasto halo model, which has emerged as the best-fitting model of the halos arising in dissipationless cosmological N-body simulations. We find that the RCs are significantly better fit with the Einasto halo than with either Iso or NFW halo models. In our best-fit Einasto models, the radius of density slope -2 and the density at this radius are highly correlated. The Einasto index, which controls the overall shape of the density profile, is near unity on average for intermediate and low mass halos. This is not in agreement with the predictions from {Lambda}CDM simulations. The indices of the most massive halos are in rough agreement with those cosmological simulations and appear correlated with the halo virial mass. We find that a typical Einasto density profile declines more strongly in its outermost parts than any of the Iso or NFW models whereas it is relatively shallow in its innermost regions. The core nature of those regions of halos thus extends the cusp-core controversy found for the NFW model with low surface density galaxies to the Einasto halo with more massive galaxies like those of THINGS. The Einasto concentrations decrease as a function of halo mass, in agreement with trends seen in numerical simulations. However, they are generally smaller than values expected for simulated Einasto halos. We thus find that, so far, the Einasto halo model provides the best match to the observed RCs and can therefore be considered as a new standard model for dark matter halos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Better together: Elements of successful scientific software development in a distributed collaborative community

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2021

Network Based Estimation of Wind Farm Power and Velocity Data Under Changing Wind Direction

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

distributed↗
DOE OSTI2023

Understanding hydrogen pressure control of furfural hydrogenation selectivity on a Pd(1 1 1) model catalyst

We investigate the postulate that hydrogen pressure influences the furfural hydrogenation selectivity on palladium to form decarbonylation products at low hydrogen pressures, but 2-methyl furan at higher pressures. This change is caused by co-adsorbed hydrogen changing the orientation of adsorbed furfural; low hydrogen coverages favor flat-lying furfural, higher coverages form tilted species. This was tested for hydrogenation of furfural on a Pd(1 1 1) single crystal. The product distribution agreed with the postulate, with 100% selectivity to furan and tetrahydrofuran for low hydrogen pressures, with 2-methyl furan increasing with hydrogen pressure. The origin of the effect is explored using reflection–absorption infrared spectroscopy. This showed that co-adsorbed hydrogen influenced the orientation in a way predicted by theory. It was also found that other co-adsorbates displayed similar effects implying that a range of molecules in the reaction mixture could have significant effects on the furfural selectivity.

09 BIOMASS FUELS↗
DOE OSTI2017

The Momentum Distribution of Liquid ⁴He

We report a high-resolution neutron Compton scattering study of liquid ⁴He under milli-Kelvin temperature control. To interpret the scattering data, we performed Quantum Monte Carlo calculations of the atomic momentum distribution and final state effects for the conditions of temperature and density considered in the experiment. There is excellent agreement between the observed scattering and ab initio calculations of its lineshape at all temperatures. We also used model fit functions to obtain from the scattering data empirical estimates of the average atomic kinetic energy and Bose condensate fraction. These quantities are also in excellent agreement with ab initio calculations. We conclude that contemporary Quantum Monte Carlo methods can furnish accurate predictions for the properties of Bose liquids, including the condensate fraction, close to the superfluid transition temperature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Neutron-neutron angular correlations in spontaneous fission of Cf 252 and Pu 240

Background: Angular anisotropy has been observed between prompt neutrons emitted during the fission process. Such an anisotropy arises because the emitted neutrons are boosted along the direction of the parent fragment. Purpose: To measure the neutron-neutron angular correlations from the spontaneous fission of 252 Cf and 240 Pu oxide samples using a liquid scintillator array capable of pulse-shape discrimination. To compare these correlations to simulations combining the Monte Carlo radiation transport code MCNPX with the fission event generator FREYA. Method: Two different analysis methods were used to study the neutron-neutron correlations with varying energy thresholds. The first is based on setting a light output threshold while the second imposes a time-of-flight cutoff. The second method has the advantage of being truly detector independent. Results: The neutron-neutron correlation modeled by FREYA depends strongly on the sharing of the excitation energy between the two fragments. The measured asymmetry enabled us to adjust the FREYA parameter x in 240 Pu , which controls the energy partition between the fragments and is so far inaccessible in other measurements. The 240 Pu data in this analysis was the first available to quantify the energy partition for this isotope. The agreement between data and simulation is overall very good for 252 Cf ( sf ) and 240 Pu ( sf ). Conclusions: The asymmetry in the measured neutron-neutron angular distributions can be predicted by FREYA. The shape of the correlation function depends on how the excitation energy is partitioned between the two fission fragments. Lastly, experimental data suggest that the lighter fragment is disproportionately excited.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism

Process monitoring (PM) for nuclear safeguards sometimes requires estimation of thresholds corresponding to small false alarm rates. Threshold estimation dates to the 1920s with the Shewhart control chart; however, because possible new roles for PM are being evaluated in nuclear safeguards, it is timely to consider modern model selection options in the context of threshold estimation. One of the possible new PM roles involves PM residuals, where a residual is defined as residual = data − prediction. This paper reviews alarm threshold estimation, introduces model selection options, and considers a range of assumptions regarding the data-generating mechanism for PM residuals. Two PM examples from nuclear safeguards are included to motivate the need for alarm threshold estimation. The first example involves mixtures of probability distributions that arise in solution monitoring, which is a common type of PM. The second example involves periodic partial cleanout of in-process inventory, leading to challenging structure in the time series of PM residuals.

Burr, Tom↗
DOE OSTI2013

Report on the Implementation of Homogeneous Nucleation Scheme in MARMOT-based Phase Field Simulation

In this report, we summarized our effort in developing mesoscale phase field models for predicting precipitation kinetics in alloys during thermal aging and/or under irradiation in nuclear reactors. The first part focused on developing a method to predict the thermodynamic properties of critical nuclei such as the sizes and concentration profiles of critical nuclei, and nucleation barrier. These properties are crucial for quantitative simulations of precipitate evolution kinetics with phase field models. Fe-Cr alloy was chosen as a model alloy because it has valid thermodynamic and kinetic data as well as it is an important structural material in nuclear reactors. A constrained shrinking dimer dynamics (CSDD) method was developed to search for the energy minimum path during nucleation. With the method we are able to predict the concentration profiles of the critical nuclei of Cr-rich precipitates and nucleation energy barriers. Simulations showed that Cr concentration distribution in the critical nucleus strongly depends on the overall Cr concentration as well as temperature. The Cr concentration inside the critical nucleus is much smaller than the equilibrium concentration calculated by the equilibrium phase diagram. This implies that a non-classical nucleation theory should be used to deal with the nucleation of Cr precipitates in Fe-Cr alloys. The growth kinetics of both classical and non-classical nuclei was investigated by the phase field approach. A number of interesting phenomena were observed from the simulations: 1) a critical classical nucleus first shrinks toward its non-classical nucleus and then grows; 2) a non-classical nucleus has much slower growth kinetics at its earlier growth stage compared to the diffusion-controlled growth kinetics. 3) a critical classical nucleus grows faster at the earlier growth stage than the non-classical nucleus. All of these results demonstrated that it is critical to introduce the correct critical nuclei into phase field modeling in order to correctly capture the kinetics of precipitation. In most alloys the matrix phase and precipitate phase have different concentrations as well as different crystal structures. For example, Cu precipitates in FeCu alloys have fcc crystal structure while the matrix Fe-Cu solid solution has bcc structure at low temperature. The WBM model and KimS model, where both concentrations and order parameters are chosen to describe the microstructures, are commonly used to model precipitations in such alloys. The WBM and KimS models have not been implemented into Marmot yet. In the second part of this report, we focused on implementing the WBM and KimS models into Marmot. The Fe-Cu alloys, which are important structure materials in nuclear reactors, was taken as the model alloys to test the models.

Fe Alloys↗
DOE OSTI2017

Atomistic modeling of nanoparticle generation in short pulse laser ablation of thin metal films in water

Laser ablation in liquids is actively used for generation of clean colloidal nanoparticles with unique shapes and functionalities. The fundamental mechanisms of the laser ablation in liquids and the key processes that control the nanoparticle structure, composition, and size distribution, however, are not yet fully understood. In this paper, we report the results of first atomistic simulations of laser ablation of metal targets in liquid environment. A model combining a coarse-grained representation of the liquid environment (parameterized for water), a fully atomistic description of laser interactions with metal targets, and acoustic impedance matching boundary conditions is developed and applied for simulation of laser ablation of a thin silver film deposited on a silica substrate. The simulations, performed at two laser fluences in the regime of phase explosion, predict a rapid deceleration of the ejected ablation plume and the formation of a dense superheated molten layer at the water-plume interface. The water in contact with the hot metal layer is brought to the supercritical state and transforms into an expanding low density metal-water mixing region that serves as a precursor for the formation of a cavitation bubble. Two distinct mechanisms of the nanoparticle formation are predicted in the simulations: (1) the nucleation and growth of small (mostly ≤10nm) nanoparticles in the metal-water mixing region and (2) the formation of larger (tens of nm) nanoparticles through the breakup of the superheated molten metal layer triggered by the emergence of complex morphological features attributed to the Rayleigh-Taylor instability of the interface between at the superheated metal layer and the supercritical water. The first mechanism is facilitated by the rapid cooling of the growing nanoparticles in the supercritical water environment, resulting in solidification of the nanoparticles located in the upper part of the mixing region on the timescale of nanoseconds. The computational prediction of the two mechanisms of nanoparticle formation yielding nanoparticles with different characteristic sizes is consistent with experimental observations of two distinct nanoparticle populations appearing at different stages of the ablation process.

Chemistry↗
DOE OSTI2024

Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

Abstract Streambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence (AI)‐enabled, and theory‐based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground‐truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log‐normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy‐Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined.

58 GEOSCIENCES↗
DOE OSTI2016

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior in real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.

14 SOLAR ENERGY↗
DOE OSTI2013

Characterizing particle-scale equilibrium adsorption and kinetics of uranium(VI) desorption from U-contaminated sediments

Rates of contaminant U(VI) release from individual size fractions of a composite sediment from the seasonally saturated lower vadose zone of the Hanford 300-Area were examined in flow-through batch reactors to maintain quasi-constant chemical conditions. Variability in equilibrium adsorption among the various size fractions was determined in static batch reactors and analyzed using the surface complexation modeling approach. The estimated stoichiometric coefficients of U(VI) surface complexation reactions with respect to pH and carbonate concentrations varied with size fractions. This source of variability significantly increased the uncertainty in U(VI) conditional equilibrium constants over that estimated from experimental errors alone. A minimum difference between conditional equilibrium constants was established in order to evaluate statistically significant differences between sediment adsorption properties. A set of equilibrium and kinetic expressions for cation exchange, calcite dissolution, aerobic respiration, and silica dissolution were incorporated in a reaction-rate model to describe the temporal evolution of solute concentrations observed during the flow-through batch experiments. Parameters in the reaction-rate model, calibrated using experimental data for select size fractions, predicted the changes in solute concentrations for the bulk, <2 mm, sediment sample. Kinetic U(VI) desorption was well described using a multi-rate surface complexation model with an assumed lognormal distribution for the rate constants. The estimated mean and standard deviation were the same for all < 2mm size fractions, but differed in the 2-8mm size fraction. Micropore volumes in the varied size fractions were also similar as assessed using t-plots to analyze N2 desorption data. These findings provide further support for the link between microporosity and particle-scale mass transfer rates controlling kinetic U(VI) adsorption/desorption and for the utility of N2 desorption isotherms for characterizing pore networks that influence mass transfer rates.

Stoliker, Deborah L.↗
DOE OSTI2012

Measurement of the single-top-quark t-channel cross section in pp collisions at $ \sqrt{s}=7 $ TeV

A measurement of the single-top-quark t-channel production cross section in pp collisions at sqrt(s) = 7 TeV with the CMS detector at the LHC is presented. Two different and complementary approaches have been followed. The first approach exploits the distributions of the pseudorapidity of the recoil jet and reconstructed top-quark mass using background estimates determined from control samples in data. The second approach is based on multivariate analysis techniques that probe the compatibility of the candidate events with the signal. Data have been collected for the muon and electron final states, corresponding to integrated luminosities of 1.17 and 1.56 inverse femtobarns, respectively. The single-top-quark production cross section in the t-channel is measured to be 67.2 +/- 6.1 pb, in agreement with the approximate next-to-next-to-leading-order standard model prediction. Using the standard model electroweak couplings, the CKM matrix element abs(V[tb]]) is measured to be 1.020 +/- 0.046 (meas.) +/- 0.017 (theor.).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Shape Control of Colloidal Semiconductor Nanocrystals through Thermodynamically Driven Aggregative Growth

The optoelectronic properties of colloidal semiconductor nanocrystals (NCs) can be manipulated by changing their geometric shapes. The precise synthetic control over particle morphologies, however, has remained elusive. Conventional growth techniques rely on the kinetic assembly of atomic units, where supersaturation and precipitation processes can lead to a broad distribution of particle shapes. In this paper, we demonstrate that replacing atomic precursors with small-size nanocrystals as building blocks for larger colloids offers an easier, more predictive control over nanoparticle shape evolution. The reported growth strategy is illustrated via shape-selective syntheses of CdSe and CdS NC cubes, spheres, rods, as well as unprecedented “donut” and ring-like structures. Growth of desired particle morphologies was achieved by choosing an appropriate reaction solvent that minimizes the surface free energy through a unique shape formation. This so-called "aggregative growth mechanism" is explained using a thermodynamic model for interacting viscous colloids. We expect that this novel growth strategy will offer a viable platform for shape–selective synthesis of many inorganic colloids, providing pathways to optimizable optoelectronic materials.

36 MATERIALS SCIENCE↗