Prediction and Characterization of MXene Nanosheet Anodes for Non-Lithium-Ion Batteries
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catalysis (heterogeneous)↗
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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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Prediction of intrinsic topological superconductivity in Mn-doped GeTe monolayer from first-principles
The recent discovery of topological superconductors (TSCs) has sparked enormous interest. The realization of TSC requires a delicate tuning of multiple microscopic parameters, which remains a great challenge. Here, we develop a first-principles approach to quantify realistic conditions of TSC by solving self-consistently Bogoliubov-de Gennes equation based on a Wannier function construction of band structure, in presence of Rashba spin-orbit coupling, Zeeman splitting and electron-phonon coupling. We further demonstrate the power of this method by predicting the Mn-doped GeTe (Ge 1- x Mn x Te) monolayer—a well-known dilute magnetic semiconductor showing superconductivity under hole doping—to be a Class D TSC with Chern number of -1 and chiral Majorana edge modes. By constructing a first-principles phase diagram in the parameter space of temperature and Mn concentration, we propose the TSC phase can be induced at a lower-limit transition temperature of ~40 mK and the Mn concentration of x ~0.015%. Our approach can be generally applied to TSCs with a phonon-mediated pairing, providing useful guidance for future experiments.
Chemistry↗
Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions
Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.
Huntzinger, D. N.↗
Absorption Spectroscopy for Quantitative Prediction of Lanthanide Concentrations in the 3LiCl – 2CsCl Eutectic at 723 K
This paper will be submitted to the ACS journal, Analytical Chemistry. Abstract. The absorption spectra for single component mixtures of erbium(III) chloride, holmium(III) chloride, neodymium(III) chloride, praseodymium(III) chloride, samarium(III) chloride and thulium(III) chloride were obtained in 3 LiCl – 2 CsCl at 723 K. The absorption spectra were collected at various concentrations from 0 mM to approximately 200 mM (above which the solutions became saturated). Using this data and Beer’s law, molar absorptivities for the absorbance peaks for each lanthanide(III) chloride were obtained. Using the collection of single component spectra, multivariate regression models were created for each lanthanide metal studied. These models were validated against a new set of absorption spectra containing multicomponent mixtures of the selected lanthanides in the molten salt solution. Results of the regression models and predictions on the validation set of data are presented.
Absorption spectroscopy, molten salt, process moni↗
A generalized van der Waals model for light gas adsorption prediction in IRMOFs
A generalized van der Waals model for predicting the adsorption isotherms in IRMOFs by defining different attractive regions.
Kong, Lingli↗
Design and synthesis of two-dimensional covalent organic frameworks with four-arm cores: prediction of remarkable ambipolar charge-transport properties
Theoretical and experimental investigations of diacetylene-linked COFs with remarkable charge-transport properties.
Thomas, Simil↗
Improving nuclear data evaluations with predictive reaction theory and indirect measurements
Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirect approaches, such as the surrogate reaction method, have been developed. Nuclear theory is key to extract reliable cross sections from such indirect measurements. We describe ongoing efforts to expand the theoretical capabilities that enable surrogate reaction measurements. We focus on microscopic predictions for charged-particle inelastic scattering, uncertainty-quantified optical nucleon-nucleus models, and neural-network enhanced parameter inference.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Communication: Theoretical prediction of free-energy landscapes for complex self-assembly
We present a technique for calculating free-energy profiles for the nucleation of multicomponent structures that contain as many species as building blocks. We find that a key factor is the topology of the graph describing the connectivity of the target assembly. By considering the designed interactions separately from weaker, incidental interactions, our approach yields predictions for the equilibrium yield and nucleation barriers. These predictions are in good agreement with corresponding Monte Carlo simulations. We show that a few fundamental properties of the connectivity graph determine the most prominent features of the assembly thermodynamics. Surprisingly, we find that polydispersity in the strengths of the designed interactions stabilizes intermediate structures and can be used to sculpt the free-energy landscape for self-assembly. Finally, we demonstrate that weak incidental interactions can preclude assembly at equilibrium due to the combinatorial possibilities for incorrect association.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Predicting a quaternary tungsten oxide for sustainable photovoltaic application by density functional theory
A quaternary oxide, CuSnW{sub 2}O{sub 8} (CTTO), has been predicted by density functional theory (DFT) to be a suitable material for sustainable photovoltaic applications. CTTO possesses band gaps of 1.25 eV (indirect) and 1.37 eV (direct), which were evaluated using the hybrid functional (HSE06) as a post-DFT method. The hole mobility of CTTO was higher than that of silicon. Further, optical absorption calculations demonstrate that CTTO is a better absorber of sunlight than Cu{sub 2}ZnSnS{sub 4} and CuIn{sub x}Ga{sub 1−x}Se{sub 2} (x = 0.5). In addition, CTTO exhibits rigorous thermodynamic stability comparable to WO{sub 3}, as investigated by different thermodynamic approaches such as bonding cohesion, fragmentation tendency, and chemical potential analysis. Chemical potential analysis further revealed that CTTO can be synthesized at flexible experimental growth conditions, although the co-existence of at least one secondary phase is likely. Finally, like other Cu-based compounds, the formation of Cu vacancies is highly probable, even at Cu-rich growth condition, which could introduce p-type activity in CTTO.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries
This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.
49 EE - Wind and Water Power Program - Wind (EE-4W↗
A 3.55 keV line from DM →a→γ: predictions for cool-core and non-cool-core clusters
We further study a scenario in which a 3.55 keV X-ray line arises from decay of dark matter to an axion-like particle (ALP), that subsequently converts to a photon in astrophysical magnetic fields. We perform numerical simulations of Gaussian random magnetic fields with radial scaling of the magnetic field magnitude with the electron density, for both cool-core 'Perseus' and non-cool-core 'Coma' electron density profiles. Using these, we quantitatively study the resulting signal strength and morphology for cool-core and non-cool-core clusters. Our study includes the effects of fields of view that cover only the central part of the cluster, the effects of offset pointings on the radial decline of signal strength and the effects of dividing clusters into annuli. We find good agreement with current data and make predictions for future analyses and observations.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Prediction of divertor heat flux width for ITER using BOUT++ transport and turbulence module
Investigation of turbulent transport dynamics in scrape-off-layer (SOL) and divertor heat flux width prediction is performed for ITER. Both BOUT++ transport and BOUT++ turbulence codes are applied to capture the physics on different temporal scales. Simulations start with an ITER 15MA baseline scenario profile generated by CORSICA (Kim et al 2015 Paper ITER_D_R9T8J9 v1.1). In BOUT++ transport code, the plasma parameters ( n i , T i , T e ) and radial electric () profiles are evolved to steady state. Here, the initial plasma profiles inside the separatrix are taken from CORSICA scenario studies. Transport coefficients are calculated by inverting the plasma profiles inside the separatrix.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint
Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.
25 ENERGY STORAGE↗
Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study
This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the model predictive control (MPC) algorithm based on the virtual phase-link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems, which included sensing, processing, and actuating components, to enable field deployment. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days, including the steps taken, the real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment by looking at the changes in delay and energy consumption.
deployment↗
Precise predictions for $V+$jets dark matter backgrounds
High-energy jets recoiling against missing transverse energy (MET) are powerful probes of dark matter at the LHC. Searches based on large MET signatures require a precise control of the $Z(\nu\bar\nu)+$jet background in the signal region. This can be achieved by taking accurate data in control regions dominated by $Z(\ell^+\ell^-)+$jet, $W(\ell\nu)+$jet and $\gamma+$jet production, and extrapolating to the $Z(\nu\bar\nu)+$jet background by means of precise theoretical predictions. In this context, recent advances in perturbative calculations open the door to significant sensitivity improvements in dark matter searches. In this spirit, we present a combination of state-of-the art calculations for all relevant $V+$jets processes, including throughout NNLO QCD corrections and NLO electroweak corrections supplemented by Sudakov logarithms at two loops. Predictions at parton level are provided together with detailed recommendations for their usage in experimental analyses based on the reweighting of Monte Carlo samples. Particular attention is devoted to the estimate of theoretical uncertainties in the framework of dark matter searches, where subtle aspects such as correlations across different $V+$jet processes play a key role. In conclusion, the anticipated theoretical uncertainty in the $Z(\nu\bar\nu)+$jet background is at the few percent level up to the TeV range.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
C3PU:Real-Time Predictive Charge Control Software for Battery Management Systems (Code for Charge Control and Predictive Unit) [SWR-21-48]
C3PU sets up a model predictive control formulation for the optimal charging control of battery packs and can be deployed for real-time operation on an embedded microprocessor. C3PU uses electrochemical and thermal models of battery packs to predict the charging trajectories of the battery pack over a time horizon under different operating conditions. It then selects the optimal charging trajectory such that a pre-defined objective is minimized, which in the present state of the code is to minimize the charging duration of the battery pack. The optimal charging trajectory is obtained by solving an underlying mathematical problem. However, C3PU is flexible to incorporate other charging objectives. The underlying battery models can be swapped as well, as long as they follow certain mathematical properties. Mathematically, optimal control problems (in this case, optimally controlling the charging current of the battery pack) are computationally expensive. C3PU implements advanced numerical techniques, namely pseudo-spectral optimization, to reduce the computational burden of the underlying problem to solve. This allows for the problem to be solved in real-time in an embedded system. Once the optimal charging trajectory is computed over a time horizon, C3PU can package such data and send it out via appropriate communication protocols.
Mishra, Partha↗
Improving Wind Predictions in the Marine Atmospheric Boundary Layer through Parameter Estimation in a Single-Column Model
A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this study, we use the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts.
17 WIND ENERGY↗
Language models for the prediction of SARS-CoV-2 inhibitors
The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.
Blanchard, Andrew E.↗