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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Real time monitoring of COVID-19 intervention effectiveness through contact tracing data

Communities worldwide have used vaccines and facemasks to mitigate the COVID-19 pandemic. When an individual opts to vaccinate or wear a mask, they may lower their own risk of becoming infected as well as the risk that they pose to others while infected. The first benefit–reducing susceptibility–has been established across multiple studies, while the second–reducing infectivity–is less well understood. Using a new statistical method, we estimate the efficacy of vaccines and facemasks at reducing both types of risks from contact tracing data collected in an urban setting. We find that vaccination reduced the risk of onward transmission by 40.7% [95% CI 25.8–53.2%] during the Delta wave and 31.0% [95% CI 19.4–40.9%] during the Omicron wave and that mask wearing reduced the risk of infection by 64.2% [95% CI 5.8–77.3%] during the Omicron wave. By harnessing commonly-collected contact tracing data, the approach can broadly provide timely and actionable estimates of intervention efficacy against a rapidly evolving pathogen.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Investigation of Best-Practices and Computationally Inexpensive Radiative Exchange Models for Discrete Element Method Modeling of Aluminosilicate Particles in Concentrating Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrating solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrating solar power environments. In this study, coupled experimental and numerical slump testing of aluminosilicate particles was performed and computationally efficient radiative exchange models were evaluated to establish best-practices for discrete element method models for concentrating solar power environments. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Existing modeling limitations and computationally-efficient multi-modal heat transfer models were evaluated using Aspherix®, a commercial discrete element method software. High-temperature (< 1073 K) slump testing of aluminosilicate particles was performed to investigate the deviation between experimentally-observed and numerically-predicted angles of repose introduced by computation-time reduction practices including the relaxation of the particle elastic modulus and coarse-graining. Coarse-graining is used to use a single modeled particle that is representative of a collection of smaller particles, decreasing the computational cost at the expense of geometric accuracy. Additionally, relaxation of the elastic modulus is used to reduce computational time at the expense of an increased, modeled particle overlap. Prior studies have determined that aluminosilicate particles retain a high elastic modulus at high temperatures (< 1073 K), requiring small simulation timesteps to ensure resolved contact forces resemble appropriate solid mechanics. A parametric study was performed to evaluate the influence of computation time improvements on the deviation between experimental and modeled angle of repose across high temperatures < 1073 K. Additionally, numerical case studies were performed on candidate particle systems at varying porosities and temperatures. These studies were performed to investigate the influence of computationally-efficient radiative-exchange modeling methods coupled to Aspherix® on modeled accuracy and computation time. The recently-developed distance-based approximation was evaluated in estimating radiative exchange between particles and participating surfaces located in close proximity. The distance based approximation was developed to use tabulated estimates of the radiative distribution factor between individual particles and surfaces in close proximity (< 40 particle radii). These methods were expanded to the aluminosilicate particles of interest, including the influence of particle size distributions. To capture radiative exchange between particles and surfaces not in close proximity (> 40 particle radii) and to capture the absorption of directional irradiation from concentrating solar resources, a volumetrically-averaged radiative distribution factor was calculated between the modeled granular flow and surfaces using Monte Carlo ray-tracing for participating media. Volume-averaged absorption and scattering coefficients were predicted using a volumetric discretization of the modeled domain with monodisperse approximations based on geometric optics and experimentally-determined scattering phase functions for aluminosilicate particles.

14 SOLAR ENERGY↗
DOE OSTI2022

Generating Bright Emissive States by Modulating the Bandgap of Monolayer Tungsten Diselenide

Transition metal dichalcogenides (TMDs) are essential due to their fascinating electronic and optical properties, strong exciton binding energy, and layer-dependent bandgap. They can be tuned to function as a single-photon emitter, but the quantum yield of photoluminescence of single-layer WSe 2 is low. There is some evidence that a bright emissive state can be produced by introducing local defects through functionalization. In this paper we use spin-polarized periodic density functional theory (DFT) to study the effect of functionalization for the specific case of cyclic carbenes. We find that the simplest of these molecules, cyclopentadiene carbene (Cyc), binds to the surface by a covalent bond leading to a substantial change in the bandgap (1.24 eV compared to 1.64 eV for the pristine surface). Moreover, there are semiflat bands below the Fermi level that originate from σ bonding and the interaction between the lone-pair p orbitals on the carbon of Cyc that bonds with Se on the TMD. Cyc is found to form a Type IIa heterojunction before and after contact with the surface, where the “a” refers to ordering of the energy levels where the TMD levels have the larger splitting and “b” to the reverse. The effect of various electron-withdrawing and electron-donating groups on Cyc is investigated, and it is found that the direct bandgap and heterointerfaces can be chemically tuned with covalently bound functional groups. With an electron-withdrawing group (EWG) such as –CHO, –COCl, and –CN attached to Cyc, a Type III//Type IIa interface is formed, whereas all other EWGs used in this study form a Type IIa junction, before and after contact to the surface. The electron-donating groups (EDG) form Type Ib//Type Ia junctions, and in particular, we find that the system WSe 2 + Cyc-Me band structure consists of semiflat bands at valence band maxima localized on Cyc-Me and the conduction band minimum is coupled between the surface and functional group with a direct bandgap of 0.88 eV. Hence, we predict that the Cyc-Me-functionalized monolayer WSe 2 will produce a red-shifted bright emissive state. Furthermore, we also find that carbenes with the Cyc all-carbon ring have (1) a triplet ground state and (2) form covalent bonds to WSe 2 , while heterocyclic carbenes (1) have a singlet ground state and (2) do not form covalent bonds to WSe 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Dopant Compensation within the Intrinsic Poly-Si Isolation Region in Poly-Si/SiOx Passivated IBC Si Solar Cells

We report on the effect of dopant compensation within intrinsic poly-Si regions between p- and n-type fingers of poly-Si/SiOx passivated interdigitated back contact (IBC) solar cells using intrinsic poly-Si as the isolation region between the doped poly-Si fingers. First, we show that dopants from the doped fingers contaminate the intrinsic gap, resulting in doping of the entire intrinsic gap and overlap of the dopant tails from each finger. Next, we show that despite this doping across the gap, shunting between the doped fingers does not occur. We show that this is a result of trap-assisted compensation creating a highly resistive intrinsic region, preventing shunt. We simulate shunt resistance across the gap based on local carrier concentration and deep trap density. We show that trap defects within the poly-Si enhance compensation between the dopant tails. We experimentally confirm these predictions by scanning spreading resistance microscopy of the gap showing ~20 um domain with resistivity ~10^7 ..omega.. cm. Additionally, Kelvin probe force microscopy are compared to finite element simulations which result in the same approximate shape for potential profile, indicating diode behavior across the isolation region. These results demonstrate the powerful effect that trap defects have within the poly-Si isolation region and suggest that precision patterning is not as essential as once thought.

dopant compensation↗
DOE OSTI2022

Non-destructive measurement and real-time monitoring of apple hardness during ultrasonic contact drying via portable NIR spectroscopy and machine learning

Portable near-infrared spectrometer in the spectral range of 900–1700 nm was evaluated for the first time to assess and monitor apple hardness in real-time during ultrasonic drying. Calibration models were developed using PLS and ANN, and their performances were evaluated by internal leave-one-out cross-validation and an external dataset. Several pre-treatments including standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay first and second derivatives were employed to examine the effects of spectral variations in hardness prediction. Seven important wavelengths were selected using weighted regression coefficients to develop a simple MLR model to facilitate the model interpretation and circumvent noise. The models using PLS, MLR, and ANN with selected wavelengths predicted the apple hardness with R 2 p of 0.91, 0.91, 0.95, and RMSEP of 14.78, 14.85, and 12.46 N, respectively. Finally, the results indicate that portable NIR spectrometers are quite promising for real-time monitoring of apple hardness during ultrasonic drying.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Continuous precipitation‐filtration process for initial capture of a monoclonal antibody product using a four‐stage countercurrent hollow fiber membrane washing step

The significant increase in product titers, coupled with the growing focus on continuous bioprocessing, has renewed interest in using precipitation as a low‐cost alternative to Protein A chromatography for the primary capture of monoclonal antibody (mAb) products. In this work, a commercially relevant mAb was purified from clarified cell culture fluid using a tubular flow precipitation reactor with dewatering and washing provided by tangential flow microfiltration. The particle morphology was evaluated using an inline high‐resolution optical probe, providing quantitative data on the particle size distribution throughout the precipitation process. Data were obtained in both a lab‐built 2‐stage countercurrent washing system and a commercial countercurrent contacting skid that provided 4 stages of continuous washing. The processes were operated continuously for 2 h with overall mAb yield of 92 ± 3% and DNA removal of nearly 3 logs in the 4‐stage system. The high DNA clearance was achieved by selective redissolution of the mAb using a low pH acetate buffer. Host cell protein clearance was 0.59 ± 0.08 logs, comparable to that based on model predictions. The process mass intensity was slightly better than typical Protein A processes and could be significantly improved by preconcentration of the antibody feed material.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Contactless measurement of the photovoltage in BiVO 4 photoelectrodes

The power output of photoelectrochemical devices for solar energy-to-fuel conversion is determined by the photovoltage of the junction under illumination. In the presence of fast redox couples, the photovoltage can be obtained directly from current–voltage measurements of the device. However, for slow redox couples (H + /H 2 , O 2 /H 2 O) used in solar fuel photoelectrodes, photovoltage measurements are not straightforward, due to the kinetic overpotentials during charge transfer. Here we show that the photovoltage of BiVO 4 electrodes in contact with fast electron donors KI, Na 2 SO 3 or H 2 O 2 or K 4 Fe(CN) 6 can be measured in a contactless way with vibrating Kelvin probe surface photovoltage (SPV) spectroscopy. The photovoltage varies with illumination wavelength and intensity and matches the open circuit potential of the electrodes, obtained separately from electrochemical measurements. Plots of the photovoltage versus irradiance can be used to predict the oxidizing power of each electrode under zero applied bias. Except for K 4 Fe(CN) 6 , which causes shunting in the BiVO 4 electrode, photovoltage values correlate well with the built-in potential of each junction. Furthermore, the ability to obtain photovoltage information through contactless SPV measurements will be useful in the search for solid–liquid junctions with superior energy conversion properties.

14 SOLAR ENERGY↗
DOE OSTI2020

Insights into the mechanisms controlling the residual corrosion rate of borosilicate glasses

Borosilicate glasses are widely used to confine high-level radioactive wastes. The lifetime of these materials could reach hundreds of thousands of years if leaching of the glass into groundwater enables the formation of a passivating gel layer. Even in this regime, the glass will never stop corroding as thermodynamic equilibrium between glass and solution cannot be achieved. Therefore, accurate predictions of glass durability including passivation, require a deep understanding of the mechanisms controlling the so-called residual rate. However, despite tremendous efforts, these mechanisms remain poorly understood. Here, focusing on the behavior of the soluble elements of the International Simple Glass (B, Na, and Ca), we show that the residual rate is controlled by the behavior of B, a glass former supposed to dissolve instantaneously when in contact with water and thus widely considered as an ideal tracer. We then demonstrate that B release is controlled by multiple processes highly dependent on the pH. At the beginning of the passivating layer formation, the hydrolysis of B-O-Si linkages is rate-limiting and has an activation energy of ~60 kJ mol –1 , a value slightly lower than that for breaking Si-O-Si linkages. Once the fraction of closed pores resulting from gel restructuring is high enough, then diffusion of both reactants (water molecules) and some products (mainly B aq , Ca aq ) through the growing gel layer becomes rate-limiting. Consequently, B and Ca accumulate in an inner layer referred to as the active zone, with potential feedback on the B-O-Si hydrolysis. A new paradigm, including B as a key element of the system, is proposed to develop a comprehensive model for the corrosion of borosilicate glass.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mechanisms of Al3+ dimerization in alkaline solutions

The molecular speciation of aluminum (Al3+) in alkaline solutions is fundamental to its precipitation chemistry within a number of industrial applications that include ore renement and industrial processing of Al wastes. Under these conditions, Al3+ is predominantly Al(OH) {4 , while at high [Al] dimeric species also known to form. To date, the mechanism of dimer formation remains unclear and it is likely infuenced by complex ion ion interactions. In the present work, we investigate the role of ion pairing and a suite of potential dimerization pathways using a three-pronged approach that consists of density functional theory-based molecular dynamics (DFTMD), static DFT calculations (sDFT) and semi-empirical density functional tight binding molecular dynamics (SEMD). Specic cation eects imparted by the background electrolyte cations Na+, Li+ and K+ have been examined. Our simulations predict that when the Al species are ion paired with either cation the formation of the oxo-bridged Al2O(OH) 2{6 is favored with respect to the dihydroxo-bridged Al2(OH) 2{8 , in agreement with previousspectroscopic work. The formation of both dimers rst proceeds by bridging of two monomeric units via one hydroxo ligand, leading to a labile Al2(OH) 2{8 isomer. The eect of contact ion pairing of Li+ and K+ on the dimerization energetics is distinctly more favorable than that of Na+, which may have an impact on further ligomerization.

Pouvreau, Maxime↗
DOE OSTI2026

Solution structures in alkali nitrates and nitrites at high concentrations

In highly concentrated electrolyte solutions, where classical models often fail, specific ion–solvent interactions dictate bulk properties. Here, in this study, we combine small-angle X-ray scattering (SAXS) and Raman spectroscopy to link molecular coordination to mesoscale structure across alkali nitrate and nitrite solutions. Our results reveal a structural hierarchy driven by cation identity in that smaller cations (Li + , Na + ) form discrete contact ion pairs, while larger cations (K + , Rb + ) promote increasingly disordered local coordination and extended solute–solvent networks. Using nitrite salts as a structural control, we confirm this organizing principle across different anion geometries. Cs + undergoes a concentration-induced transition to a uniquely ordered state, resulting in a highly structured solution. This work provides a direct, mechanistic explanation for how cation choice dictates the ‘rules’ of solution architecture, offering a predictive basis for understanding phase behavior in complex industrial and environmental systems.

Mergelsberg, Sebastian T. [Pacific Northwest Natio↗
DOE OSTI2020

Electrode Mesoscale as a Collection of Particles: Coupled Electrochemical and Mechanical Analysis of NMC Cathodes

Battery electrodes are composed of polydisperse particles and a porous, composite binder domain. These materials are arranged into a complex mesostructure whose morphology impacts both electrochemical performance and mechanical response. We present image-based, particle-resolved, mesoscale finite element model simulations of coupled electrochemical-mechanical performance on a representative NMC electrode domain. Beyond predicting macroscale quantities such as half-cell voltage and evolving electrical conductivity, studying behaviors on a per-particle and per-surface basis enables performance and material design insights previously unachievable. Voltage losses are primarily attributable to a complex interplay between interfacial charge transfer kinetics, lithium diffusion, and, locally, electrical conductivity. Mesoscale heterogeneities arise from particle polydispersity and lead to material underutilization at high current densities. Particle-particle contacts, however, reduce heterogeneities by enabling lithium diffusion between connected particle groups. While the porous composite binder domain (CBD) may have slower ionic transport and less available area for electrochemical reactions, its high electrical conductivity makes it the preferred reaction site late in electrode discharge. Mesoscale results are favorably compared to both experimental data and macrohomogeneous models. This work enables improvements in materials design by providing a tool for optimization of particle sizes, CBD morphology, and manufacturing conditions.

42 ENGINEERING↗
DOE OSTI2021

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Contact patterns in wild pigs ( Sus scrofa ) from GPS tracking reveal spatial and temporal dynamics of social behaviour

Wild pig ( Sus scrofa Linnaeus, 1758) social behaviour affects disease transmission and landscape-level population management. Recent research has incorporated analysis of social structure to better understand the risk of disease transmission in wild pigs, although the relationship between overall social structure of wild pigs remains unclear. Here, we seek to improve understanding of the spatial and temporal dynamics of wild pig social structure and contact rates to better inform management strategies. Using GPS tracking data, we measured home range overlap, and estimated contact rates of wild pigs at four study sites in the southern USA to identify pairwise social associations (i.e., contacts) based on synchronous movement. Contact rate was strongly associated with home range overlap, but exhibited substantial variation, especially at moderate levels of home range overlap. We found that female–female dyads had higher contact rates and longer duration phases of social association compared to female–male and male–male dyads. We found male–male dyads tended to experience social associations farther from their home range centers than female–female or female–male dyads. Social associations between wild pig dyads are highly dynamic in their spatial and temporal structure. Further, dyads with strong social associations still experience substantial time apart. Our findings highlight the challenges of predicting spatial and social associations in wild pig social pairs due to their dynamic social structure over space and time.

McIlraith, Jack R.↗
DOE OSTI2020

Adaptive Recovery Model: Designing Systems for Testing Tracing and Vaccination to Support COVID-19 Recovery Planning.

This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

First-principles prediction of the Landau parameter for Fermi liquids near the unitarity limit

This paper explores the behavior of systems of cold fermions as they approach unitary above the critical temperature. Symmetry arguments indicate that at unitarity the Fermi-liquid picture breaks down. As we move away from unitarity, by decreasing the scattering length, the dilaton, the Goldstone boson resulting from the spontaneous breaking of Schrodinger symmetry by the Fermi sea, becomes gapped. At energies below this gap, the interaction between quasiparticles will be dominated by dilaton exchange. Here, the dilaton mass can, in turn, be related via anomaly matching to the scattering length and contact parameter within the confines of a systematic expansion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Designing strong and tunable magnetoelectric coupling in 2D trilayer heterostructures

Abstract The quest for electric-field control of nanoscale magnetic states such as skyrmions, which would impact the field of spintronics, has led to a challenging search for multiferroic materials or structures with strong magnetoelectric coupling and efficient electric-field control. Here we report a theoretical prediction that such phenomena can be realized in two-dimensional (2D) bilayer FE/PMM and trilayer FE/PMM/FE heterostructures (two-terminal and three-terminal devices), where FE is a 2D ferroelectric and PMM is a polar magnetic metal with strong spin–orbit coupling. Such a PMM has strong Dzyaloshinskii-Moriya interactions (DMI) that can generate skyrmions, while the FE can generate strong magnetoelectric coupling through polarization-polarization interactions. In trilayer heterostructures, contact to the metallic PMM layer enables multiple polarization configurations for electric-field control of skyrmions. We report density-functional-theory calculations for particular material choices that demonstrate the effectiveness of these arrangements, with the key driver being the polarization-polarization interactions between the PMM and FE layers. The present findings provide a method to achieve strong magnetoelectric coupling in the 2D limit and a new perspective for the design of related spintronics.

Materials Science↗