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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ferroelectric HfO 2 and the importance of strain

Ferroelectric oxides based on HfO 2 show tremendous promise for the next generation of memory and logic devices. The ferroelectric polymorph is one of several that can be derived from the high symmetry cubic fluorite structure of HfO 2 . A single grain of HfO 2 may consist of a coherent mixture of multiple orientational and translational variants of different polymorphs. Here, we use symmetry-adapted strain-order parameters to elucidate the relationship between the different HfO 2 polymorphs and their symmetrically equivalent variants. We use first-principles electronic structure methods to identify minimum energy pathways and map them in subspaces of the symmetry-adapted strain order parameters. We next investigate the atomic structure of domain boundaries that separate coexisting variants of ferroelectric HfO 2 . Further, we rely on Gibbsian excess quantities and a precise specification of mechanical boundary conditions to describe the thermodynamic properties of domain boundaries. Our first-principles calculations show that the O and Hf shuffle arrangement within a domain boundary is closely related to the intermediate shuffle patterns of the homogeneous pathways between ferroelectric variants. Furthermore, the preferred structure within a boundary is very sensitive to local strain constraints imposed by the adjacent ferroelectric variants, leading to highly anisotropic domain boundary energies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The incommensurately modulated structures of low-temperature labradorite feldspars: a single-crystal X-ray and neutron diffraction study

Labradorite feldspars of the plagioclase solid solution series have been known for their complicated subsolidus phase relations and enigmatic incommensurately modulated structures. Characterized by the irrationally indexed e -reflections in the diffraction pattern, e -labradorite shows the largest variation in the incommensurate ordering states among the e -plagioclase structures. The strongly ordered low-temperature e -labradorite is one of the last missing pieces of the e -plagioclase puzzle. Nine plutonic and metamorphic labradorite feldspar samples from Canada, Ukraine, Minnesota (USA), Tanzania and Greenland with compositions ranging from An 52.5 to An 68 were studied with single-crystal X-ray diffraction. Two crystals from Labrador, Canada, and Duluth, MN, USA, with wide enough twin lamellae were analyzed with single-crystal neutron diffraction. The incommensurately modulated structures of e -plagioclase are refined for the first time with neutron diffraction data, which confirmed that the T—O distance modulation in the low-temperature e -plagioclase results from the Al–Si ordering in the framework. Detailed configurations of the M site are also observed in the structures refined from neutron diffraction data, which were not possible to see with X-ray diffraction data. The relation between the q -vectors and the mole% An composition is revealed for the entire compositional range of e -plagioclase, from An 25 to An 75 . The previously proposed two-trend relation depending on the cooling rate and phase transition path is confirmed. A new classification of e -plagioclase ( e α , e β and e γ ) is proposed based on the q -vector of the structure, which makes it an independent character from the presence/absence of density modulation. New parameters are proposed to quantify the ordering states of these complicated aperiodic structures of e -plagioclases, such as the difference between $\langle$T 1 o—O$\rangle$ and $\langle$T 1 m—O$\rangle$ at phase t = 0.2 or the normalized intensity of the (071\bar 1) reflection.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Confusion-Driven Machine Learning of Structural Phases of a Flexible, Magnetic Stockmayer Polymer

We use a semisupervised, neural-network-based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole–dipole and Lennard-Jones interactions. As input for the neural network, we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang–Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimized structure and electronic band gap of monolayer GeSe from quantum Monte Carlo methods

Here, we have used highly accurate quantum Monte Carlo methods to determine the chemical structure and electronic band gaps of monolayer GeSe. Two-dimensional (2D) monolayer GeSe has received a great deal of attention due to its unique thermoelectric, electronic, and optoelectronic properties with a wide range of potential applications. Density functional theory (DFT) methods have usually been applied to obtain optical and structural properties of bulk and 2D GeSe. For the monolayer, DFT typically yields a larger band-gap energy than for bulk GeSe but cannot conclusively determine if the monolayer has a direct or indirect gap. Moreover, the DFT-optimized lattice parameters and atomic coordinates for monolayer GeSe depend strongly on the choice of approximation for the exchange-correlation functional, which makes the ideal structure-and its electronic properties-unclear. In order to obtain accurate lattice parameters and atomic coordinates for the monolayer, we use a surrogate Hessian-based parallel line search within diffusion Monte Carlo to fully optimize the GeSe monolayer structure. The DMC-optimized structure is different from those obtained using DFT, as are calculated band gaps. The potential energy surface has a shallow minimum at the optimal structure. This, combined with the sensitivity of the electronic structure to strain, suggests that the optical properties of monolayer GeSe are highly tunable by strain.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Emergent chirality in a polar meron to skyrmion phase transition

Polar skyrmions are predicted to emerge from the interplay of elastic, electrostatic and gradient energies, in contrast to the key role of the anti-symmetric Dzyalozhinskii-Moriya interaction in magnetic skyrmions. Here, we explore the reversible transition from a skyrmion state (topological charge of –1) to a two-dimensional, tetratic lattice of merons (with topological charge of –1/2) upon varying the temperature and elastic boundary conditions in [(PbTiO 3 ) 16 /(SrTiO 3 ) 16 ] 8 membranes. This topological phase transition is accompanied by a change in chirality, from zero-net chirality (in meronic phase) to net-handedness (in skyrmionic phase). We show how scanning electron diffraction provides a robust measure of the local polarization simultaneously with the strain state at sub-nm resolution, while also directly mapping the chirality of each skyrmion. Using this, we demonstrate strain as a crucial order parameter to drive isotropic-to-anisotropic structural transitions of chiral polar skyrmions to non-chiral merons, validated with X-ray reciprocal space mapping and phase-field simulations.

42 ENGINEERING↗
DOE OSTI2024

Ultrastable cathodes enabled by compositional and structural dual-gradient design

Cathodes for next-generation batteries are pressed for higher voltage operation (≥4.5 V) to achieve high capacity with long cyclability and thermal tolerance. Current cathodes fail to meet these requirements owing to structural and electrochemical strains at high voltages, leading to fast capacity fading. Here, in this work, we present a cathode with a coherent architecture ranging from ordered to disordered frameworks with concentration gradient and controllable Ni oxidation activities, which can overcome voltage ceilings imposed by existing cathodes. This design enables simultaneous high-capacity and high-voltage operation at 4.5 V without capacity fading, and up to 4.7 V with negligible capacity decay. Multiscale diffraction and imaging techniques reveal the disordered surface is electrochemically and structurally indestructible, preventing surface parasitic reactions and phase transitions. Structural coherence from ordering to disordering limits lattice parameter changes, mitigating lattice strain and enhancing morphological integrity. The dual-gradient design also notably improves thermal stability, driving the advancement of high-performance cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

How the birth and death of shear layers determine confinement evolution: from the L → H transition to the density limit

Electric field profile structure—especially its shear—is a natural order parameter for the edge plasma, and characterizes confinement regimes ranging from the H-mode (Wagner et al. 1982 Phys. Rev. Lett.49, 1408–1412 (doi:10.1103/PhysRevLett.49.1408)) to the density limit (DL) (Greenwald et al. 1988 Nucl. Fusion28, 2199–2207 (doi:10.1088/0029-5515/28/12/009)). The theoretical developments and lessons learned during 40 years of H-mode studies (Connor & Wilson 1999 Plasma Phys. Control. Fusion42, R1–R74 (doi:10.1088/0741-3335/42/1/201); Wagner 2007 Plasma Phys. Control. Fusion49, B1–B33 (doi:10.1088/0741-3335/49/12b/s01)) are applied to the shear layer collapse paradigm (Hong et al. 2017 Nucl. Fusion58, 016041 (doi:10.1088/1741-4326/aa9626)) for the onset of DL phenomena. Results from recent experiments on edge shear layers and DL phenomenology are summarized and discussed in the light of L → H transition physics. The theory of shear layer collapse is then developed. In this work, we demonstrate that shear layer physics captures both the well known current (Greenwald) scaling of the DL (Greenwald 2002 Plasma Phys. Control. Fusion44, R27–R53 (doi:10.1088/0741-3335/44/8/201); Greenwald et al. 2014 Phys. Plasmas21, 110501 (doi:10.1063/1.4901920)), as well as the emerging power scaling (Zanca, Sattin, JET Contributors 2019 Nucl. Fusion59, 126011 (doi:10.1088/1741-4326/ab3b31)). The derivation of the power scaling theory exploits an existing model, originally developed for the L → H transition (Diamond, Liang, Carreras, Terry 1994 Phys. Rev. Lett.72, 2565–2568 (doi:10.1103/PhysRevLett.72.2565); Kim & Diamond 2003 Phys. Rev. Lett.90, 185006 (doi:10.1103/PhysRevLett.90.185006)). We describe the enhanced particle transport events that occur following shear layer collapse. Open problems and future directions are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Domain wall dynamics in tungsten trioxide: Evidence for polar domain walls

Domain walls have distinct properties from the bulk, and tailoring them to suit the needs for device applications is critical. Tungsten trioxide, WO 3 , is of great interest for device applications that make use of domain wall properties; it exhibits a phenomenologically rich sequence of phase transitions, virtually all of which are ferroelastic in character, resulting in many sets of domain walls at low temperatures, each with their own unique properties. Domain wall motion and its contribution to the piezoelectric response have been investigated in WO 3 from 300 to 180 K using resonant ultrasound spectroscopy (RUS) and resonant piezoelectric spectroscopy (RPS), which showed that the P 2 1 / n , P 1 ¯ , and P 2 1 / c phases give a piezoelectric response despite the bulk being nominally centrosymmetric. Second harmonic generation (SHG) confirmed that polarity was strongest within the domain walls, and additional weak signals were found in the domains. Domain wall mobility was investigated in the P b c n , P 2 1 / n , and P 2 1 / c phases from 685 to 5 K. Domain walls in the P 2 1 / n and P 1 ¯ were more mobile than those in the P b c n and P 2 1 / c structures, and soon after the P 1 ¯ → P 2 1 / c transition the walls become pinned at ∼ 140 K . Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

From classical thermodynamics to phase-field method

Phase-field method is a density-based computational method at the mesoscale for modeling and predicting the temporal microstructure and property evolution during materials processes. The focus of this article is on connecting the most common phase-field equations to the very basic first and second laws of classical thermodynamics through rudimentary irreversible thermodynamics. It briefly discusses the relations of the continuum phase-field equations to their counter parts at the microscopic and atomic levels. It attempts to clarify the contributions of long-range elastic, electrostatic, and magnetic interactions to domain structure evolution during structural, ferroelectric, and ferromagnetic phase transformations by separating order parameter changes due to the presence of quasi-static fields and those arising from phase transformations. A few examples are presented to demonstrate the possibility of employing the phase-field method to provide guidance to designing materials for optimum properties or discovering novel mesoscale phenomena or new materials functionalities. Here, the article ends with a brief perspective on a number of potential future directions on the development and applications of phase-field method beyond its traditional applications to structural alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ab Initio Study of Stability, Local Order, and Phase Diagram For a Series of bcc-based Transition Metal Alloys

In this work, a parameter-free electronic structure approach is applied to the study of stability and chemical order in the 15 substitutional body-centered cubic (bcc)-based alloys made of the six transition metals of groups 5 (V, Nb, Ta) and 6 (Cr, Mo, W) of the periodic table. The method is based on a Green’s function description of the electronic structure of the random alloys. Configurational order is treated within the generalized perturbation method, and temperature effects are examined with a generalized mean-field approach. In contrast to the results summarized in the assessed phase diagrams, stability and ordering trends are predicted in a broad range of alloy composition for at least seven alloys, and explanation is found in their electronic structure properties. Short-range order results, thermodynamics analysis, and bcc-based phase diagrams are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning

Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( W C N s ‾ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Greybox Thermal Parameter Identification of Electric Machine Stators

The parameters of electric machine thermal equivalent circuit networks are difficult to predict due to material and manufacturing uncertainties. In this paper, a Greybox system identification approach is used to identify parameters of electric machine stator lumped parameter thermal networks (LPTNs). LPTNs provide a low order, computationally efficient, dynamic model of temperatures at specific locations. Second and third order LPTN model structures are defined as state space equations with stator thermal parameters to be identified. To test the Greybox electric machine stator thermal system identification, five stator motorette prototypes were constructed with controlled variations in slot fill and slot liner thickness. The variation in the motorette thermal parameters and thermal time constants are detected using the Greybox identification. Special attention is given to the impact of sampling rate and Greybox data record length on parameter estimation accuracy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Realizing continuous cation order-to-disorder tuning in a class of high-energy spinel-type Li-ion cathodes

Conventional Li-ion cathode materials are dominated by well-ordered structures, in which Li and transition metals occupy distinct crystallographic sites. Here, we show in this paper that profoundly new degrees of freedom for the optimization of electrochemical properties may be accessed if controllable cation disorder is introduced. In a class of high-capacity spinel-type cathode materials, we identify cation to anion ratio in synthesis as a key parameter for tuning the structure continuously from a well-ordered spinel, through a partially ordered spinel, to rocksalt. We find that the varying degree of cation disorder modifies the voltage profile, rate capability, and charge-compensation mechanism in a rational and predictable way. Our results indicate that spinel-type order is most beneficial for achieving high-rate performance as long as the cooperative 8a to 16c phase transition is suppressed, while more rocksalt-like disorder facilitates O redox, which can increase capacity. Our findings reveal an important tuning handle for achieving high energy and power in the vast space of partially ordered cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2021

The transport–structural correspondence across the nematic phase transition probed by elasto X-ray diffraction

Electronic nematicity in iron pnictide materials is coupled to both the lattice and the conducting electrons, which allows both structural and transport observables to probe nematic fluctuations and the order parameter. Here we combine simultaneous transport and X-ray diffraction measurements with in-situ tunable strain (elasto X-ray diffraction) to measure the temperature dependence of the shear modulus and elastoresistivity above the nematic transition and the spontaneous orthorhombicity and resistivity anisotropy below the nematic transition, all within a single sample of Ba(Fe 0.96 Co 0.04 ) 2 As 2 . The ratio of transport to structural quantities is nearly temperature independent over a 74 K range and agrees between the ordered and disordered phases. These results show that elasto X-ray diffraction is a powerful technique to probe the nemato-elastic and nemato-transport couplings, which have important implications to the nearby superconductivity. Furthermore, it also enables the measurement in the large strain limit, where the breakdown of the mean-field description reveals the intertwined nature of nematicity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Graph Dynamical neural network approach for decoding dynamical states in ferroelectrics.

Ferroelectric materials such as BaTiO 3 show tremendous potential for emerging advances in memory devices, particular neuromorphic type devices. High density of memory can be obtained by stabilising polar domain walls at the nanoscale, regions of discontinuity between the well-defined polarization order parameter, but little is known about what controls their structure and dynamics in real nanoscale materials. Indeed, chiral polar domain walls have been observed in heterogeneous ferroelectrics, such as oxygen-deficient BaTiO 3 , but very little is known about how such polar-domains walls interact with defects. Indeed, a critical understanding of how dynamics of domain-walls depend on point-defects is crucial to create engineered ferroelectric memory devices. For this work, we perform large-scale simulations of nansocale domain-wall dynamics in pristine and defective BaTiO 3 using reactive force-field developed by us earlier (Phys. Chem. Chem. Phys., 2019, 21, 18240–18249), and capture their dynamical dependence on point defects using a graph dynamical neural-network approach, which we adapted to interrogate solids with well-defined order-parameters, and implemented using Pytorch based libraries. Our machine learning (ML) approach goes beyond the traditional post-processing methods to capture both spatial and temporal heterogeneities of large-scale molecular dynamics simulations of complex defective ferroelectric oxide materials. We crucially find that isolated oxygen vacancies introduce very localized spatial regions (~1–2 unit-cell in length) that show slow dipole relaxation due to formation of defect-dipoles, and that these defect-dipoles in turn slow the intrinsic dynamics of domain walls. Further, the roughness of domain walls, also influenced by vacancies, introduce dynamic heterogeneity along the domain-wall. As such we find a novel mechanism by which quenched disorder due to defects introduce dynamic heterogeneity thereby influencing response to external fields (particularly time varying fields) in a ferroelectric. Our study also emphasizes the need for creating digital twins of dynamical quantities to achieve autonomous in operando control of nanoscale switching.

42 ENGINEERING↗
DOE OSTI2021

Solid-state NMR of unlabeled plant cell walls: high-resolution structural analysis without isotopic enrichment

Abstract Background Multidimensional solid-state nuclear magnetic resonance (ssNMR) spectroscopy has emerged as an indispensable technique for resolving polymer structure and intermolecular packing in primary and secondary plant cell walls. Isotope ( 13 C) enrichment provides feasible sensitivity for measuring 2D/3D correlation spectra, but this time-consuming procedure and its associated expenses have restricted the application of ssNMR in lignocellulose analysis. Results Here, we present a method that relies on the sensitivity-enhancing technique Dynamic Nuclear Polarization (DNP) to eliminate the need for 13 C-labeling. With a 26-fold sensitivity enhancement, a series of 2D 13 C– 13 C correlation spectra were successfully collected using the unlabeled stems of wild-type Oryza sativa (rice). The atomic resolution allows us to observe a large number of intramolecular cross peaks for fully revealing the polymorphic structure of cellulose and xylan. NMR relaxation and dipolar order parameters further suggest a sophisticated change of molecular motions in a ctl1 ctl2 double mutant: both cellulose and xylan have become more dynamic on the nanosecond and microsecond timescale, but the motional amplitudes are uniformly small for both polysaccharides. Conclusions By skipping isotopic labeling, the DNP strategy demonstrated here is universally extendable to all lignocellulose materials. This time-efficient method has landed the technical foundation for understanding polysaccharide structure and cell wall assembly in a large variety of plant tissues and species.

09 BIOMASS FUELS↗
DOE OSTI2023

Unusual Temperature Dependence of Water Sorption in Semicrystalline Hydrogels

Abstract Water vapor sorption is a ubiquitous phenomenon in nature and plays an important role in various applications, including humidity regulation, energy storage, thermal management, and water harvesting. In particular, capturing moisture at elevated temperatures is highly desirable to prevent dehydration and to enlarge the tunability of water uptake. However, owing to the thermodynamic limit of conventional materials, sorbents inevitably tend to capture less water vapor at higher temperatures, impeding their broad applications. Here, an inverse temperature dependence of water sorption in poly(ethylene glycol) (PEG) hydrogels, where their water uptake can be doubled with increasing temperature from 25 to 50 °C, is reported. With mechanistic modeling of water–polymer interactions, this unusual water sorption is attributed to the first‐order phase transformation of PEG structures, and the key parameters for a more generalized strategy in materials development are identified. This work elucidates a new regime of water sorption with an unusual temperature dependence, enabling a promising engineering space for harnessing moisture and heat.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cluster Variation Method Analysis of Correlations and Entropy in BCC Solid Solutions

Solid solutions occur when multiple chemical species share sites of a common crystal lattice. Although the single site occupation is random, chemical interaction preferences bias the occupation probabilities of neighboring sites, and this bias reduced the entropy of mixing below its ideal value. Sufficiently strong bias leads to symmetry-breaking phase transitions. Here, we apply the cluster variation method to explore solid solutions on body centered cubic lattices in the context of two specific compounds that exhibit opposite ordering trends. Employing density functional theory to model the energetics, we show that CuZn exhibits an order-disorder transition to the CsCl prototype structure, while AlLi instead takes the NaTl prototype structure, and we evaluate their temperature-dependent order parameters, correlations and entropies.

36 MATERIALS SCIENCE↗