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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. While existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).

42 ENGINEERING↗

DOE OSTI2022

Reticular chemistry for the rational design of mechanically robust mesoporous merged-net metal-organic frameworks

Access to metal-organic frameworks (MOFs) with enhanced mechanical stability is key to their successful deployment in practical applications. However, the high porosity of the material often affects mechanical stability. In this article, to achieve highly porous MOFs with enhanced mechanical stability, we explored the merged-net approach where two relatively fragile frameworks were merged into a robust MOF structure. We demonstrate the effectiveness of this approach by computationally evaluating mechanical properties of sph-MOFs with varying lengths of linkers. Prominently, we pinpoint the significance of triangular rigidity on the robustness of large-pore MOFs and, subsequently, designed and synthesized a rare earth (RE)-based RE-sph-MOF-5 by the reticulation of hexanuclear RE clusters, tritopic linkers, and unprecedentedly large planar hexatopic linkers containing 19 phenyl rings. The mechanical properties of sph-MOFs were characterized and quantified using amplitude-frequency modulation (AM-FM) bimodal atomic force microscopy (AFM) analyses. Markedly, the mesoporous RE-sph-MOF-5 expresses high mechanical stability despite its large mesoporous cavities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Relative plan robustness of step-and-shoot vs rotational intensity–modulated radiotherapy on repeat computed tomographic simulation for weight loss in head and neck cancer

Introduction: Interfractional anatomical alterations may have a differential effect on the dose delivered by step-and-shoot intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT). The increased degrees of freedom afforded by rotational delivery may increase plan robustness (measured by change in target volume coverage and doses to organs at risk [OARs]). However, this has not been evaluated for head and neck cancer. Materials and methods: A total of 10 patients who required repeat computed tomography (CT) simulation and replanning during head and neck IMRT were included. Step-and-shoot IMRT and VMAT plans were generated from the original planning scan. The initial and second CT simulation scans were fused and targets/OAR contours transferred, reviewed, and modified. The plans were applied to the second CT scan and doses recalculated without repeat optimization. Differences between step-and-shoot IMRT and VMAT for change in target volume coverage and doses to OARs between first and second CT scans were compared by Wilcoxon signed rank test. Results: There were clinically relevant dosimetric changes between the first and the second CT scans for both the techniques (reduction in mean D{sub 95%} for PTV2 and PTV3, D{sub min} for CTV2 and CTV3, and increased mean doses to the parotid glands). However, there were no significant differences between step-and-shoot IMRT and VMAT for change in any target coverage parameter (including D{sub 95%} for PTV2 and PTV3 and D{sub min} for CTV2 and CTV3) or dose to any OARs (including parotid glands) between the first and the second CT scans. Conclusions: For patients with head and neck cancer who required replanning mainly due to weight loss, there were no significant differences in plan robustness between step-and-shoot IMRT and VMAT. This information is useful with increased clinical adoption of VMAT.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Robustness of magnetocrystalline anisotropy and coercivity in Fe–Co–B

As a potential candidate for critical chemical element free permanent magnet, (Fe 0.7 Co 0.3 ) 2 B, shows promising intrinsic magnetic properties, including large magnetization, high Curie temperature and moderate magnetocrystalline anisotropy (MCA). Still, the coercivity that has been achieved is relatively low. To understand and improve coercivity, here we study the robustness of MCA related to the fluctuation of chemical composition and the structural distortion and its effect on coercivity in (Fe 0.7 Co 0.3 ) 2 B from first principles DFT (density functional theory) calculation and micromagnetic simulation. A fluctuation of cobalt content of 10%, or 1% isotropic lattice strain, or 2% tetragonal distortion reduces MCA energy up to 30%. By considering randomly distributed local MCA energy reduction up to 30%, the calculated coercivity of anisotropic bulk magnets reduced from 3.6 kOe to 2.2 kOe. Rational microstructure design, such as grain size refinement and development of strong crystal texture of MCA easy-axis (i.e., degree of alignment for (Fe 0.7 Co 0.3 ) 2 B grains), can enhance coercivity and mitigate the effect of local MCA reduction. In addition to acceptable intrinsic magnetic properties, the promising hard magnetic phase needs good MCA robustness to facilitate the development of practical magnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The exit-wave power-cepstrum transform for scanning nanobeam electron diffraction: robust strain mapping at subnanometer resolution and subpicometer precision

Scanning nanobeam electron diffraction (NBED) with fast pixelated detectors is a valuable technique for rapid, spatially resolved mapping of lattice structure over a wide range of length scales. However, intensity variations caused by dynamical diffraction and sample mistilts can hinder the measurement of diffracted disk centers as necessary for quantification. Robust data processing techniques are needed to provide accurate and precise measurements for complex samples and non-ideal conditions. Here we present an approach to address these challenges using a transform, called the exit wave power cepstrum (EWPC), inspired by cepstral analysis in audio signal processing. The EWPC transforms NBED patterns into real-space patterns with sharp peaks corresponding to inter-atomic spacings. We describe a simple analytical model for interpretation of these patterns that cleanly decouples lattice information from the intensity variations in NBED patterns caused by tilt and thickness. With tracking the inter-atomic spacing peaks in EWPC patterns, strain mapping is demonstrated for two practical applications: mapping of ferroelectric domains in epitaxially strained PbTiO 3 films and mapping of strain profiles in arbitrarily oriented core-shell Pt-Co nanoparticle fuel-cell catalysts. The EWPC transform enables lattice structure measurement at sub-pm precision and sub-nm resolution that is robust to small sample mistilts and random orientations.

42 ENGINEERING↗

DOE OSTI2015

Elastic hydrogel substrate supports robust expansion of murine myoblasts and enhances their engraftment

The application of satellite cell-derived myoblasts in regenerative medicine has been restricted by the rapid loss of stemness during in vitro cell expansion using traditional culture systems. However, studies published in the past decade have highlighted the influence of substrate elasticity on stem cell fate and revealed that culture on a soft hydrogel substrate can promote self-renewal and prolong the regenerative potential of muscle stem cells. Whether hydrogel substrates have similar effects after long-term robust expansion remains to be determined. Herein we prepared an elastic chitosan/beta-glycerophosphate/collagen hydrogel mimicking the soft microenvironment of muscle tissues for use as the substrate for satellite cell culture and investigated its influence on long-term cell expansion. After 20 passages in culture, satellite cell-derived myoblasts cultured on our hydrogel substrate exhibited significant improvements in proliferation capability, cell viability, colony forming frequency, and potential for myogenic differentiation compared to those cultured on a routine rigid culture surface. Immunochemical staining and western blot analysis both confirmed that myoblasts cultured on the hydrogel substrate expressed higher levels of several differentiation-related markers, including Pax7, Pax3, and SSEA-1, and a lower level of MyoD compared to myoblasts cultured on rigid culture plates (all p<0.05). After transplantation into the tibialis anterior of nude mice, myoblasts that had been cultured on the hydrogel substrate demonstrated a significantly greater engraftment efficacy than those cultured on the traditional surface. Collectively, these results indicate that the elastic hydrogel substrate supported robust expansion of murine myoblasts and enhanced their engraftment in vivo. - Highlights: • An elastic hydrogel was designed to mimic the pliable muscle tissue microenvironment. • Myoblasts retained their stemness in long-term culture on the elastic hydrogels. • Myoblasts expanded on the elastic hydrogel exhibited enhanced in vivo engraftment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning (DL) research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high-dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace–Beltrami equation on a high-dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. This paper is a significant extension of our earlier work published in NeurIPS, 2018. For reproducibility, the code is available at https://github.com/BaoWangMath/DNN-DataDependentActivation .

Mathematics↗

DOE OSTI2024

A Trapping-Micro-LC-FAIMS/dCV-MS Strategy for Ultrasensitive and Robust Targeted Quantification of Protein Drugs and Biomarkers

The sensitivity of LC-MS in quantifying target proteins in plasma/tissues is significantly hindered by co-eluted matrix interferences. While antibody-based immuno-enrichment effectively reduces interferences, developing and optimizing antibodies are often time-consuming and costly. Here, in this study, by leveraging the orthogonal separation capability of Field Asymmetric Ion Mobility Spectrometry (FAIMS), we developed a FAIMS/differential-compensation-voltage (FAIMS/dCV) method for antibody-free, robust, and ultra-sensitive quantification of target proteins directly from plasma/tissue digests. By comparing the intensity-CV profiles of the target vs. co-eluted endogenous interferences, the FAIMS/dCV approach identifies the optimal CV for quantification of each target protein, thus maximizing the signal-to-noise ratio (S/N). Compared to quantification without FAIMS, this technique dramatically reduces endogenous interferences, showing a median improvement of the S/N by 14.8-fold for the quantification of 17 representative protein drugs and biomarkers in plasma or tissues and a 5.2-fold median increase in S/N over conventional FAIMS approach, which uses the peak CV of each target. We also discovered that the established CV parameters remain consistent over months and are matrix-independent, affirming the robustness of the developed FAIMS/dCV method and the transferability of the method across matrices. The developed method was successfully demonstrated in three applications: the quantification of monoclonal antibodies with sub-ng/mL LOQ in plasma, an investigation of the time courses of evolocumab and its target PCSK9 in a preclinical setting, and a clinical investigation of low abundance obesity-related biomarkers. This innovative and easy-to-use method has extensive potential in clinical and pharmaceutical research, particularly where sensitive and high-throughput quantification of protein drugs and biomarkers is required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

An Antibody Cocktail-Based Immunoaffinity-LC-MS Method Enabled Ultra-Sensitive and Robust Quantification of Circulating Proinsulin Proteoforms and C-Peptide

Accurately measuring circulating proinsulin proteoforms is crucial for clinical investigation of diabetes, but was previously not feasible owing to limited assay specificity/sensitivity. Here, in this study, we devised a highly sensitive LC-MS-based strategy to quantify intact proinsulin, des-31,32 and des-64,65 proinsulin, and C-peptide in circulation. The method involves: (i) quantitative, robust affinity capture using an optimized antibody cocktail, eliminating the severe quantitative bias across multiple proteoforms typically introduced when using a single antibody; (ii) Lys-C digestion producing unique signature peptides for each proteoform, and (iii) trapping-nano-LC coupled with FAIMS/dCV-MS for an ultrasensitive analysis. The selective trapping/delivery ensured sensitive/selective analysis of the targets while achieving excellent analytical robustness that is critical for clinical assays, and the FAIMS/dCV substantially reduces baseline noise/interferences, further enhancing S/N. The assay achieved exceptional sensitivity, with serum LOQs of 1.7, 2.3, and 3.6 pg/mL respectively for intact-proinsulin, des-31,32 and des-64,65, representing the first assay capable of sensitively quantifying these major circulating proinsulin proteoforms. We applied this assay to 78 subjects, including autoantibody positive (n = 20) and new-onset type 1 diabetes (T1D, n = 19) with respective age/sex/BMI-matched controls, enabling the first accurate profiling of proinsulin proteoforms in clinical groups. The assay results demonstrated a clear separation of control and new-onset T1D groups that a parallel total-proinsulin ELISA assay fails to capture. Furthermore, distinct expression patterns in relative abundance ratios among proteoforms were observed across clinical groups. This assay may provide valuable insights into the β-cell functions and the onset/progression of diabetes and other associated conditions. Moreover, the strategy is broadly applicable to targeted measurement of other biomarker proteoforms.

Shen, Qingqing [University at Buffalo, NY (United ↗

DOE OSTI2022

Polymeric Microcapsules as Robust Mimics of Emulsion Liquid Membranes for Selective Ion Separations

Selective ion separations are increasingly needed to combat water scarcity, recover resources from wastewater, and enable the efficient recycling of electronics waste. Emulsion liquid membranes (ELMs) have received interest due to rapid kinetics, high selectivities, and low solvent requirements but are too unstable for industrial usage. Here, we demonstrate that polymeric microcapsules can serve as robust, solvent-free mimics of ELMs. As a proof of concept, we incorporated the copper-selective ligand Lix 84-I in the walls of microcapsules formed from a commercial polystyrene-b-polybutadiene-b-polystyrene triblock polymer. The microcapsules were formed from a double-emulsion template, resulting in particles typically 20–120 μm in diameter that encapsulated even smaller droplets of a dilute (≤0.5 M) H 2 SO 4 solution. Batch experiments demonstrated facilitated-transport behavior, with equilibrium reached in as little as 10 min for microcapsules with 1% ligand, and with ~15-fold selectivity for Cu 2+ over Ni 2+ . Furthermore, the microcapsules could be packed readily in columns for flow-through operation, thus enabling near-complete Cu 2+ removal in ~2 min under certain conditions, recovery of Cu 2+ by flowing through fresh dilute H 2 SO 4 , and reuse for at least 10 cycles. The approach in this work can serve as a template for using selective ligands to enable robust and simple flow-through processes for a variety of selective ion separations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Designing a Robust MEA-Based Post-Combustion Carbon Capture Process with Capture Rate Guarantees

This work presents an application of the nonlinear two-stage robust optimization solver PyROS to the model-based design and operation of a monoethanolamine scrubbing process for CO2 capture under epistemic uncertainty. Through this application, risk-averse process designs are successfully obtained for CO2 capture targets ranging from 90% to over 99%. In particular, the risk-averse solutions for CO2 capture targets of up to 98% are shown to be only marginally more expensive than their nominally optimal counterparts. Thus, the results demonstrate the utility of recently developed nonlinear robust optimization approaches for the solution of large-scale chemical process models under uncertainty.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Robust Tensor Hypercontraction of the Particle–Particle Ladder Term in Equation-of-Motion Coupled Cluster Theory

One method of representing a high-rank tensor as a (hyper-)product of lower-rank tensors is the tensor hypercontraction (THC) method of Hohenstein et al. This strategy has been found to be useful for reducing the polynomial scaling of coupled-cluster methods by representation of a four-dimensional tensor of electron-repulsion integrals in terms of five two-dimensional matrices. Pierce et al. have already shown that the application of a robust form of THC to the particle–particle ladder (PPL) term reduces the cost of this term in couple-cluster singles and doubles (CCSD) from O(N 6 ) to O(N 5 ) with negligible errors in energy with respect to the density-fitted variant. In this work, we have implemented the least-squares variant of THC (LS-THC) which does not require a nonlinear tensor factorization, including the robust form (R-LS-THC), for the calculation of the excitation and electron attachment energies using equation-of-motion coupled cluster methods EOMEE-CCSD and EOMEA-CCSD, respectively. We have benchmarked the effect of the R-LS-THC-PPL approximation on excitation energies using the comprehensive QUEST database and the accuracy of electron attachment energies using the NAB22 database. Here, we find that errors on the order of 1 meV are achievable with a reduction in total calculation time of approximately 5x.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Optimized Auxiliary Functions for Robust Mitigation of Finite-Size Errors in Periodic Hybrid Density Functional Theory

When calculating properties of periodic systems at the thermodynamic limit (TDL), the dominant source of finite size error (FSE) arises from the long-range Coulomb interaction, and can manifest as a slowly converging quadrature error when approximating an integral in the reciprocal space by a finite sum. The singularity subtraction (SS) method offers a systematic approach for reducing this quadrature error and thus the FSE. Here, in this work, we first investigate the performance of the SS method in the simplest setting, aiming at reducing the FSE in exact exchange calculations by subtracting the Coulomb contribution with a single, adjustable Gaussian auxiliary function. We demonstrate that a simple fitting method can robustly estimate the optimal Gaussian width and leads to rapid convergence toward the TDL. Furthermore, we suggest new forms of the auxiliary function, whose optimal parameters could also be determined through least-squares fitting. For a range of semiconductors and insulators, the proposed auxiliary functions achieve robust, millihartree-level accuracy in hybrid density functional theory calculations, including cases with sparse k-meshes and large basis sets.

Quiton, Stephen Jon [University of California, Ber↗

DOE OSTI2019

Energy Transfer Networks within Upconverting Nanoparticles Are Complex Systems with Collective, Robust, and History-Dependent Dynamics

Applications of photon upconverting nanoparticles (UCNPs) in biological imaging and solar energy conversion demand that their anti-Stokes luminescence be both tunable and efficient. Rational design of more efficient UCNPs requires an understanding of energy transfer (ET) between their lanthanide dopants, dynamics that are typically characterized by measuring luminescence lifetimes. Existing knowledge, however, cannot explain basic observations in lifetime experiments, such as their dependence on excitation power, significantly limiting the generality and reliability of lifetime measurements. Here in this paper, we elucidate the origins of the ET dynamics and luminescence lifetimes of Yb 3+ -, Er 3+ -co-doped NaYF 4 UCNPs using time-resolved luminescence and novel applications of rate equations and stochastic simulations. Experiments and calculations consistently show that at high concentrations of Er 3+ , the luminescence lifetimes of UCNPs decrease as much as six-fold when excitation power densities are increased over 6 orders of magnitude. Since power-dependent lifetimes cannot be explained by intrinsic relaxation rates of individual transitions, we analyze lifetime data by treating each UCNP as a complex ET network. We find that UCNP ET networks exhibit four distinguishing characteristics of complex systems: collectivity, nonlinear feedback, robustness, and history dependence. We conclude that power-dependent lifetimes are the consequence of thousands of minor relaxation pathways that act collectively to depopulate and repopulate Er 3+ emitting levels. These ET pathways are dependent on past excitation power because they originate from excited donors and excited acceptors; however, each transition has an unexpectedly small impact on lifetimes due to negative feedback in the network. This robustness is determined by systematically “knocking out”, or disabling, ET transitions in kinetic models. Our classification of UCNP ET networks as complex systems explains why UCNP luminescence lifetimes do not match the intrinsic lifetimes of emitting states. In the future, UCNP networks may be engineered to rival the complexity of biological networks that pattern features with unmatched precision.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Two-Dimensional van der Waals Nanoplatelets with Robust Ferromagnetism

We have synthesized unique colloidal nanoplatelets of the two-dimensional (2D) van der Waals ferromagnet CrI3 and have characterized these nanoplatelets structurally, magnetically, and by magnetic circular dichroism spectroscopy. The CrI3 nanoplatelets have lateral dimensions of ~25 nm and thicknesses of only ~4 nm, corresponding to just a few CrI3 monolayers. Magnetic and magneto-optical measurements demonstrate robust 2D ferromagnetic ordering with Curie temperatures similar to bulk CrI3, despite their small size. These data also show magnetization steps akin to those observed in micron-sized few-layer 2D sheets associated with concerted spin-reversal of individual CrI3 layers within few-layer van der Waals stacks. Similar data have also been obtained for CrBr3 and anion-alloyed Cr(I1–xBrx)3 nanoplatelets. These results represent the first example of lateral nanostructures of 2D van der Waals ferromagnets of any composition. The demonstration of robust ferromagnetism at nanometer lateral dimensions opens new doors for miniaturization in spintronics devices based on van der Waals ferromagnets.

De Siena, Michael C.↗

DOE OSTI2025

Robust Spin-Moiré-Superlattice-Driven Gap Opening in EuAg 4 Sb 2 under in-Plane Magnetic Field

Moiré superlattices introduce new length and energy scales, enabling discoveries, such as unconventional superconductivity, in 2D systems. This concept has recently been extended to bulk materials with multiple-q spin textures, opening exciting opportunities for spin moiré physics. A notable example is EuAg 4 S b2 , where a spin moiré superlattice (SMS), manifested as a double-q spin modulation, induces a superzone gap opening. In this work, we investigate the tunability of this gap by an in-plane magnetic field in EuAg 4 Sb 2 by using neutron scattering, magnetization, and transport measurements. We reveal a cycloidal ground state and multiple spin-reoriented phases induced by the in-plane field, highlighting the critical role of in-plane components in driving magnetic transitions. Moreover, we demonstrate that a robust gap opening persists in the double-q phase, regardless of in-plane field orientation. Model calculations attribute this robustness to the stability of the SMS under tilted fields. Here, our results establish EuAg 4 Sb 2 as a tunable platform for exploring the spin-texture-driven superzone gap opening in electronic states.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Hybrid MBE Route to Adsorption-Controlled Growth of BaTiO 3 Membranes with Robust Polarization Switching

Freestanding ferroelectric membranes are promising for flexible electronics, nonvolatile memory, photonics, and spintronics, but their synthesis is challenged by the need for reproducibility with precise stoichiometric control. Here, we demonstrate the adsorption-controlled growth of single-crystalline, epitaxial BaTiO 3 films by hybrid molecular beam epitaxy (MBE) on a binary oxide sacrificial layer. Using a simple water-droplet lift-off method, we obtained submillimeter- to millimeter-sized membranes that retained crystallinity, as confirmed by high-resolution X-ray diffraction, and exhibited robust tetragonal symmetry, as verified by Raman spectroscopy. Impedance spectroscopy confirmed a high dielectric constant of ∼1340, reflecting the robust dielectric response of the membranes. Ferroelectric functionality was revealed by piezoresponse force microscopy (PFM) and further verified by polarization-electric field (P-E) loop measurements with Positive-Up-Negative-Down (PUND). The P-E loops exhibited a remnant polarization of ∼5 μC cm –2 and a coercive field of ∼63 kV cm –1 . Furthermore, these results were interpreted in relation to c- and a-domain configurations.

Raman spectroscopy↗