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36 results for: All records
Author ORCID ID is 0000000153546152
Full Text and Citations
  1. Strongly correlated vanadium dioxide (VO 2) is one of the most promising materials that exhibits a temperature-driven, metal–insulator transition (MIT) near room temperature. The ability to manipulate the MIT at nanoscale offers both insight into understanding the energetics of phase transition and a promising potential for nanoelectronic devices. In this work, we study nanoscale electrochemical modifications of the MIT in epitaxial VO 2 thin films using a combined approach with scanning probe microscopy (SPM) and theoretical calculations. We find that applying electric voltages of different polarity through an SPM tip locally changes the contact potential difference and conductivity on themore » surface of VO 2 by modulating the oxygen stoichiometry. We observed nearly 2 orders of magnitude change in resistance between positive and negative biased-tip written areas of the film, demonstrating the electric field modulated MIT behavior at the nanoscale. Density functional theory calculations, benchmarked against more accurate many-body quantum Monte Carlo calculations, provide information on the formation energetics of oxygen defects that can be further manipulated by strain. In conclusion, this study highlights the crucial role of oxygen vacancies in controlling the MIT in epitaxial VO 2 thin films, useful for developing advanced electronic and iontronic devices.« less
  2. Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures ofmore » ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.« less
  3. Exploration of phase transitions and construction of associated phase diagrams are of fundamental importance for condensed matter physics and materials science alike, and remain the focus of extensive research for both theoretical and experimental studies. For the latter, comprehensive studies involving scattering, thermodynamics, and modeling are typically required. We present a new approach to data mining multiple realizations of collective dynamics, measured through piezoelectric relaxation studies, to identify the onset of a structural phase transition in nanometer-scale volumes, that is, the probed volume of an atomic force microscope tip. Machine learning is used to analyze the multidimensional data sets describingmore » relaxation to voltage and thermal stimuli, producing the temperature-bias phase diagram for a relaxor crystal without the need to measure (or know) the order parameter. The suitability of the approach to determine the phase diagram is shown with simulations based on a two-dimensional Ising model. Finally, these results indicate that machine learning approaches can be used to determine phase transitions in ferroelectrics, providing a general, statistically significant, and robust approach toward determining the presence of critical regimes and phase boundaries.« less
  4. The ability to control thin-film growth has led to advances in our understanding of fundamental physics as well as to the emergence of novel technologies. However, common thin-film growth techniques introduce a number of limitations related to the concentration of defects on film interfaces and surfaces that limit the scope of systems that can be produced and studied experimentally. Here, we developed an ion-beam based subtractive fabrication process that enables creation and modification of thin films with pre-defined thicknesses. To accomplish this we transformed a multimodal imaging platform that combines time-of-flight secondary ion mass spectrometry with atomic force microscopy tomore » a unique fabrication tool that allows for precise sputtering of the nanometer-thin layers of material. To demonstrate fabrication of thin-films with in situ feedback and control on film thickness and functionality we systematically studied thickness dependence of ferroelectric switching of lead-zirconate-titanate, within a single epitaxial film. Lastly, our results demonstrate that through a subtractive film fabrication process we can control the piezoelectric response as a function of film thickness as well as improve on the overall piezoelectric response versus an untreated film.« less
  5. Organometallic halide perovskites (OMHPs) have attracted broad attention as prospective materials for optoelectronic applications. Among the many anomalous properties of these materials, of special interest are the ferroelectric properties including both classical and relaxor-like components, as a potential origin of slow dynamics, field enhancement, and anomalous mobilities. Here, ferroelectric properties of the three representative OMHPs are explored, including FAPb xSn 1–xI 3 (x = 0, x = 0.85) and FA 0.85MA 0.15PbI 3 using band excitation piezoresponse force microscopy and contact mode Kelvin probe force microscopy, providing insight into long- and short-range dipole and charge dynamics in these materials andmore » probing ferroelectric density of states. Furthermore, second-harmonic generation in thin films of OMHPs is observed, providing a direct information on the noncentrosymmetric polarization in such materials. Overall, the data provide strong evidence for the presence of ferroelectric domains in these systems; however, the domain dynamics is suppressed by fast ion dynamics. These materials hence present the limit of ferroelectric materials with spontaneous polarization dynamically screened by ionic and electronic carriers.« less
  6. Here, a new polar and magnetic oxide, YCrWO 6, was successfully synthesized and characterized. YCrWO 6 crystallizes in polar orthorhombic space group Pna21 (no. 33) of edge-sharing dimers of CrO 6 and WO 6 octahedra, which are connected by corner-sharing to form a three-dimensional framework structure with Y 3+ cations located in the channels. The structure of YCrWO 6 is related to that of CaTa2O 6; however, the ordering of Cr 3+ and W 6+ in the octahedral sites breaks the inversion symmetry of the parent CaTa 2O 6 structure. X-ray absorption near edge spectroscopy of YCrWO 6 confirmed themore » oxidation state of Cr 3+ and W 6+. Temperature-dependent optical second harmonic generation measurements on YCrWO 6 confirmed the noncentrosymmetric character and evidenced a noncentrosymmetric-to-centrosymmetric phase transition above 800 °C. Piezoresponse force microscopy measurements on YCrWO 6 at room temperature show strong piezoelectric domains. Magnetic measurements of YCrWO 6 indicate antiferromagnetic order at T N of ~22 K with Weiss temperature of -34.66 K.« less
  7. Double corundum-related polar magnets are promising materials for multiferroic and magnetoelectric applications in spintronics. However, their design and synthesis is a challenge, and magnetoelectric coupling has only been observed in Ni 3TeO 6 among the known double corundum compounds to date. Here in this paper we address the high-pressure synthesis of a new polar and antiferromagnetic corundum derivative Mn 2MnWO 6, which adopts the Ni 3TeO 6-type structure with low temperature first-order field-induced metamagnetic phase transitions (T N = 58 K) and high spontaneous polarization (~ 63.3 μC·cm -2). The magnetostriction-polarization coupling in Mn 2MnWO 6 is evidenced by secondmore » harmonic generation effect, and corroborated by magnetic-field-dependent pyroresponse behavior, which together with the magnetic-field-dependent polarization and dielectric measurements, qualitatively indicate magnetoelectric coupling. Finally, piezoresponse force microscopy imaging and spectroscopy studies on Mn 2MnWO 6 show switchable polarization, which motivates further exploration on magnetoelectric effect in single crystal/thin film specimens.« less
  8. Recent advances in scanning transmission electron and scanning probe microscopies have opened unprecedented opportunities in probing the materials structural parameters and various functional properties in real space with an angstrom-level precision. This progress has been accompanied by exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extracting informationmore » from atomically resolved images including location of the atomic species and type of defects. We develop a “weakly-supervised” approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular “rotor”. In conclusion, this deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.« less

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