Abstract
A single-phase sample of the Aurivillius compound Pb0.4Bi2.1La0.5Nb1.7Mn0.3O9 was prepared by a molten salt method using K2SO4/Na2SO4 as the flux. The crystal structure, morphology, ferroelectric, and magnetic properties were investigated. Neutron powder diffraction data confirmed a non-centrosymmetric orthorhombic crystal structure with space group A21am and Pb/Bi disorder in the bismuth oxide blocks, Bi/Pb/La disorder on the perovskite A-site, and Nb/Mn disorder on the perovskite B-site. The morphology of the sample showed anisotropic plate-like grains as probed by scanning electron microscopy. The dielectric constant exhibits a transition peak between 600 K and 640 K that depends on frequency, indicating relaxor ferroelectric behavior. Electrical polarization versus applied field loops are unsaturated, with a remnant polarization of 0.43 μC/cm2 at 40 Hz under the maximum electrical field applied of 160 kV/cm. The ferroelectricity originates from the displacement of oxygen atoms in the BO6 octahedra, resulting in a polar structural distortion. Magnetic susceptibility measurements showed the presence of mixed Mn3+ and Mn4+, resulting in short-range ferromagnetic order via double exchange interactions below 33 K. The remnant magnetization (Mr) is 0.01 emu/g at 5 K. This mixed valence of Mn cations is mainly responsible for the high electrical conductivity. Thus, Pb0.4Bi2.1La0.5Nb1.7Mn0.3O9 exhibits coexisting ferroelectric and ferromagnetic properties.
Highlights
Upscaling local eddy covariance (EC) measurements (Baldocchi et al, 2001) from tower footprint to global wall-towall maps uses globally available predictor variables such as satellite remote sensing and meteorological data (Jung et al, 2011)
Our results suggest a high degree of cross-product consistency of global mean gross primary productivity (GPP) patterns (Fig. 2)
The FLUXCOM initiative generated a large ensemble of global carbon flux products for two defined setups that differ in the set of predictor variables and spatial–temporal resolution
Summary
Upscaling local eddy covariance (EC) measurements (Baldocchi et al, 2001) from tower footprint to global wall-towall maps uses globally available predictor variables such as satellite remote sensing and meteorological data (Jung et al, 2011) These forcing data are first used to establish empirical models for fluxes of interest at the site level and to estimate gridded fluxes by applying these models across all vegetated grid cells. Previous FLUXNET upscaling efforts using machine learning techniques (Beer et al, 2010; Jung et al, 2009, 2011) yielded global products that present a data-driven “bottom-up” perspective on carbon fluxes between the biosphere and the atmosphere These bottom-up products are complementary to process-based model simulations and “top-down” atmospheric inversions. These setups systematically vary machine learning and flux partitioning methods as well as forcing data sets to separate measured net ecosystem exchange (NEE) into gross primary productivity (GPP) and terrestrial ecosystem respiration (TER) (Jung et al, 2019; Tramontana et al, 2016)
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