HIMU geochemical signature originating from the transition zone

Plume volcanism may sample mantle sources deeper than mid-ocean ridge and arc volcanism. Ocean island basalts (OIBs) are commonly related to plume volcanism, and their diverse isotopic and elemental compositions can be described using a limited number of mantle endmembers. However, the origins and depths of these mantle endmembers are highly debated. Here we show that the HIMU (high μ, U/204Pb) endmember may reside in the transition zone. Specifically, we report the geochemical signature of a high-pressure multiphase diamond inclusion, entrapped at 420–440 km depth and 1450 ± 50 K, which matches exactly the geochemical patterns of the HIMU-rich OIBs. Since the HIMU component is variably sampled by almost all OIBs, our finding implies that the transition zone causes a major overprint of the geochemical features of mantle plumes. Some mantle plumes, like those feeding Bermuda, St Helena, Tubuai and Mangaia, appear to be dominated by this source. Furthermore, our finding highlights the importance of the transition zone in highly incompatible element budget of the mantle

Discovery of davemaoite, CaSiO3-perovskite, as a mineral from the lower mantle.

Calcium silicate perovskite, CaSiO3, is arguably the most geochemically important phase in the lower mantle, because it concentrates elements that are incompatible in the upper mantle, including the heat-generating elements thorium and uranium, which have half-lives longer than the geologic history of Earth. We report CaSiO3-perovskite as an approved mineral (IMA2020-012a) with the name davemaoite. The natural specimen of davemaoite proves the existence of compositional heterogeneity within the lower mantle. Our observations indicate that davemaoite also hosts potassium in addition to uranium and thorium in its structure. Hence, the regional and global abundances of davemaoite influence the heat budget of the deep mantle, where the mineral is thermodynamically stable

Probing optical anisotropy of nanometer-thin van der waals microcrystals by near-field imaging.

Most van der Waals crystals present highly anisotropic optical responses due to their strong in-plane covalent bonding and weak out-of-plane interactions. However, the determination of the polarization-dependent dielectric constants of van der Waals crystals remains a nontrivial task, since the size and dimension of the samples are often below or close to the diffraction limit of the probe light. In this work, we apply an optical nano-imaging technique to determine the anisotropic dielectric constants in representative van der Waals crystals. Through the study of both ordinary and extraordinary waveguide modes in real space, we are able to quantitatively determine the full dielectric tensors of nanometer-thin molybdenum disulfide and hexagonal boron nitride microcrystals, the most-promising van der Waals semiconductor and dielectric. Unlike traditional reflection-based methods, our measurements are reliable below the length scale of the free-space wavelength and reveal a universal route for characterizing low-dimensional crystals with high anisotropies

Real-Space Infrared Spectroscopy of Ferroelectric Domain Walls in Multiferroic h‑(Lu,Sc)FeO3

We employ synchrotron-based near-field infrared spectroscopy to image the phononic properties of ferroelectric domain walls in hexagonal (h) Lu0.6Sc0.4FeO3, and we compare our findings with a detailed symmetry analysis, lattice dynamics calculations, and prior models of domain-wall structure. Rather than metallic and atomically thin as observed in the rare-earth manganites, ferroelectric walls in h-Lu0.6Sc0.4FeO3 are broad and semiconducting, a finding that we attribute to the presence of an A-site substitution-induced intermediate phase that reduces strain and renders the interior of the domain wall nonpolar. Mixed Lu/Sc occupation on the A site also provides compositional heterogeneity over micron-sized length scales, and we leverage the fact that Lu and Sc cluster in different ratios to demonstrate that the spectral characteristics at the wall are robust even in different compositional regimes. This work opens the door to broadband imaging of physical and chemical heterogeneity in ferroics and represents an important step toward revealing the rich properties of these flexible defect states

Ultrahigh-Quality Infrared Polaritonic Resonators Based on Bottom-Up-Synthesized van der Waals Nanoribbons.

van der Waals nanomaterials supporting phonon polariton quasiparticles possess extraordinary light confinement capabilities, making them ideal systems for molecular sensing, thermal emission, and subwavelength imaging applications, but they require defect-free crystallinity and nanostructured form factors to fully showcase these capabilities. We introduce bottom-up-synthesized α-MoO3 structures as nanoscale phonon polaritonic systems that feature tailorable morphologies and crystal qualities consistent with bulk single crystals. α-MoO3 nanoribbons serve as low-loss hyperbolic Fabry-Pérot nanoresonators, and we experimentally map hyperbolic resonances over four Reststrahlen bands spanning the far- and mid-infrared spectral range, including resonance modes beyond the 10th order. The measured quality factors are the highest from phonon polaritonic van der Waals structures to date. We anticipate that bottom-up-synthesized polaritonic van der Waals nanostructures will serve as an enabling high-performance and low-loss platform for infrared optical and optoelectronic applications

Phase‐Change Hyperbolic Heterostructures for Nanopolaritonics: A Case Study of hBN/VO₂

Unlike conventional plasmonic media, polaritonic van der Waals (vdW) materials hold promise for active control of light-matter interactions. The dispersion relations of elementary excitations such as phonons and plasmons can be tuned in layered vdW systems via stacking using functional substrates. In this work, infrared nanoimaging and nanospectroscopy of hyperbolic phonon polaritons are demonstrated in a novel vdW heterostructure combining hexagonal boron nitride (hBN) and vanadium dioxide (VO₂). It is observed that the insulator-to-metal transition in VO₂ has a profound impact on the polaritons in the proximal hBN layer. In effect, the real-space propagation of hyperbolic polaritons and their spectroscopic resonances can be actively controlled by temperature. This tunability originates from the effective change in local dielectric properties of the VO₂ sublayer in the course of the temperature-tuned insulator-to-metal phase transition. The high susceptibility of polaritons to electronic phase transitions opens new possibilities for applications of vdW materials in combination with strongly correlated quantum materials. Keywords: hexagonal boron nitride; phase-change materials; polariton

Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general

Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general