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Inclusions of breyite (previously known as walstromite-structured CaSiO3) in diamond are usually interpreted as retrogressed CaSiO3 perovskite trapped in the transition zone or the lower mantle. However, the thermodynamic stability field of breyite does not preclude its crystallization together with diamond under upper-mantle conditions (6–10 GPa). The possibility of breyite forming in subducted sedimentary material through the reaction CaCO3 + SiO2 = CaSiO3 + C + O2 was experimentally evaluated in the CaO–SiO2–C–O2 ± H2O system at 6–10 GPa, 900–1500 ∘C and oxygen fugacity 0.5–1.0 log units below the Fe–FeO (IW) buffer. One experimental series was conducted in the anhydrous subsystem and aimed at determining the melting temperature of the aragonite–coesite (or stishovite) assemblage. It was found that melting occurs at a lower temperature (∼1500 ∘C) than the decarbonation reaction, which indicates that breyite cannot be formed from aragonite and silica under anhydrous conditions and an oxygen fugacity above IW – 1. In the second experimental series, we investigated partial melting of an aragonite–coesite mixture under hydrous conditions at the same pressures and redox conditions. The melting temperature in the presence of water decreased strongly (to 900–1200 ∘C), and the melt had a hydrous silicate composition. The reduction of melt resulted in graphite crystallization in equilibrium with titanite-structured CaSi2O5 and breyite at ∼1000 ∘C. The maximum pressure of possible breyite formation is limited by the reaction CaSiO3 + SiO2 = CaSi2O5 at ∼8 GPa. Based on the experimental results, it is concluded that breyite inclusions found in natural diamond may be formed from an aragonite–coesite assemblage or carbonate melt at 6–8 GPa via reduction at high water activity.
In magic angle twisted bilayer graphene, transport, thermodynamic and spectroscopic experiments pinpoint at a competition between distinct low-energy states with and without electronic order, as well as a competition between localized and delocalized charge carriers. In this study, we utilize Dynamical Mean Field Theory (DMFT) on the topological heavy Fermion (THF) model of twisted bilayer graphene to investigate the emergence of electronic correlations and long-range order in the absence of strain. We explain the nature of emergent insulating and correlated metallic states, as well as transitions between them driven by three central phenomena: (i) the formation of local spin and valley isospin moments around 100K, (ii) the ordering of the local isospin moments around 10K, and (iii) a cascadic redistribution of charge between localized and delocalized electronic states upon doping. At integer fillings, we find that low energy spectral weight is depleted in the symmetric phase, while we find insulating states with gaps enhanced by exchange coupling in the zero-strain ordered phases. Doping away from integer filling results in distinct metallic states: a "bad metal" above the ordering temperature, where coherence of the low-energy electronic excitations is suppressed by scattering off the disordered local moments, and a "good metal" in the ordered states with coherence of quasiparticles facilitated by isospin order. Upon doping, there is charge transfer between the localized and delocalized orbitals of the THF model such that they get periodically filled and emptied in between integer fillings. This charge reshuffling manifests itself in cascades of doping-induced Lifshitz transitions, local spectral weight redistributions and periodic variations of the electronic compressibility ranging from nearly incompressible to negative.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.