Can Orbital-Selective Néel Transitions Survive Strong Nonlocal Electronic Correlations?

Spin- or orbital-selective behaviors in correlated electron materials offer rich promise for spintronics or orbitronics phenomena and applications deriving from them. Strong local electronic Coulomb correlations might lead to an orbital-selective Mott state, characterized by the coexistence of localized electrons in some orbitals with itinerant electrons in others. Nonlocal electronic fluctuations are much more entangled in orbital space than the local ones. For this reason, finding orbital-selective phenomena related to nonlocal correlations, such as orbital-selective magnetic transitions, is a challenge. In this Letter, we investigate possibilities to realize an orbital-selective Néel transition (OSNT). We illustrate that stabilizing this state requires a decoupling of magnetic fluctuations in different orbitals, which can only be realized in the absence of Hund's exchange coupling. On the basis of two-orbital calculations for a Hubbard model with different bandwidths we show that the proposed OSNT can be found all the way from the weak to the strong coupling regime. In the weak coupling regime the transition is governed by a Slater mechanism and thus occurs first for the narrow orbital. At strong coupling a Heisenberg mechanism of the OSNT sets in, and the transition occurs first for the wide orbital. Remarkably, at intermediate values of the interaction we find a nontrivial regime of the OSNT, where the Slater mechanism leads to a Néel transition occurring first for the wide orbital. Our work suggests strategies for searching for orbital-selective Néel ordering in real materials in view of possible spin-orbitronics applications.

Local and Nonlocal Electronic Correlations at the Metal-Insulator Transition in the Two-Dimensional Hubbard Model.

Elucidating the physics of the single-orbital Hubbard model in its intermediate-coupling regime is a key missing ingredient to our understanding of metal-insulator transitions in real materials. Using recent nonperturbative many-body techniques that are able to interpolate between the spin-fluctuation-dominated Slater regime at weak coupling and the Mott insulator at strong coupling, we obtain the momentum-resolved spectral function in the intermediate regime and disentangle the effects of antiferromagnetic fluctuations and local electronic correlations in the formation of an insulating state. This allows us to identify the Slater and Heisenberg regimes in the phase diagram, which are separated by a crossover region of competing spatial and local electronic correlations. We identify the crossover regime by investigating the behavior of the local magnetic moment, shedding light on the formation of the insulating state at intermediate couplings.

Eliminating Orbital Selectivity from the Metal-Insulator Transition by Strong Magnetic Fluctuations.

The orbital-selective electronic behavior is one of the most remarkable manifestations of strong electronic correlations in multiorbital systems. A prominent example is the orbital-selective Mott transition (OSMT), which is characterized by the coexistence of localized electrons in some orbitals, and itinerant electrons in other orbitals. The state-of-the-art theoretical description of the OSMT in two- and three-dimensional systems is based on local nonperturbative approximations to electronic correlations provided by dynamical mean-field theory or slave spin method. In this work we go beyond this local picture and focus on the effect of spatial collective electronic fluctuations on the OSMT. To this aim, we consider a half-filled Hubbard-Kanamori model on a cubic lattice with two orbitals that have different bandwidths. We show that strong magnetic fluctuations that are inherent in this system prevent the OSMT and favor the Néel transition that occurs at the same critical temperature for both orbitals.

Orbital Isotropy of Magnetic Fluctuations in Correlated Electron Materials Induced by Hund's Exchange Coupling.

Characterizing nonlocal magnetic fluctuations in materials with strong electronic Coulomb interactions remains one of the major outstanding challenges of modern condensed matter theory. In this Letter, we address the spatial symmetry and orbital structure of magnetic fluctuations in perovskite materials. To this aim, we develop a consistent multiorbital diagrammatic extension of dynamical mean-field theory, which we apply to an anisotropic three-orbital model of cubic t_{2g} symmetry. We find that the form of spatial spin fluctuations is governed by the local Hund's coupling. For small values of the coupling, magnetic fluctuations are anisotropic in orbital space, which reflects the symmetry of the considered t_{2g} model. Large Hund's coupling enhances collective spin excitations, which mixes orbital and spatial degrees of freedom, and magnetic fluctuations become orbitally isotropic. Remarkably, this effect can be seen only in two-particle quantities; single-particle observables remain anisotropic for any value of the Hund's coupling. Importantly, we find that the orbital isotropy can be induced both at half filling and for the case of four electrons per lattice site, where the magnetic instability is associated with different, antiferromagnetic and ferromagnetic, modes, respectively.

Hyperglycemia exacerbates ischemic stroke not through increased generation of hydrogen peroxide.

Diabetes is one of the significant risk factors for ischemic stroke. Hyperglycemia exacerbates the pathogenesis of stroke, leading to more extensive cerebral damage and, as a result, to more severe consequences. However, the mechanism whereby the hyperglycemic status in diabetes affects biochemical processes during the development of ischemic injury is still not fully understood. In the present work, we record for the first time the real-time dynamics of H2O2 in the matrix of neuronal mitochondria in vitro in culture and in vivo in the brain tissues of rats during development of ischemic stroke under conditions of hyperglycemia and normal glucose levels. To accomplish this, we used a highly sensitive HyPer7 biosensor and a fiber-optic interface technology. We demonstrated that a high glycemic status does not affect the generation of H2O2 in the tissues of the ischemic core, while significantly exacerbating the consequences of pathogenesis. For the first time using Raman microspectroscopy approach, we have shown how a sharp increase in the blood glucose level increases the relative amount of reduced cytochromes in the mitochondrial electron transport chain in neurons under normal conditions in awake mice.

Real-time fiber-optic recording of acute-ischemic-stroke signatures.

We present an experimental framework and methodology for in vivo studies on rat stroke models that enable a real-time fiber-optic recording of stroke-induced hydrogen peroxide and pH transients in ischemia-affected brain areas. Arrays of reconnectable implantable fiber probes combined with advanced optogenetic fluorescent protein sensors are shown to enable a quantitative multisite time-resolved study of oxidative-stress and acidosis buildup dynamics as the key markers, correlates and possible drivers of ischemic stroke. The fiber probes designed for this work provide a wavelength-multiplex forward-propagation channel for a spatially localized, dual-pathway excitation of genetically encoded fluorescence-protein sensors along with a back-propagation channel for the fluorescence return from optically driven fluorescence sensors. We show that the spectral analysis of the fiber-probe-collected fluorescence return provides means for a high-fidelity autofluorescence background subtraction, thus enhancing the sensitivity of real-time detection of stroke-induced transients and significantly reducing measurement uncertainties in in vivo acute-stroke studies as inherently statistical experiments operating with outcomes of multiply repeated measurements on large populations of individually variable animal stroke models.

In vivo dynamics of acidosis and oxidative stress in the acute phase of an ischemic stroke in a rodent model.

Ischemic cerebral stroke is one of the leading causes of death and disability in humans. However, molecular processes underlying the development of this pathology remain poorly understood. There are major gaps in our understanding of metabolic changes that occur in the brain tissue during the early stages of ischemia and reperfusion. In particular, it is generally accepted that both ischemia (I) and reperfusion (R) generate reactive oxygen species (ROS) that cause oxidative stress which is one of the main drivers of the pathology, although ROS generation during I/R was never demonstrated in vivo due to the lack of suitable methods. In the present study, we record for the first time the dynamics of intracellular pH and H2O2 during I/R in cultured neurons and during experimental stroke in rats using the latest generation of genetically encoded biosensors SypHer3s and HyPer7. We detect a buildup of powerful acidosis in the brain tissue that overlaps with the ischemic core from the first seconds of pathogenesis. At the same time, no significant H2O2 generation was found in the acute phase of ischemia/reperfusion. HyPer7 oxidation in the brain was detected only 24 h later. Comparison of in vivo experiments with studies on cultured neurons under I/R demonstrates that the dynamics of metabolic processes in these models significantly differ, suggesting that a cell culture is a poor predictor of metabolic events in vivo.

Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks.

Error-related potentials are considered an important neuro-correlate for monitoring human intentionality in decision-making, human-human, or human-machine interaction scenarios. Multiple methods have been proposed in order to improve the recognition of human intentions. Moreover, current brain-computer interfaces are limited in the identification of human errors by manual tuning of parameters (e.g., feature/channel selection), thus selecting fronto-central channels as discriminative features within-subject. In this paper, we propose the inclusion of error-related potential activity as a generalized two-dimensional feature set and a Convolutional Neural Network for classification of EEG-based human error detection. We evaluate this pipeline using the BNCI2020 - Monitoring Error-Related Potential dataset obtaining a maximum error detection accuracy of 79.8% in a within-session 10-fold cross-validation modality, and outperforming current state of the art.