The perovskite ruthenate CaRuO3 has attracted considerable interest due to reports of possible non-Fermi-liquid behavior and its proximity to a magnetic quantum critical point, yet its ground state and electronic structure remain enigmatic. Here, we report measurements of the Fermi surface and quasiparticle dispersion in CaRuO3 through a combination of oxide molecular beam epitaxy and in situ angle-resolved photoemission spectroscopy.

We construct examples of translationally invariant solvable models of strongly correlated metals, composed of lattices of Sachdev-Ye-Kitaev dots with identical local interactions. These models display crossovers as a function of temperature into regimes with local quantum criticality and marginal-Fermi-liquid behavior. In the marginal-Fermi-liquid regime, the dc resistivity increases linearly with temperature over a broad range of temperatures. By generalizing the form of interactions, we also construct examples of non-Fermi liquids with critical Fermi surfaces.

We analyze the dynamical response of a two-dimensional system of itinerant fermions coupled to a scalar boson φ, which undergoes a continuous transition towards nematic order with a d-wave form factor. We consider two cases: (a) when φ is a soft collective mode of fermions near a Pomeranchuk instability, and (b) when it is an independent critical degree of freedom, such as a composite spin order parameter. In both cases, the order parameter is not a conserved quantity and the d-wave fermionic polarization Π(q,Ω) remains finite even at q=0.

The atomic thickness of two-dimensional materials provides a unique opportunity to control their electrical 1 and optical 2 properties as well as to drive the electronic phase transitions 3 by electrostatic doping. The discovery of two-dimensional magnetic materials 4-10 has opened up the prospect of the electrical control of magnetism and the realization of new functional devices 11 . A recent experiment based on the linear magneto-electric effect has demonstrated control of the magnetic order in bilayer CrI3 by electric fields 12 .

Aberration-corrected optics have made electron microscopy at atomic resolution a widespread and often essential tool for characterizing nanoscale structures. Image resolution has traditionally been improved by increasing the numerical aperture of the lens (α) and the beam energy, with the state-of-the-art at 300 kiloelectronvolts just entering the deep sub-ångström (that is, less than 0.5 ångström) regime. Two-dimensional (2D) materials are imaged at lower beam energies to avoid displacement damage from large momenta transfers, limiting spatial resolution to about 1 ångström.

We demonstrate the fabrication of ∼1.08 μm deep microfluidic cavities with characteristic size as large as 7 mm × 11 mm or 11 mm diameter, using a silicon-glass anodic bonding technique that does not require posts to act as separators to define cavity height. Since the phase diagram of 3He is significantly altered under confinement, posts might act as pinning centers for phase boundaries.

Tunneling of electrons into a two-dimensional electron system is known to exhibit an anomaly at low bias, in which the tunneling conductance vanishes due to a many-body interaction effect. Recent experiments have measured this anomaly between two copies of the half-filled Landau level as a function of in-plane magnetic field, and they suggest that increasing spin polarization drives a deeper suppression of tunneling.

Advances in nanomechanics within recent years have demonstrated an always expanding range of devices, from top-down structures to appealing bottom-up MoS2 and graphene membranes, used for both sensing and component-oriented applications. One of the main concerns in all of these devices is frequency noise, which ultimately limits their applicability. This issue has attracted a lot of attention recently, and the origin of this noise remains elusive to date.

Neural-network-based machine learning is emerging as a powerful tool for obtaining phase diagrams when traditional regression schemes using local equilibrium order parameters are not available, as in many-body localized (MBL) or topological phases. Nevertheless, instances of machine learning offering new insights have been rare up to now. Here we show that a single feed-forward neural network can decode the defining structures of two distinct MBL phases and a thermalizing phase, using entanglement spectra obtained from individual eigenstates.

We show experimentally that the spin direction of the spin current generated by spin-orbit interactions within a ferromagnetic layer can be reoriented by turning the magnetization direction of this layer. We do this by measuring the fieldlike component of spin-orbit torque generated by an exchange-biased Fe95Gd5 thin film and acting on a nearby Co40Fe40B20 layer. The relative angle of the Co40Fe40B20 and Fe95Gd5 magnetic moments is varied by applying an external magnetic field.