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From left to right and top to bottom, the five Ge₂H₂⁺structures are shown:trans, monobridged, butterfly, germylidene, and linear. 

Abstract The topological features of optical vortices have been opening opportunities for free-space and on-chip photonic technologies, e.g., for multiplexed optical communications and robust information transport. In a parallel but disjoint effort, polar anisotropic van der Waals nanomaterials supporting hyperbolic phonon polaritons (HP 2 s) have been leveraged to drastically boost light-matter interactions. So far HP 2 studies have been mainly focusing on the control of their amplitude and scale features. Here we report the generation and observation of mid-infrared hyperbolic polariton vortices (HP 2 Vs) associated with reconfigurable topological charges. Spiral-shaped gold disks coated with a flake of hexagonal boron nitride are exploited to tailor spin–orbit interactions and realise deeply subwavelength HP 2 Vs. The complex interplay between excitation spin, spiral geometry and HP 2 dispersion enables robust reconfigurability of the associated topological charges. Our results reveal unique opportunities to extend the application of HP 2 s into topological photonics, quantum information processing by integrating these phenomena with single-photon emitters, robust on-chip optical applications, sensing and nanoparticle manipulation. 

Abstract Violet phosphorus (VP) is garnering attention for its appealing physical properties and potential applications in optoelectronics. A comprehensive investigation of the photodegradation and thermal effects of exfoliated VP on SiO₂/Si substrates is presented. The degradation rate of VP is strongly influenced by the wavelength and exposure duration of light. Light illumination of VP above the bandgap leads to faster degradation, attributed to interactions with reactive oxygen species. Power‐dependent photoluminescence (PL) measurements at low temperature (T = 4 K) show neutral exciton (X⁰) and trion (T) intensities linearly increase with excitation power, while the energy difference between peak energies decreases. The T/X⁰spectral weight ratio increases from 0.28 at 300 K to 0.69 at 4 K, suggesting enhanced T formation due to reduced phonon scattering. Temperature‐dependent Raman is used to investigate the phonon properties of VP. Tracking peak positions of 9 Raman modes with temperature, the linear first‐order temperature coefficient is obtained and found to be linear for all modes. The results provide a deeper understanding of VP's degradation behavior and implications for optoelectronic applications. 

Hydrogen-doped perovskites can be reconfigured by electrical pulses to take on all essential functions necessary for artificial intelligence hardware. 

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia , habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence. 

Point defects, such as oxygen vacancies, control the physical properties of complex oxides, relevant in active areas of research from superconductivity to resistive memory to catalysis. In most oxide semiconductors, electrons that are associated with oxygen vacancies occupy the conduction band, leading to an increase in the electrical conductivity. Here we demonstrate, in contrast, that in the correlated-electron perovskite rare-earth nickelates, R NiO 3 ( R is a rare-earth element such as Sm or Nd), electrons associated with oxygen vacancies strongly localize, leading to a dramatic decrease in the electrical conductivity by several orders of magnitude. This unusual behavior is found to stem from the combination of crystal field splitting and filling-controlled Mott–Hubbard electron–electron correlations in the Ni 3 d orbitals. Furthermore, we show the distribution of oxygen vacancies in NdNiO 3 can be controlled via an electric field, leading to analog resistance switching behavior. This study demonstrates the potential of nickelates as testbeds to better understand emergent physics in oxide heterostructures as well as candidate systems in the emerging fields of artificial intelligence. 

Habituation and sensitization represent nonassociative learning mechanisms in both non‐neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular‐like learning in oxygen‐deficient NiO_xdevices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiO_xunder sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next‐generation neuroinspired computing hardware.