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.
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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
x devices 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 NiOx under 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. -
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Abstract From a fundamental science perspective, black phosphorus (BP) is a canonical example of a material that possesses fascinating surface and electronic properties. It has extraordinary in‐plane anisotropic electrical, optical, and vibrational states, as well as a tunable band gap. However, instability of the surface due to chemical degradation in ambient conditions remains a major impediment to its prospective applications. Early studies were limited by the degradation of black phosphorous surfaces in air. Recently, several robust strategies have been developed to mitigate these issues, and these novel developments can potentially allow researchers to exploit the extraordinary properties of this material and devices made out of it. Here, the fundamental chemistry of BP degradation and the tremendous progress made to address this issue are extensively reviewed. Device performances of encapsulated BP are also compared with nonencapsulated BP. In addition, BP possesses sensitive anisotropic photophysical surface properties such as excitons, surface plasmons/phonons, and topologically protected and Dirac semi‐metallic surface states. Ambient degradation as well as any passivation method used to protect the surface could affect the intrinsic surface properties of BP. These properties and the extent of their modifications by both the degradation and passivation are reviewed.
