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1. Epitaxial hexagonal boron nitride with high quantum efficiency

   https://doi.org/10.1063/5.0142242  

   Laleyan, David_Arto; Lee, Woncheol; Zhao, Ying; Wu, Yuanpeng; Wang, Ping; Song, Jun; Kioupakis, Emmanouil; Mi, Zetian (May 2023, APL Materials)

   Two-dimensional (2D) hexagonal boron nitride (h-BN) is one of the few materials showing great promise for light emission in the far ultraviolet (UV)-C wavelength, which is more effective and safer in containing the transmission of microbial diseases than traditional UV light. In this report, we observed that h-BN, despite having an indirect energy bandgap, exhibits a remarkably high room-temperature quantum efficiency (∼60%), which is orders of magnitude higher than that of other indirect bandgap material, and is enabled by strong excitonic effects and efficient exciton-phonon interactions. This study offers a new approach for the design and development of far UV-C optoelectronic devices as well as quantum photonic devices employing 2D semiconductor active regions. 

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2. The role of oxygen incorporation in Ni (111) substrates on the growth of hexagonal boron nitride monolayers

   https://doi.org/10.1088/1361-6528/acfaa8  

   Li, Yuan; Gomez, Hector; Tran, Jason; He, Yanwei; Shou, Chengyun; Yang, Tianchen; Wei, Peng; Lake, Roger K; Liu, Jianlin (October 2023, Nanotechnology)

   Abstract Reliable and controllable growth of two-dimensional (2D) hexagonal boron nitride (h-BN) is essential for its wide range of applications. Substrate engineering is one of the critical factors that influence the growth of the epitaxial h-BN films. Here, we report the growth of monolayer h-BN on Ni (111) substrates incorporated with oxygen atoms via molecular beam epitaxy. It was found that the increase of incorporated oxygen concentration in the Ni substrate through a pretreatment process prior to the h-BN growth step would have an adverse effect on the morphology and growth rate of 2D h-BN. Under the same growth condition, h-BN monolayer coverage decreases exponentially as the amount of oxygen incorporated into Ni (111) increases. Density functional theory calculations and climbing image nudged elastic band (CI-NEB) method reveal that the substitutional oxygen atoms can increase the diffusion energy barrier of B and N atoms on Ni (111) thereby inhibiting the growth of h-BN films. As-grown large-area h-BN monolayer films and fabricated Al/h-BN/Ni (MIM) nanodevices were comprehensively characterized to evaluate the structural, optical and electrical properties of high-quality monolayers. Direct tunneling mechanism and high breakdown strength of ∼11.2 MV cm⁻¹are demonstrated for the h-BN monolayers grown on oxygen-incorporated Ni (111) substrates, indicating that these films have high quality. This study provides a unique example that heterogeneous catalysis principles can be applied to the epitaxy of 2D crystals in solid state field. Similar strategies can be used to grow other 2D crystalline materials, and are expected to facilitate the development of next generation devices based on 2D crystals. 

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3. Mechanical properties of cubic boron nitride and diamond at dynamical pressure and temperature

   https://doi.org/10.1063/5.0172885  

   Kastuar, Srihari M.; Liu, Zhong-Li; Najmaei, Sina; Ekuma, Chinedu E. (December 2023, Applied Physics Letters)

   We report the mechanical properties of cubic boron nitride (c-BN) and diamond under the combined impact of dynamical pressure and temperature, calculated using ab initio molecular dynamics. Our study revealed a pronounced sensitivity of the mechanical properties of c-BN to applied pressure. Notably, c-BN undergoes a brittle-to-ductile transition at ∼220 GPa, consistent across various dynamical temperatures, while diamond exhibits no such transition. Furthermore, the Vickers hardness profile for c-BN closely mirrors that of diamond across a spectrum of temperature–pressure conditions, highlighting c-BN's significant mechanical robustness. These results underscore the superior resilience and adaptability of c-BN compared to diamond, suggesting its potential as an ideal candidate for applications in extreme environments. 

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4. Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware

   https://doi.org/10.1038/s41699-022-00328-2  

   Xie, Jing; Afshari, Sahra; Sanchez_Esqueda, Ivan (July 2022, npj 2D Materials and Applications)

   Abstract Recent studies of resistive switching devices with hexagonal boron nitride (h-BN) as the switching layer have shown the potential of two-dimensional (2D) materials for memory and neuromorphic computing applications. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. These characteristics are of interest for the implementation of neuromorphic computing and machine learning hardware based on memristor crossbars. However, existing demonstrations of h-BN memristors focus on single isolated device switching properties and lack attention to fundamental machine learning functions. This paper demonstrates the hardware implementation of dot product operations, a basic analog function ubiquitous in machine learning, using h-BN memristor arrays. Moreover, we demonstrate the hardware implementation of a linear regression algorithm on h-BN memristor arrays. 

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5. Dot-product computation and logistic regression with 2D hexagonal-Boron Nitride (h-BN) memristor arrays

   https://doi.org/10.1088/2053-1583/acdfe1  

   Afshari, Sahra; Radhakrishnan, Sritharini; Xie, Jing; Musisi-Nkambwe, Mirembe; Meng, Jian; He, Wangxin; Seo, Jae-sun; Sanchez Esqueda, Ivan (July 2023, 2D Materials)

   Abstract This work reports on the hardware implementation of analog dot-product operation on arrays of 2D hexagonal boron nitride (h-BN) memristors. This extends beyond previous work that studied isolated device characteristics towards the application of analog neural network accelerators based on 2D memristor arrays. The wafer-level fabrication of the memristor arrays is enabled by large-area transfer of CVD-grown few-layer (8 layers) h-BN films. Individual devices achieve an on/off ratio of >10, low voltage operation (~0.5 Vset/Vreset), good endurance (>6,000 programming steps), and good retention (>104 s). The dot-product operation shows excellent linearity and repeatability, with low read energy consumption (~200 aJ to 20 fJ per operation), with minimal error and deviation over various measurement cycles. Moreover, we present the implementation of a stochastic logistic regression algorithm in 2D h-BN memristor hardware for the classification of noisy images. The promising resistive switching characteristics, performance of dot-product computation, and successful demonstration of logistic regression in h-BN memristors signify an important step towards the integration of 2D materials for next-generation neuromorphic computing systems. 

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