The recently discovered spin defects in hexagonal boron nitride (hBN), a layered van der Waals material, have great potential in quantum sensing. However, the photoluminescence and the contrast of the optically detected magnetic resonance (ODMR) of hBN spin defects are relatively low so far, which limits their sensitivity. Here we report a record-high ODMR contrast of 46% at room temperature, and simultaneous enhancement of the photoluminescence of hBN spin defects by up to 17-fold by the surface plasmon of a gold-film microwave waveguide. Our results are obtained with shallow boron vacancy spin defects in hBN nanosheets created by low-energy He + ion implantation, and a gold-film microwave waveguide fabricated by photolithography. We also explore the effects of microwave and laser powers on the ODMR, and improve the sensitivity of hBN spin defects for magnetic field detection. Our results support the promising potential of hBN spin defects for nanoscale quantum sensing.

counterparts. [2] Therefore, 2D materials are ideal for flexible optoelectronics and have the potential to be used in the next-generation ultrathin electronic and optoelectronic devices. [1] The concept of 2D materials was first realized when graphene was found in 2004. [4] Graphene has attracted extensive attention for its excellent electrical, optical, and mechanical properties. [4][5][6] They have been investigated for various technological applications, including spintronics, sensors, optoelectronics, supercapacitors, and solar cells, etc. [5,7] Besides graphene, other 2D materials, such as h-BN, phosphorene, silicene, germanene, and transition metal dichalcogenides (molybdenum disulfide (MoS 2 ), molybdenum diselenide (MoSe 2 ), tungsten disulfide (WS 2 ), and tungsten diselenide (WSe 2 ), etc.), have been studied extensively in recent years. [1,[8][9][10][11] The thickness of single-layer 2D materials is usually on the order or less than 1 nm. At the same time, their lateral sizes could reach much larger size (from microns to even inches), and 2D materials can be transferred to different substrates before subsequent processing or follow-up measurements for characterizations or device applications.Strain engineering is a promising way to tune the electrical, electrochemical, magnetic, and optical properties of 2D materials, with the potential to achieve high-performance 2D-material-based devices ultimately. This review discusses the experimental and theoretical results from recent advances in the strain engineering of 2D materials. Some novel methods to induce strain are summarized and then the tunable electrical and optical/optoelectronic properties of 2D materials via strain engineering are highlighted, including particularly the previously less-discussed strain tuning of superconducting, magnetic, and electrochemical properties. Also, future perspectives of strain engineering are given for its potential applications in functional devices. The state of the survey presents the ever-increasing advantages and popularity of strain engineering for tuning properties of 2D materials. Suggestions and insights for further research and applications in optical, electronic, and spintronic devices are provided.

With the development of Raman spectroscopy and the expansion of its application domains, conventional methods for spectral data analysis have manifested many limitations. Exploring new approaches to facilitate Raman spectroscopy and analysis has become an area of intensifying focus for research. It has been demonstrated that machine learning techniques can more efficiently extract valuable information from spectral data, creating unprecedented opportunities for analytical science. This paper outlines traditional and more recently developed statistical methods that are commonly used in machine learning (ML) and ML‐algorithms for different Raman spectroscopy‐based classification and recognition applications. The methods include Principal Component Analysis, K‐Nearest Neighbor, Random Forest, and Support Vector Machine, as well as neural network‐based deep learning algorithms such as Artificial Neural Networks, Convolutional Neural Networks, etc. The bulk of the review is dedicated to the research advances in machine learning applied to Raman spectroscopy from several fields, including material science, biomedical applications, food science, and others, which reached impressive levels of analytical accuracy. The combination of Raman spectroscopy and machine learning offers unprecedented opportunities to achieve high throughput and fast identification in many of these application fields. The limitations of current studies are also discussed and perspectives on future research are provided.

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Articles you may be interested inShot noise of charge current in a quantum dot responded by rotating and oscillating magnetic fields J. Appl. Phys. 116, 093702 (2014); 10.1063/1.4894294 Y-shape spin-separator for two-dimensional group-IV nanoribbons based on quantum spin hall effect Appl. Phys. Lett.The effect of phase-transition from the quantum-spin-hall to the band-insulator phase on the transport through a three-terminal U-shape spin-separator has been computationally investigated via non-equilibrium green function formalism. Two-dimensional group-IV elements have been comprehensively appraised as the device material. The device separates the unpolarized current injected at the source-terminal into nearly 100% spin-polarized currents of the opposite polarities at the two drain terminals. The phase-transition activated by the electric-field orthogonal to the device is shown to extensively influence the current magnitude and its spin-polarization, and the effect is stronger for materials with smaller intrinsic spin-orbit coupling. Moreover, the device length and the area under field are shown to critically affect the device characteristics on phase change. It is shown that the same device can be operated as a spin-filter by inducing phase-transition selectively in the channel. The results are important for designing spin-devices from Group-IV monolayers. V C 2014 AIP Publishing LLC. [http://dx.