Publications

Significance The gas-sensing performance of graphene could be remarkably enhanced by incorporating dopants into its lattice based on theoretical calculations. However, to date, experimental progress on boron-doped graphene (BG) is still very scarce. Here, we achieved the controlled growth of large-area, high-crystallinity BG sheets and shed light on their electronic features associated with boron dopants at the atomic scale. As a proof-of-concept, it is demonstrated that boron doping in graphene could lead to a much enhanced sensitivity when detecting toxic gases (e.g. NO 2 ). Our results will open up new avenues for developing high-performance sensors able to detect trace amount of molecules. In addition, other new fascinating properties can be exploited based on as-synthesized large-area BG sheets.

Graphene exhibits electrical and optical properties promising for future applications in ultra-fast photonics [1]. High carrier mobility and Fermi velocity [2, 3] combined with its constant absorption over the visible wavelength range to the near-infrared [4] potentially allow its application for photodetection over a broad wavelength spectrum, operating at high frequencies. However, absorption being 2.3% per monolayer [4], responsivity of these devices is rather low [5, 6]. Here we show that by combining graphene-based photodetectors with metal-nanostructures, plasmonic effects lead to an increased responsivity.

Accurate and efficient prediction of the optical properties of two-dimensional (2D) materials is crucial for photonic applications but remains a challenging task due to discrepancies between theoretical and experimental approaches. Here, we present a physics-guided machine learning (ML) framework for accelerated screening of 2D materials. It combines first-principles density functional theory (DFT) calculations and graph neural network models backed by experimental spectroscopic validation and Cauchy-model integration. Within this framework, we collected a database of more than 1000 monolayers of transition metal dichalcogenides (TMDs) along with their optical properties. We also proposed a universal method for defining a physically meaningful thickness of two-dimensional structures, which enabled a correction of the optical properties obtained from PBE-based density functional theory. Using the collected database, we developed a Cauchy-model-based machine learning model to calculate refractive indices in the near-infrared (near-IR) region (755-1064 nm). The developed approach reflects correlations between the atomic structures of monolayers and their optical properties, which are confirmed by extensive testing against independent 2D materials databases. In this way, our ML-driven strategy offers a powerful tool for the rapid screening of novel monolayer materials with tailored optical functionalities, significantly accelerating the discovery and design of next-generation photonic materials. As an application, we further demonstrate how high-refractive-index candidates such as Bi₂Te₂Se enable enhanced field confinement and long crosstalk lengths in monolayer waveguides, highlighting their promise for integrated photonics.

Excitons, pairs of electrons and holes, undergo a Bose-Einstein condensation at low temperatures. An important platform to study excitons is double-layer two-dimensional electron gases, with two parallel planes of electrons and holes separated by a thin insulating layer. Lowering this separation ( d ) strengthens the exciton binding energy, however, leads to the undesired interlayer tunneling, resulting in annihilation of excitons. Here, we report the observation of a sequences of robust exciton condensates (ECs) in double bilayer graphene twisted to ~ 10° with no insulating mid-layer. The large momentum mismatch between two graphene layers suppresses interlayer tunneling, reaching a d ~ 0.334 nm. Measuring the bulk and edge transport, we find incompressible states corresponding to ECs when both layers are in half-filled N = 0, 1 Landau levels (LLs). Theoretical calculations suggest that the low-energy charged excitation of ECs can be meron-antimeron or particle-hole pair, which relies on both LL index and carrier type. Our results establish a novel platform with extreme coupling strength for studying quantum bosonic phase.

Spin-valley properties in two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDC) has attracted significant interest due to the possible applications in quantum computing. Spin-valley properties can be exploited in TMDC quantum dot (QD) with well-resolved energy levels. This requires smaller QDs, especially in material systems with heavy carrier effective mass e.g. TMDCs and silicon. Device architectures employed for TMDC QDs so far have difficulty achieving smaller QDs. Therefore, an alternative approach in the device architecture is needed. Here, we propose a multilayer device architecture to achieve a gate-defined QD in TMDC with a relatively large energy splitting on the QD. We provide a range of device dimensions and dielectric thicknesses and its correlation with the QD energy splitting. The device architecture is modeled realistically. Moreover, we show that all the device parameters used in modeling are experimentally achievable. These studies lay the foundation for future work toward spin-valley qubits in TMDCs. The successful implementation of these device architectures will drive the technological development of 2D materials-based quantum technologies.

No abstract is provided for this article.

2D crystals with noncentrosymmetric structures exhibit piezoelectric properties that show great potential for applications in energy conversion and electromechanical devices. Quantitative visualization of piezoelectric field spatial distribution is expected to offer a better understanding of macroscopic piezoelectricity, yet remains to be realized. Here, a technique of mapping piezoelectric potential on 2D materials bubbles based on the measurements of surface potential using kelvin probe force microscope is reported. By using odd number of layers hexagonal boron nitride and MoS 2 nanobubbles, strain‐induced piezoelectric potential profiles are quantitatively visualized on the bubbles. The obtained piezoelectric coefficient is 3.4 ± 1.2 × 10 −10 C m −1 and 3.3 ± 0.2 × 10 −10 C m −1 for hBN and MoS 2 , in agreement with the values reported. On the contrary, homogeneous distribution of surface potential is measured on even number of layers crystals bubbles where the crystal's inversion symmetry is restored. Using such technique, in situ visualization of photogenerated charge carrier separation under piezoelectric potential is also achieved, which offers a platform of investigating the coupling between piezoelectricity and photoelectric effect, and an approach of tuning piezoelectric field. The present work should aid the understanding of local piezoelectric potential and its various affecting factors including substrate doping and external stimuli, and give insights for designing piezoelectric nanodevices based on 2D nanobubbles.

No abstract is provided for this article.

No abstract is provided for this article.

Since first developed, the conducting materials in wireless communication and electromagnetic interference (EMI) shielding devices have been primarily made of metal-based structures. Here, we present a graphene-assembled film (GAF) that can be used to replace copper in such practical electronics. The GAF-based antennas present strong anticorrosive behavior. The GAF ultra-wideband antenna covers the frequency range of 3.7 GHz to 67 GHz with the bandwidth (BW) of 63.3 GHz, which exceed ~110% than the copper foil-based antenna. The GAF Fifth Generation (5G) antenna array features a wider BW and lower sidelobe level compared with that of copper antennas. EMI shielding effectiveness (SE) of GAF also outperforms copper, reaching up to 127 dB in the frequency range of 2.6 GHz to 0.32 THz, with a SE per unit thickness of 6,966 dB/mm. We also confirm that GAF metamaterials exhibit promising frequency selection characteristics and angular stability as flexible frequency selective surfaces.