Search

The bulk phase of transition metal nitrides (TMNs) has long been a subject of extensive investigation due to their utility as coating materials, electrocatalysts, and diffusion barriers, attributed to their high conductivity and refractory properties. Downscaling TMNs into two-dimensional (2D) forms would provide valuable members to the existing 2D materials repertoire, with potential enhancements across various applications. Moreover, calculations have anticipated the emergence of uncommon physical phenomena in TMNs at the 2D limit. In this study, we use the atomic substitution approach to synthesize 2D W5N6 with tunable thicknesses from tens of nanometers down to 2.9 nm. The obtained flakes exhibit high crystallinity and smooth surfaces. Electrical measurements on 15 samples show an average electrical conductivity of 161.1 S/cm, which persists while thickness decreases from 45.6 nm to 2.9 nm. The observed weak gate tuning effect suggests the semimetallic nature of the synthesized 2D W5N6. Further investigation into the conversion mechanism elucidates the crucial role of chalcogen vacancies in the precursor for initiating the reaction and strain in propagating the conversion. Our work introduces a desired semimetallic crystal to the 2D material library with mechanistic insights for future design of the synthesis., Comment: 5 figures in main text

Unlocking the vast potential of optical sensing technology has long been hindered by the challenges of achieving fast, sensitive, and broadband photodetection at ambient temperatures. In this review, we summarize recent progress in the study of nonlinear photocurrent in topological quantum materials, and its application in broadband photodetection without the use of p-n junction based semiconductor diodes. The intrinsic quadratic transverse current-input voltage relation is used to rectify the alternating electric field from incident radio, terahertz or infrared waves into a direct current, without a bias voltage and at zero magnetic field. We review novel photocurrents in several material systems, including topological Weyl semimetals, chiral crystals, ferroelectric materials, and low dimensional topological insulators. These quantum materials hold tremendous promise for broadband high-frequency rectification and photodetection, featuring substantial responsivity and detectivity., Comment: Review article, 24 pages + 19 figures

Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational ellipsometry approach based on a conventional tabletop optical microscope and a deep learning model called EllipsoNet. Without any prior knowledge about the multilayer substrates, EllipsoNet can predict the complex refractive indices of thin films on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers-Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials within complex photonic structures or optoelectronic devices.