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Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures. 

Along with the increasing interest in MoS 2 as a promising electronic material, there is also an increasing demand for nanofabrication technologies that are compatible with this material and other relevant layered materials. In addition, the development of scalable nanofabrication approaches capable of directly producing MoS 2 device arrays is an imperative task to speed up the design and commercialize various functional MoS 2 -based devices. The desired fabrication methods need to meet two critical requirements. First, they should minimize the involvement of resist-based lithography and plasma etching processes, which introduce unremovable contaminations to MoS 2 structures. Second, they should be able to produce MoS 2 structures with in-plane or out-of-plane edges in a controlled way, which is key to increase the usability of MoS 2 for various device applications. Here, we introduce an inkjet-defined site-selective (IDSS) method that meets these requirements. IDSS includes two main steps: (i) inkjet printing of microscale liquid droplets that define the designated sites for MoS 2 growth, and (ii) site-selective growth of MoS 2 at droplet-defined sites. Moreover, IDSS is capable of generating MoS 2 with different structures. Specifically, an IDSS process using deionized (DI) water droplets mainly produces in-plane MoS 2 features, whereas the processes using graphene ink droplets mainly produce out-of-plane MoS 2 features rich in exposed edges. Using out-of-plane MoS 2 structures, we have demonstrated the fabrication of miniaturized on-chip lithium ion batteries, which exhibit reversible lithiation/delithiation capacity. This IDSS method could be further expanded as a scalable and reliable nanomanufacturing method for generating miniaturized on-chip energy storage devices. 

We report on system integration of plasmonic nanoparticles and a few-layered molybdenum disulfide (M0S2) photoconductive nanochannel sheet on a silicon substrate. Plasma-assisted electrostatic bonding and van der Waals bonding are employed to create a high-sensitivity photoelectronic biosensor for immunological analysis. 

The ability to detect low‐abundance proteins in human body fluids plays a critical role in proteomic research to achieve a comprehensive understanding of protein functions and early‐stage disease diagnosis to reduce mortality rates. Ultrasensitive (sub‐fM), rapid, simple “mix‐and‐read” plasmonic colorimetric biosensing of large‐size (≈180 kDa) proteins in biofluids using an ultralow‐noise multilayer molybdenum disulfide (MoS₂) photoconducting channel is reported here. With its out‐of‐plane structure optimized to minimize carrier scattering, the multilayer MoS₂channel operated under near‐infrared illumination enables the detection of a subtle plasmonic extinction shift caused by antigen‐induced nanoprobe aggregation. The demonstrated biosensing strategy allows quantifying carcinoembryonic antigen in unprocessed whole blood with a dynamic range of 10⁶, a sample‐to‐answer time of 10 min, and a limit of detection of 0.1–3 pg mL⁻¹, which is ≈100‐fold more sensitive than the clinical‐standard enzyme‐linked immunosorbent assays. The biosensing methodology can be broadly used to realize timely personalized diagnostics and physiological monitoring of diseases in point‐of‐care settings.