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Risk management for drinking water often requires continuous monitoring of various toxins in flowing water. While they can be readily integrated with existing water infrastructure, two-dimensional (2D) electronic sensors often suffer from device-to-device variations due to the lack of an effective strategy for identifying faulty devices from preselected uniform devices based on electronic properties alone, resulting in sensor inaccuracy and thus slowing down their real-world applications. Here, we report the combination of wet transfer, impedance and noise measurements, and machine learning to facilitate the scalable nanofabrication of graphene-based field-effect transistor (GFET) sensor arrays and the efficient identification of faulty devices. Our sensors were able to perform real-time detection of heavy-metal ions (lead and mercury) andE. colibacteria simultaneously in flowing tap water. This study offers a reliable quality control protocol to increase the potential of electronic sensors for monitoring pollutants in flowing water.

 

Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS₂nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10³. The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS₂channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS₂as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb²⁺in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.

 

The development of vascularized tissue is a substantial challenge within the field of tissue engineering and regenerative medicine. Studies have shown that positively-charged microspheres exhibit dual-functions: (1) facilitation of vascularization and (2) controlled release of bioactive compounds. In this study, gelatin-coated microspheres were produced and processed with either EDC or transglutaminase, two crosslinkers. The results indicated that the processing stages did not significantly impact the size of the microspheres. EDC and transglutaminase had different effects on surface morphology and microsphere stability in a simulated colonic environment. Incorporation of EGM and TGM into bioink did not negatively impact bioprintability (as indicated by density and kinematic viscosity), and the microspheres had a uniform distribution within the scaffold. These microspheres show great potential for tissue engineering applications.

 

Solid-state lithium batteries (SSLBs) are promising candidates for replacing traditional liquid-based Li-ion batteries and revolutionizing battery systems for electric vehicles and portable devices. However, longstanding issues such as form factors, interfacial contact resistance, balance between ion conductivity and mechanical strength, and manufacturing processability limit their applications. In this review we present how advanced printing technologies can help to mitigate typical problems in main components of SSLBs and improve device performance. We first introduce the common printing techniques for energy storage devices, then focus on the issues and corresponding printing strategies for anodes, cathodes, and solid-state electrolytes to guide the construction of energy-dense, free-form SSLBs. The features and effects of the printed structures are emphasized, as well. We conclude by discussing the problems associated with printing technologies and the potential research directions for printed solid-state batteries. 

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.