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Creators/Authors contains: "Pu, Haihui"

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  1. Abstract

    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.

     
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  2. Abstract

    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.

     
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    Free, publicly-accessible full text available August 15, 2024
  3. 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. 
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  5. Real-time lead ion monitoring for drinking water is in an urgent demand, due to the high biotoxicity of lead. We fabricated a reduced graphene oxide (rGO) percolation network based field-effect transistor (FET) by using an easy and scalable micromolding-in-capillary method for lead ion detection in water. The percolation theory analysis elucidates that the required GO mass concentration for a 2D continuum connection converges at a predictable value. Guided by the theoretical analysis, the prepared rGO network was constructed with 1–4 layers of rGO flakes and exhibits comparable electrical properties with single-layer rGO devices. A thin Al2O3layer was deposited on the device to isolate the analyte from the FET device. With the specific L-Glutathione reduced (GSH) probe, the sensor can reach a limit of detection (LOD) in ppb-level to lead ions. In addition, good selectivity and the high sensing response to Pb2+concentrations around 15 ppb (maximum contaminant level of lead for drinking water, US Environmental Protection Agency) suggest our sensor holds great potential for lead ion monitoring in drinking water.

     
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