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• W2896903349 abstract "Optical cross-reactive sensor arrays have recently been proven to be a powerful tool for high-throughput bioanalytes identification. Nevertheless, identification and classification of microbes, especially using microbial lysates as the analytes, still is a great challenge due to their complex composition. Herein, we achieve this goal by using luminogens featuring aggregation-induced emission characteristics (AIEgens) and graphene oxide (GO) to construct a microbial lysate responsive fluorescent sensor array. The combination of AIEgen with GO not only reduces the background signal but also induces the competition interactions among AIEgen, microbial lysates, and GO, which highly improves the discrimination ability of the sensor array. As a result, six microbes, including two fungi, two Gram-positive bacteria, and two Gram-negative bacteria are precisely identified. Thus, this work provides a new way to design safer and simpler sensor arrays for the discrimination of complex analytes." @default.
• W2896903349 created "2018-10-26" @default.
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• W2896903349 date "2018-10-23" @default.
• W2896903349 modified "2023-10-16" @default.
• W2896903349 title "Fluorescent Sensor Array for Highly Efficient Microbial Lysate Identification through Competitive Interactions" @default.
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• W2896903349 doi "https://doi.org/10.1021/acssensors.8b00650" @default.
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