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• W4313644900 endingPage "141364" @default.
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• W4313644900 abstract "Conductive metal–organic frameworks (C-MOF) materials possess a high absorbability and structural tunability. However, their low sensitivity and poor specificity limit their applications. Constructing PN heterostructures with other semiconductor materials can regulate the C-MOF energy band structure, reduce particle stacking, and boost gas-sensing performance. Here, we synthesize Cu3(HHTP)2 (C-MOF) material in-situ onto SnS2 nanolayers to form PN heterojunctions that facilitate high-performance NH3 sensing, a four times higher response compared to pristine MOF-based sensors, ultralow detection limits (experimental: 125 ppb; theoretical: 9.84 ppb), and improved selectivity against various rotten gases. Machine learning is applied to analyse the effects of temperature, time, humidity, and category on the sensing response, achieving 78.5 % accuracy in predicting meat storage time. By adding a Bluetooth module and cloud-based signal analysis, we provide a proof-of-concept for a portable, fast response, non-destructive remote gas sensing device for real-time food-freshness monitoring deployable at any stage along the food supply chain." @default.
• W4313644900 created "2023-01-07" @default.
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• W4313644900 date "2023-02-01" @default.
• W4313644900 modified "2023-10-16" @default.
• W4313644900 title "Wireless food-freshness monitoring and storage-time prediction based on ammonia-sensitive MOF@SnS2 PN heterostructure and machine learning" @default.
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• W4313644900 doi "https://doi.org/10.1016/j.cej.2023.141364" @default.
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