This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
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A Carbon Capture and Utilization Process for the Production of Solid Carbon Materials from Atmospheric CO<sub>2</sub> - Part 2: Carbon Characterization. [PDF]
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Correction: Celery-derived porous carbon materials for superior performance supercapacitors. [PDF]
Liu S, Xu Y, Wu J, Huang J.
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Degradation of Bisphenol A by Nitrogen-Rich ZIF-8-Derived Carbon Materials-Activated Peroxymonosulfate. [PDF]
Tang X +5 more
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A Carbon Capture and Utilization Process for the Production of Solid Carbon Materials from Atmospheric CO<sub>2</sub> - Part 1: Process Performance. [PDF]
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Low-temperature dechlorination of polyvinyl chloride (PVC) for production of H2 and carbon materials using liquid metal catalysts. [PDF]
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