Carbon-based sensors based on low-dimensional carbon nanomaterials demonstrate broad application prospects in fields such as wearable devices and human–computer interaction, owing to the exceptional properties of carbon nanomaterials. Driven by advancements in artificial intelligence (AI) technology, these sensors are progressively evolving from single-function devices into intelligent systems. This article highlights five types of AI-oriented carbon-based sensors, discussing the integration and application of artificial intelligence with carbon-based sensing technology. To address challenges like insufficient external information acquisition capability and system redundancy caused by the separation of sensing and computation, we introduce the multimodal sensing system and the sensing-computation integrated architecture: the former enhances information dimensionality through collaborative perception of multiple physical signals, while the latter seamlessly integrates signal acquisition with intelligent processing. Ultimately, AI-empowered carbon-based sensing systems not only improve perception accuracy and processing efficiency but also establish the foundation for autonomous intelligent sensing systems, demonstrating substantial prospects for next-generation smart hardware.
Citation: Yi Zhang, Lu-Yu Zhao, Yu-Tao Li, Ye-Liang Wang. Recent progress in low dimensional carbon nanomaterials sensors and their integration with artificial intelligence technologies[J]. AIMS Materials Science, 2025, 12(5): 1041-1064. doi: 10.3934/matersci.2025048
Carbon-based sensors based on low-dimensional carbon nanomaterials demonstrate broad application prospects in fields such as wearable devices and human–computer interaction, owing to the exceptional properties of carbon nanomaterials. Driven by advancements in artificial intelligence (AI) technology, these sensors are progressively evolving from single-function devices into intelligent systems. This article highlights five types of AI-oriented carbon-based sensors, discussing the integration and application of artificial intelligence with carbon-based sensing technology. To address challenges like insufficient external information acquisition capability and system redundancy caused by the separation of sensing and computation, we introduce the multimodal sensing system and the sensing-computation integrated architecture: the former enhances information dimensionality through collaborative perception of multiple physical signals, while the latter seamlessly integrates signal acquisition with intelligent processing. Ultimately, AI-empowered carbon-based sensing systems not only improve perception accuracy and processing efficiency but also establish the foundation for autonomous intelligent sensing systems, demonstrating substantial prospects for next-generation smart hardware.
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Yi Zhang, Lu-Yu Zhao, Yu-Tao Li, Ye-Liang Wang. Recent progress in low dimensional carbon nanomaterials sensors and their integration with artificial intelligence technologies[J]. AIMS Materials Science, 2025, 12(5): 1041-1064. doi: 10.3934/matersci.2025048
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