Shi Lin, Song Jian, Wang Yu, Fu Heng, Patrick-Iwuanyanwu Kingsley, Zhang Lei, Lawrie Charles H, Zhang Jianhua
School of Microelectronics, Shanghai University, Shanghai, 201800, People's Republic of China.
Sino-Swiss Institute of Advanced Technology (SSIAT), Shanghai University, Shanghai, 201899, People's Republic of China.
Nanomicro Lett. 2025 May 3;17(1):246. doi: 10.1007/s40820-025-01741-0.
Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.
在最近几十年里，碳基化学传感器技术取得了显著进展。然而，在增强分析物识别能力方面仍存在重大机遇，尤其是在复杂环境中。传统的单变量传感器存在固有局限性，例如容易受到共存分析物的干扰，这会导致响应重叠。尽管传感器阵列通过对多种传感材料进行改性，为分析物识别提供了一种潜在解决方案，但其实际应用受到复杂的材料改性过程的限制。在这种背景下，多变量化学传感器已成为一种有前途的替代方案，它能够产生多个输出，以构建用于分析物识别的综合传感空间，同时使用单一传感材料。在各种碳基材料中，碳纳米管（CNTs）和石墨烯由于其成熟的批量制造工艺、优异的电学性能和出色的传感能力，已成为构建高性能化学传感器的理想候选材料。本文综述了碳基多变量化学传感器的进展，重点关注以碳纳米管/石墨烯作为传感材料以及以场效应晶体管作为用于分析物识别的换能器。讨论涵盖了这些传感器的基本方面，包括传感材料、传感器架构、性能指标、模式识别算法和多变量传感机制。此外，本文还强调了创新的多变量提取方案及其与先进模式识别算法集成时的实际应用。
