Key Takeaways & Executive Findings
- •• Carbon-based multivariable chemical sensors using CNTs/graphene and FET transducers enable analyte recognition with a single sensing material, overcoming limitations of monovariable sensors and complex sensor arrays. • The review provides a comprehensive analysis of multivariable sensing mechanisms and design criteria, highlighting the role of pattern recognition algorithms in enhancing selectivity and classification. • Innovative multivariable extraction schemes integrated with advanced algorithms demonstrate practical applications in environmental monitoring, industrial production, and medical diagnostics. • The work underscores the potential of carbon-based sensors for low-cost, compact, and portable real-time monitoring in IoT and industrial internet contexts.
Abstract
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.
1. Introduction
In recent years, chemical sensors have been widely employed for environmental monitoring, industrial production, and medical diagnostics [1–6]. There are two major test prerequisites for the practical application of sensors within complex chemical environments. Firstly, the accurate measurement of one component, and secondly, the classification and identification of multiple or all chemical components. In order to meet the former requirement, it is necessary for the sensor to demonstrate a high degree of selectivity, that is, to be resistant to interference from other analytes present in the environment. In order to satisfy the second requirement, the sensor must demonstrate a differentiated response to a range of analytes and possess the capacity to incorporate pattern recognition algorithms for the identification of analyte species and concentrations. While gas chromatograph, mass spectrometer, and high-performance liquid chromatography are capable of fulfilling both of these testing needs, their considerable size and intricate operational requirements restrict their deployment in portable, real-time monitoring applications [7].
With the development of the Internet of Things (IoT) and the industrial internet, there is a growing demand for low-cost, compact chemical sensors for the construction of sensor networks [8, 9]. Alongside the increasing pursuit of healthy lifestyles, these sensors are expected to facilitate applications such as the detection of harmful substances and the early pre-diagnosis of diseases in home settings [10–12]. Researchers have developed a range of chemical sensors, including optical [13, 14], electrochemical [15, 16], catalytic combustion [17, 18], chemoresistive [19–22], and field-effect transistor (FET) [23, 24]. Catalytic combustion and chemiresistive sensors have achieved commercialization due to their simple manufacturing processes and reliable performance, effectively addressing the challenges of low-cost and large-scale deployment that traditional analytical instruments struggle to overcome in IoT and industrial internet applications [25–27]. Benefiting from advances in microelectronics technology, FET-based chemical sensors have emerged as promising candidates for portable and highly sensitive detection, offering the potential for integration with signal processing and wireless communication modules.
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Lin Shi, Jian Song, Yu Wang, Heng Fu, Kingsley Patrick-Iwuanyanwu, Lei Zhang, Charles H. Lawrie, Jianhua Zhang (2025). Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition. Nano-Micro Letters. https://doi.org/10.1007/s40820-025-01741-0
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Frequently Asked Questions
What are carbon-based multivariable chemical sensors?
Carbon-based multivariable chemical sensors are devices that use carbon nanomaterials like carbon nanotubes or graphene as the sensing material and field-effect transistors as transducers. They generate multiple outputs from a single sensing material to create a comprehensive sensing space, enabling the classification and identification of multiple analytes without the need for complex sensor arrays.
How do multivariable sensors improve analyte recognition compared to conventional sensors?
Conventional monovariable sensors suffer from response overlap due to interference from coexisting analytes. Multivariable sensors overcome this by producing multiple independent signals from a single sensing material, which, when combined with pattern recognition algorithms, allow for more accurate discrimination and quantification of analytes in complex environments.
What are the advantages of using carbon nanotubes and graphene in these sensors?
Carbon nanotubes and graphene offer well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. Their high surface-to-volume ratio and excellent charge carrier mobility make them ideal for high-performance chemical sensors, enabling sensitive and rapid detection.
What are the main applications of carbon-based multivariable chemical sensors?
These sensors are used in environmental monitoring, industrial production, and medical diagnostics. They are particularly suited for portable and real-time monitoring applications, such as detecting harmful substances and early disease diagnosis, due to their low cost, compact size, and compatibility with IoT networks.
What is the significance of pattern recognition algorithms in these sensors?
Pattern recognition algorithms are crucial for interpreting the multivariable data generated by the sensors. They enable the classification and identification of analyte species and concentrations, enhancing selectivity and reducing false positives, which is essential for practical applications in complex environments.
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