مقاله انگلیسی رایگان در مورد سنسور گرافین چند منظوره برای تشخیص سیگنال های محیطی با استفاده از تکنیک جدا سازی – الزویر 2019

1- Introduction

2- Experimental details

3- Results and discussion

4- Conclusion

References

Abstract

Graphene is one of good candidates for application to multifunctional sensors due to its unique material properties, extremely high surface-to-volume ratio and ability to be effectively co-integrated on various substrates. However, the electrical properties of graphene are sensitive to most typical environment signals such as temperature, light, humidity, and gas. Therefore, to develop graphene-based multifunctional sensors, it is essential to identify multiple environment information values from the output characteristics of a single graphene sensor. In this study, we developed a temperature–illuminance multifunctional graphene sensor that can identify both temperature and illuminance values. We also propose a decoupling technique to enable the precise identification of temperature and illuminance values from the output characteristics of a sensor that has cross-sensitivity to the both inputs. This decoupling technique is developed based on the different gate-voltage dependence of the temperature- and illumination-induced modulations of the graphene conductivity. The results show that graphene can be used as a material for multifunctional sensors when combined with an appropriate decoupling technique for identifying various types of environment signals.

Introduction

Nowadays, various types of sensors are used for environment monitoring, energy management, health care, feedback control of machines, and so on. To obtain more various environment information, it is generally reguired to increass the number and kinds of individual sensors in sensor systems like a multi sensors module, and it involves the increase of the size, complexity, and cost of sensor modules or systems. An integrated multifunctional sensor can detect the multiple environment signals with relatively less increase of the size, complexity, and cost. Recently, techniques for integration of multiple sensors on a single chip are studied for cost-effective smart sensor applications such as smart home monitoring, road activity monitoring, and weather monitoring [1-4]. For example, Roozeboom et al. demonstrated a compact multifunctional sensor chip (2 × 2 mm2 size) in which 9 individual sensors are integrated for measuring 7 types of environment signals [2]. These multifunctional integrated sensors have been fabricated on Si substrates by using conventional sensing materials and Si based micro-electro-mechanical-system (MEMS) processes. Graphene is an attractive material to develop various types of sensors due to its outstanding properties such as extremely high surface-to-volume ratio with high mobility, superior flexibility, and capability of effective co-integration on various substrates with relatively simple fabrication process [5-7]. These properties have enabled sensor applications of graphene in artificial electronic skin [8-9], stretchable and flexible sensors [10], human motion sensors [11-13], and so on. Graphene is also widely studied for development of environment sensors because it is sensivite to most of typical environment signals like temperature, illuminance, humidity, pressure, gas, and so on. [5-7, 14-20]. Furthermore, since the single material, graphene, has sensitivity to multiple environment signals simultaenously, graphene has a great potential for being used as a sensing material for multifunctional environment sensors. However, the electrical properties of graphene that are sensitive to multiple environment signals can cause a ‘cross-sensitivity’ problem that make it hard to identify multiple environment information values from the output characteristic of a single graphene sensor [4]. Therefore, for realization of graphene-based multifunctional sensors, it is essential to develop a decoupling technique for precisely identifying multiple environment signals from a single graphene sensor that has the cross-sensitivity problem.