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基于肖特基结中费米能级大幅移动的筛层调制的超快超灵敏气体传感器。

Ultrafast and Ultrasensitive Gas Sensors Derived from a Large Fermi-Level Shift in the Schottky Junction with Sieve-Layer Modulation.

机构信息

Department of Physics, National Taiwan University , Taipei 10617, Taiwan.

出版信息

ACS Appl Mater Interfaces. 2016 Jul 13;8(27):17382-8. doi: 10.1021/acsami.6b03172. Epub 2016 Jun 30.

Abstract

Gas sensors play an important role in numerous fields, covering a wide range of applications, including intelligent systems and detection of harmful and toxic gases. Even though they have attracted much attention, the response time on the order of seconds to minutes is still very slow. To circumvent the existing problems, here, we provide a seminal attempt with the integration of graphene, semiconductor, and an addition sieve layer forming a nanocomposite gas sensor with ultrahigh sensitivity and ultrafast response. The designed sieve layer has a suitable band structure that can serve as a blocking layer to prevent transfer of the charges induced by adsorbed gas molecules into the underlying semiconductor layer. We found that the sensitivity can be reduced to the parts per million level, and the ultrafast response of around 60 ms is unprecedented compared with published graphene-based gas sensors. The achieved high performance can be interpreted well by the large change of the Fermi level of graphene due to its inherent nature of the low density of states and blocking of the sieve layer to prevent charge transfer from graphene to the underlying semiconductor layer. Accordingly, our work is very useful and timely for the development of gas sensors with high performance for practical applications.

摘要

气体传感器在众多领域中发挥着重要作用,涵盖了广泛的应用,包括智能系统和有害及有毒气体的检测。尽管它们已经引起了广泛关注,但响应时间仍在秒到分钟的数量级,非常缓慢。为了解决现有问题,我们在这里首次尝试将石墨烯、半导体和附加筛网层集成在一起,形成具有超高灵敏度和超快响应的纳米复合气体传感器。设计的筛网层具有合适的能带结构,可以作为阻挡层,防止被吸附气体分子诱导的电荷转移到下面的半导体层。我们发现,灵敏度可以降低到百万分之几的水平,与已发表的基于石墨烯的气体传感器相比,超快响应时间约为 60 毫秒,这是前所未有的。由于石墨烯固有低态密度和筛网层对阻止电荷从石墨烯转移到下面的半导体层的特性,石墨烯费米能级的大幅变化可以很好地解释所实现的高性能。因此,我们的工作对于开发用于实际应用的高性能气体传感器非常有用和及时。

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