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ACS Sens. 2018 Sep 28;3(9):1782-1788. doi: 10.1021/acssensors.8b00510. Epub 2018 Sep 4.

A Single Input Multiple Output (SIMO) Variation-Tolerant Nanosensor.

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Department of Aeronautics and Astronautics , Stanford University , Stanford , California 94305 United States.
Institute of Photonics and Nanotechnology , Vilnius University , Vilnius , LT 10257 , Lithuania.


Successful transition to commercialization and practical implementation of nanotechnology innovations may very well need device designs that are tolerant to the inherent variations and imperfections in all nanomaterials including carbon nanotubes, graphene, and others. As an example, a single-walled carbon nanotube network based gas sensor is promising for a wide range of applications such as environment, industry, and biomedical and wearable devices due to its high sensitivity, fast response, and low power consumption. However, a long-standing issue has been the production of extremely high purity semiconducting nanotubes, thereby contributing to the delay in the market adoption of those sensors. Inclusion of even less than 0.1% of metallic nanotubes, which is inevitable, is found to result in a significant deterioration of sensor-to-sensor uniformity. Acknowledging the coexistence of metallic and semiconducting nanotubes as well as all other possible imperfections, we herein present a novel variation-tolerant sensor design where the sensor response is defined by a statistical Gaussian measure in contrast to a traditional deterministic approach. The single input and multiple output data are attained using multiport electrodes fabricated over a relatively large area single nanotube ensemble. The data processing protocol discards outlier data points and the origin of the outliers is investigated. Both the experimental demonstration and complementary analytical modeling support the hypothesis that the statistical analysis of the device can strengthen the credibility of the sensor constructed using nanomaterials with any imperfections. The proposed strategy can also be applied to physical, radiation, and biosensors as well as other electronic devices.


carbon nanotube; gas sensor; printed electronics; sensor variation; statistical analysis; variation tolerance


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