Title

Spray Deposition Modeling Of Carbon Nano-Inks For Structural Health Monitoring

Keywords

Multiwalled carbon nanotubes; Spray evaporation deposition; Strain sensor; Structural health monitoring

Abstract

Carbon nanotubes (CNTs) have become prominent smart materials due to their unique electrical, mechanical, and sensing properties. The great properties of carbon nanotubes represent a potential for developing a piezo-resistive strain sensor with smart structure for structural health monitoring (SHM) applications. The objective of this study is to fabricate multi-walled carbon nanotubes (MWCNT) sensors by a novel digital controlled spray deposition process. In this study, a new spraying-evaporation deposition process that uses a 12-array bubble jet nozzle attached to a digital x-y plotter combined with a heated substrate which induces evaporation of the solvent was employed to produce carbon nanotube strain sensors. This system is able to produce MWCNT sensors with variable thickness in defined locations and geometries. The effectiveness of the fabricated MWCNTs as strain sensors was investigated. Four point probe apparatus was used to measure the electrical resistivity of MWCNTs sensors. For sensors with 30 printed CNT layers, the sheet resistivity of carbon nanotubes could be as low as 95 Ω/sq. Real-time strain response of the MWCNTs sensors under tensile load was also studied. The printed carbon nanotube strain sensor has a gauge factor of 5.02. These research results indicated that this new spray deposition process is effective for the fabrication of a CNT strain sensor for structural health monitoring applications.

Publication Date

1-1-2016

Publication Title

Earth and Space 2016: Engineering for Extreme Environments - Proceedings of the 15th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments

Number of Pages

1072-1078

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1061/9780784479971.103

Socpus ID

85025701957 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85025701957

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