Piezoresistive type graphene nano platelet sensor for SHM application in structural components

Abstract: The paper proposes a new theoretical model describing the strain-sensing response of smart bricks, a novel class of piezoresistive smart sensors for structural health monitoring of masonry structures. The proposed model is experimentally validated by carrying out electromechanical tests on single smart bricks. An illustrative application is also presented to exemplify the use of the model when smart bricks are embedded within a masonry wall. Results demonstrate that the novel theoretical model is able to accurately reproduce the bricks’ response when subjected to compressive loads and is also effective for strain estimation and damage detection in a typical structural setting.

Abstract: A carbon nanotube polymer material was used to form a piezoresistive strain sensor for structural health monitoring applications. The polymer improves the interfacial bonding between the nanotubes. Previous single walled carbon nanotube buckypaper sensors produced distorted strain measurements because the van der Waals attraction force allowed axial slipping of the smooth surfaces of the nanotubes. The polymer sensor uses larger multi-walled carbon nanotubes which improve the strain transfer, repeatability and linearity of the sensor. An electrical model of the nanotube strain sensor was derived based on electrochemical impedance spectroscopy and strain testing. The model is useful for designing nanotube sensor systems. A biomimetic artificial neuron was developed by extending the length of the sensor. The neuron is a long continuous strain sensor that has a low cost, is simple to install and is lightweight. The neuron has a low bandwidth and adequate strain sensitivity. The neuron sensor is particularly useful for detecting large strains and cracking, and can reduce the number of channels of data acquisition needed for the health monitoring of large structures.

Abstract: We report the fast, low-cost, simple fabrication of a large chemically-converted graphene (CCG) films by spray deposition of graphene oxide (GO)–hydrazine dispersions. The GO–hydrazine dispersion was prepared by mixing GO dispersion with excess amount of hydrazine monohydrate. By spray deposition on preheated substrate, the creation of the thin film and the reduction of GO to CCG were carried out simultaneously. The prepared CCG films had a low sheet resistance of 2.2 × 10 3 Ω □ −1 and a high transmittance of 84% at a wavelength of 550 nm. Atomic force microscope images clearly showed continuous films resulting from the overlap of graphene sheets and a uniform surface morphology with root mean square about 1 nm.

Abstract: Epoxy based polymer nano-composite was prepared by dispersing graphite nano-platelets (GNPs) using two different techniques: three-roll mill (3RM) and sonication combined with high speed shear mixing (Soni_hsm). The influence of addition of GNPs on the electrical and thermal conductivity, fracture toughness and storage modulus of the nano-composite was investigated. The GNP/epoxy prepared by 3RM technique showed a maximum electrical conductivity of 1.8 × 10 −03 S/m for 1.0 wt% which is 3 orders of magnitude higher than those prepared by Soni_hsm. The percentage of increase in thermal conductivity was only 11% for 1.0 wt% and 14% for 2.0 wt% filler loading. Dynamic mechanical analysis results showed 16% increase in storage modulus for 0.5 wt%, although the Tg did not show any significant increase. Single edge notch bending (SENB) fracture toughens ( K IC ) measurements were carried out for different weight percentage of the filler content. The toughening effect of GNP was most significant at 1.0 wt% loading, where a 43% increase in K IC was observed. Among the two different dispersion techniques, 3RM process gives the optimum dispersion where both electrical and mechanical properties are better.

Abstract: In order to build upon the exceptional interest for flexible sensors based on carbon nanotube networks (CNNs), the field requires high device-to-device reproducibility Inkjet printing has provided outstanding results for flexible ohmic sensors in terms of reproducibility of their resistance However, the reproducibility of the sensitivity, the most critical parameter for sensing application, has been only marginally assessed In the present paper, CNN-based resistive strain sensors fabricated by inkjet-printing on flexible Ethylene Tetrafluoroethylene (EFTE) sheets are presented The variability on the device initial resistance is studied for 5 different batches of sensors from 3 to 72 devices each The variability ranges between 84% and 43% depending on the size of the batches, with a 20% average An 8-device batch with 15% variability on initial resistance is further studied for variability on the strain and thermal sensitivity Standard deviation values are found to be as low as 16% on the strain sensitivity and 8% on the temperature sensitivity Moreover, the devices are hysteresis free, a rare achievement for CNT strain sensors on plastics

Abstract: Graphene/Polypropylene nanocomposites were prepared at different filler loading and different average surface diameter 5, 15 and 25μm of graphene nanoplatelets by using Haake Minilab mixer at 180C and rotor speed 50rpm. Besides, Haake MiniJet is used to obtain dumbbell shape specimen. The effect of filler loading and average surface area of filler in PP/GnP composites on Raman spectrum and tensile properties were studied. Raman spectrum of graphene particles indicate three major spectrums such as D, G and 2D band. In addition, PP/GnP composites shows the Raman band shift quite strong by increasing GnP loading. In general, increased of graphene nanoplatelets loading have increased the value of modulus of elasticity, whereas tensile strength, elongation at break of composites reduced.