Interfacial shear stress transfer of a monolayer graphene on top of a polymer substrate subjected to uniaxial tension was investigated by a cohesive zone model integrated with a shear-lag model. Strain distribution in the graphene flake was found to behave in three stages in general, bonded, damaged, and debonded, as a result of the interfacial stress transfer. By fitting the cohesive-shear-lag model to our experimental results, the interface properties were identified including interface stiffness (74 Tpa/m), shear strength (0.50 Mpa), and mode II fracture toughness (0.08 N/m). Parametric studies showed that larger interface stiffness and/or shear strength can lead to better stress transfer efficiency, and high fracture toughness can delay debonding from occurring. 3D finite element simulations were performed to capture the interfacial stress transfer in graphene flakes with realistic geometries. The present study can provide valuable insight and design guidelines for enhancing interfacial shear stress transfer in nanocomposites, stretchable electronics and other applications based on graphene and other 2D nanomaterials.

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This paper applies the concept of spectral profile division multiplexing to track each Bragg wavelength shift in a serially multiplexed fiber Bragg grating (FBG) network. Each sensor in the network is uniquely characterized by its own reflected spectrum shape, thus spectral overlapping is allowed in the wavelength domain. In contrast to the previous literature, spectral distortion caused by multiple reflections and spectral shadowing between FBG sensors, that occur in serial topology sensor networks, are considered in the identification algorithm. To detect the Bragg wavelength shift of each FBG, a nonlinear optimization function based on the output spectrum is constructed and a modified dynamic multi-swarm particle swarm optimizer is employed. The multiplexing approach is experimentally demonstrated on data from multiplexed sensor networks with up to four sensors. The wavelength prediction results show that the method can efficiently interrogate the multiplexed network in these overlapped situations. Specifically, the maximum error in a fully overlapped situation in the specific four sensor network demonstrated here was only 110 pm. A more general analysis of the prediction error and guidelines to optimize the sensor network are the subject of future work.

In this paper we develop a methodology that uses in-plane strain measurements to determine outof-plane deflection for woven fabrics using an optical fiber based sensor network. A multiplexed fiber Bragg grating (FBG) network is used to collect strain at discrete locations in the fabric. To simplify the problem a circularly shaped two-dimensional woven fabric material under a spherical indenter load is studied. A finite element (FE) model of the fabric behavior, derived from benchmark testing, was used to help develop reconstruction algorithms, some of which account for slipping of the fabric at the clamped boundaries. Due to the large deflections, complex material behavior of the fabric and slipping of the fabric at the outer boundaries, a modified empirical approximation approach was found to be the optimal choice for the deflection reconstruction. Experiments are performed to evaluate one of the algorithms on strain data from FBG sensors for two test cases: bonded to and woven into the fabric. Despite the complex strain on the FBGs bonded to the fabric, the empirical approach well predicts the out-of-plane deflection, except in the region under the indentor, where the fabric deformation was different than that modeled in the FE simulations. This result is promising for structural applications were direct observations of the out-of-plane deflections are not possible. To increase the maximum deflection of the fabric that could be measured, weaving of the FBGs into the fabric is also attempted. This method was less successful, due to the large amount of relative slipping between the optical fiber and the fabric, drastically reducing the strain measured by the FBGs.