Flexible and highly sensitive piezoresistive nanocomposites have been demonstrated to possess considerable potential for monitoring structural integrity and human physiological performance. To enhance the mechanical and strain sensing properties of these nanocomposites, different nanofillers (e.g., metal nanowires, carbon nanotube, and graphene) have been incorporated in polymeric matrices to establish electrically conductive pathways that are also sensitive to applied strains. Their piezoresistivity mainly stem from nanofillers’ intrinsic piezoresistivity, tunneling effect, and contact resistance changes of the nanofiller networks. Although many high-performance nanocomposite strain sensors have been developed and using different techniques, the empirically guided fabrication approach can be laborious, inefficient, and, most importantly, unpredictable. Therefore, this study proposes a topological design-based approach to strategically control and manipulate the strain sensing performance of the nanocomposites, simply by altering its geometric pattern design. First, polyethylene terephthalate (PET) substrates were patterned with pre-designed hierarchical inhomogeneous topologies and kirigami cuts created using a laser cutter. Second, the substrates were spray-coated using a carbon nanotube (CNT)-latex to deposit the strain-sensitive thin films. Third, the strain sensing performance of the CNT-latex nanocomposite thin films of different topologies was characterized and compared. It was found that, as the initial solid mechanics analysis predicted, the hierarchical inhomogeneous topology effectively enhanced the nanocomposites’ strain sensitivity, while the kirigami cuts significantly reduced sensitivity. The proposed methodology can help guide the development of high-performance nanocomposites with pre-programmed sensing properties for structural and human health monitoring applications.
Electrical impedance tomography (EIT) has been recently applied as a structural health monitoring (SHM) technique to many diﬀerent kinds of structures. In short, EIT is an algorithm that reconstructs the spatial conductivity response of a conductive body using only voltage measurement along its boundaries. For a conductive structure with its electrical properties being sensitive to damages and/or strains, mapping the distribution of its conductivity allows one to obtain its corresponding damage and/or strain distribution. To date, the EIT inverse problem has been solved using diﬀerent techniques. This study compared the performance of two diﬀerent approaches using four evaluation criteria. The first technique is based on EIDORS, which is an open-source EIT solver based on the maximum a posteriori (MAP) approach. It can rapidly, using a one-step linear approach, evaluate the relative impedance change of a given region when a baseline measurement (i.e., the response collected under its initial state) is provided. The second approach is a two-step iterative shrinkage thresholding (TwIST) method that compresses a signal’s sparsity in preserving sharp edges of an image. Both methods were evaluated using a 16-electrode 2D square shape with a simulated “point” damage at diﬀerent locations. The evaluation results suggested that TwIST outperforms MAP in terms of the resolution and accuracy of the reconstructed results, and MAP wins over TwIST in causing minor shape deformation and less ringing. Results from both methods exhibit position-dependency. These results are significant in promoting EIT becoming a powerful technique for in situ health monitoring.
Flexible and wearable sensors for human monitoring have received increased attention. Besides detecting motion and physical activity, measuring human vital signals (e.g., respiration rate and body temperature) provide rich data for assessing subjects’ physiological or psychological condition. Instead of using conventional, bulky, sensing transducers, the objective of this study was to design and test a wearable, fabric-like sensing system. In particular, multi-walled carbon nanotube (MWCNT)-latex thin films of different MWCNT concentrations were first fabricated using spray coating. Freestanding MWCNT-latex films were then sandwiched between two layers of flexible fabric using iron-on adhesive to form the wearable sensor. Second, to characterize its strain sensing properties, the fabric sensors were subjected to uniaxial and cyclic tensile load tests, and they exhibited relatively stable electromechanical responses. Finally, the wearable sensors were placed on a human subject for monitoring simple motions and for validating their practical strain sensing performance. Overall, the wearable fabric sensor design exhibited advances such as flexibility, ease of fabrication, light weight, low cost, noninvasiveness, and user comfort.