No abstract

Structural health monitoring systems must provide accuracy and robustness in predicting the structure’s health using the minimum intervention to ensure commercial viability. Characterization of impact is useful in assessing its severity, deciding if detailed damage analysis is necessary, and re-evaluating the present health of the structure under monitoring with better confidence. In this characterization process, the impact location is significant since some positions within a structure are more sensitive to damage. The inherent noise and uncertainties present in the sensor response pose a substantial hurdle to estimating the external impact correctly. This paper quantitatively compares three of the widely used neural networks, namely, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory network (LSTM), to estimate impact location from the lead zirconate titanate (PZT) sensor response. For this purpose, a square aluminum plate of 500 × 500 mm was equipped with four PZT sensors; each placed 100 mm away in both the plate directions from a corner and impact loads were given on a grid covering the whole plate. The PZT responses were used to train the three neural networks under study here, and their estimations were compared based on the Mean Absolute Error (MAE). In addition, increasing Gaussian noise was added to the PZT responses, and the robustness of the three neural networks was monitored. It was found that the ANN gives better accuracy with a Mean Absolute Error of 22 mm compared to Convolutional Neural Network (MAE = 31 mm) and Long Short-Term Memory (MAE = 25 mm). However, CNN is more robust when encountering noise with a 2% reduction in accuracy, while LSTM and ANN lost 7% and 11% accuracy, respectively.

In this work, tung oil was utilised as a catalyst-free self-healing agent, and an in-situ polymerization process was applied to encapsulate the tung oil core with a poly(urea-formaldehyde) (PUF) shell. The conventional poly(ethylene-alt-maleic-anhydride) (PEMA) polymer was compared to a more naturally abundant gelatin (GEL) emulsifier to compare the microcapsules’ barrier, morphological, thermal, and chemical properties, and the crystalline nature of the shell material. GEL emulsifiers produced microcapsules with a higher payload (96.5%), yield (28.9%), and encapsulation efficiency (61.7%) compared to PEMA (90.8%, 28.6% and 52.6%, respectively). Optical and electron microscopy imaging indicated a more uniform morphology for the GEL samples. The thermal decomposition measurements indicated that GEL decomposed to a value 7% lower than that of PEMA, which was suggested to be attributed to the much thinner shell materials that the GEL samples produced. An innovative and novel focused ion beam (FIB) milling method was exerted on the GEL sample, confirming the storage and release of the active tung oil material upon rupturing. The samples with GEL conveyed a higher healing efficiency of 91%, compared to PEMA’s 63%, and the GEL samples also conveyed higher levels of corrosion resistance.

No abstract

Flexible supercapacitors (SCs) have attracted growing interests as the power source for portable and wearable electronics. Carbon nanotubes (CNTs) are emerged as one of the promising materials for flexible SC electrodes due to their excellent high electrical conductivity, high mechanical strength, large surface area, and functionalization ability. CNTs can be assembled into several macroscopic forms based on the applications. In the present work, the CNT buckypapers integrated with 2D materials such as graphene and transition metal sulfides (TMDs) especially, tin sulfide (SnS2) nanoparticles were prepared using a simple mould casting method. The flexible and free-standing, interface-enhanced CNT paper having homogeneously dispersed 2D materials was prepared. The hybridized CNT papers were thoroughly characterized to elucidate their structural and surface properties. Finally, such hybrid CNT papers were used as binder-free, free-standing electrodes for the fabrication of flexible-all-solid-state supercapacitors. The electrochemical performance (CV, GCD, EIS) of the as obtained the symmetric and asymmetric devices were assessed to evaluate the charge storage performance and effect of 2D materials on the charge storage capability and stability were studied. Figure 1