The study considers the delamination resistance of carbon/epoxy laminates modified with Thermoplastic Nanoparticles of Polysulfone (TNPs). A new electrospinning nanofiber technique was utilized to convert polysulfone polymer into nanoparticles and uniformly disperse them within the resin. Fracture toughness was evaluated under loading modes I and II. In mode I, the toughness (GIC) increased significantly from 170 to 328 J/m² with TNPs incorporation. However, mode II showed minimal change, with GIIC values of 955 J/m² for virgin and 950 J/m² for TNPs-modified specimens. Scanning Electron Microscopy (SEM) was employed to depict the influence of TNPs on damage characteristics and crack propagation patterns. In mode I, crack deviation enhanced toughness as TNPs bypassed the PSU, while in mode II, cracks propagated through TNPs, resulting in particle smearing on the epoxy surface. This highlights TNPs' potential to modify the fracture toughness in mode I loading, but their effect is constrained in mode II loading scenarios.@en

Traditional inspection methods often fall short in detecting defects or damage in fibre-reinforced polymer (FRP) composite structures, which can compromise their performance and safety over time. A prime example is barely visible impact damage (BVID) caused by out-of-plane loadings such as indentation and low-velocity impact that can considerably reduce the residual strength. Therefore, developing advanced visual inspection techniques is essential for early detection of defects, enabling proactive maintenance and extending the lifespan of composite structures. This study explores the viability of using novel bio-inspired hybrid composite sensors for detecting BVID in laminated FRP composite structures. Drawing inspiration from the colour-changing mechanisms found in nature, hybrid composite sensors composed of thin-ply glass and carbon layers are designed and attached to the surface of laminated FRP composites exposed to transverse loading. A comprehensive experimental characterisation, including quasi-static indentation and low-velocity impact tests alongside non-destructive evaluations such as ultrasonic C-scan and visual inspection, is conducted to assess the sensors’ efficacy in detecting BVID. Moreover, a comparison between the two transverse loading types, static indentation and low-velocity impact, is presented. The results suggest that integrating sensors into composite structures has a minimal effect on mechanical properties such as structural stiffness and energy absorption, while substantially improving damage visibility. Additionally, the influence of fibre orientation of the sensing layer on sensor performance is evaluated, and correlations between internal and surface damage are demonstrated. @en

Internet of Medical Thing (IoMT) is an emerging technology in healthcare that can be used to realize a wide variety of medical applications. It improves people’s quality of life and makes it easier to care for the sick individuals in an efficient and safe manner. To do this, IoMT leverages the capabilities of some new technologies including IoT, Artificial Intelligence, cloud computing, computer networks and medicine. Combining these technologies to monitor the patient’s health conditions in real-time or semi-real-time is a critical challenge in IoMT. In this regard, one of the most crucial components of IoMT are network communication protocols that should provide a fast and reliable communication path between a connected biosensor to a patient and cloud computing environment. In this paper, we propose EQRSRL as an efficient routing mechanism for different types of IoMT applications. The aim of EQRSRL is to provide a reasonable level of Quality of Service (QoS) for IoMT traffics. To achieve this goal, it categorizes the network traffic into three classes and treats them differently concerning their QoS requirements. Moreover, EQRSRL divides the network environment into multiple zones to decrease the number of message exchange between the nodes. In order to compute optimal paths between the nodes, it considers QoS and energy metrics, and makes use of a reinforcement learning approach in path computation process. Simulation results show that the implementation of EQRSRL in IoMT is practical and leads to improvement of 82% in average energy consumption, 25% in end-to-end delay and 7% packet delivery ration in compared to the state-of-the-art routing techniques.

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In this study, the fatigue properties of carbon fiber-reinforced polymer (CFRP) composite laminates were investigated, specifically focusing on the incorporation of 100-µm polysulfone (PSU) nanofibers as an interleaving material. The PSU nanofibers were produced using the electrospinning technique. Both quasi-static and fatigue tests were conducted on both the reference specimens and the modified specimens to evaluate their mode-I performance. The results revealed an 85% increase in fracture toughness (G_IC) under quasi-static testing. The fatigue plots revealed a noteworthy reduction in the fatigue crack growth rate (da/dN) for the modified specimens due to new toughening mechanisms. Scanning electron microscopy (SEM) demonstrated that, the PSU nanofiber became melted and distributed in the interface, leading to phase separation and a sea-island structure. The presence of PSU microspheres caused crack deflection during delamination, which resulted in increased fracture and fatigue resistance.

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