Surface engineering of intrinsically microporous poly(ether-ether-ketone) membranes: From flat to honeycomb structures

Semi-interpenetrating anion exchange membranes using hydrophobic microporous linear poly(ether ketone)

In order to realise high ionic conductivity and improved chemical stability, a series of anion exchange membranes (AEMs) with semi-interpenetrating polymer network (sIPN) has been prepared via the incorporation of crosslinked poly(biphenyl N-methylpiperidine) (PBP) and spirobisindane-based intrinsically microporous poly(ether ketone) (PEK-SBI). The formation of phase separated structures as a result of the incompatibility between the hydrophilic PBP network and the hydrophobic PEK-SBI segment, has successfully promoted the hydroxide ion conductivity of AEMs. A swelling ratio (SR) as low as 12.2 % at 80 °C was recorded for the sIPN containing hydrophobic PEK-SBI as the linear polymer and crosslinked structure with a mass ratio of PBP to PEK-SBI of 90/10 (sIPN-90/10_(PEK-SBI)). The sIPN-90/10_(PEK-SBI) AEM achieved the highest hydroxide ion conductivity of 122.4 mS cm^-1 at 80 °C and a recorded ion exchange capacity (IEC) of 2.26 meq g^-1. Atomic force microscopy (AFM) and transmission electron microscopy (TEM) clearly revealed the improved phase separation structure of sIPN-90/10_(PEK-SBI). N₂ adsorption isotherm indicated that the Brunauer-Emmett-Teller (BET) surface area of the AEMs increased with the increase of microporous PEK-SBI content. Interestingly, the sIPN-90/10_(PEK-SBI) AEM showed good alkaline stability for being able to maintain a conductivity of 94.7 % despite being soaked in a 1 M sodium hydroxide solution at 80 °C for 30 days. Meanwhile, a peak power density of 481 mW cm^-2 can be achieved by the hydrogen/oxygen single cell using sIPN-90/10_(PEK-SBI) as the AEM.

Minireview on Self-Healing Polymers: Versatility, Application, and Prospects

Nature is blessed with self-healing properties. Mimicking nature is a traditional practice to innovate new classes of materials for researchers. In this practice, researchers made a revolutionary approach to innovate self-healing polymer (SHP) that can be used to treat damage-related losses. Different SHPs with various properties have been developed for a wide range of applications. SHPs unlocked the key to the taste of real life through their application and versatility in the sectors close to our day-by-day life of this age and the near future. In this study, we reviewed the scopes and prospects of the application of SHPs owing to different properties. Varieties of amazing properties made SHPs fit in different sectors such as construction, paint and coat, electronics, healthcare, textile, and automotive and aerospace. Similarly, due to having suitable functionality, SHPs can also be used in different industries. Therefore, it is high time to generalize the production of SHPs by suitable research and make sure the easy application for the welfare of human civilization and other living creatures.

Low-Velocity Impact Localization on a Honeycomb Sandwich Panel Using a Balanced Projective Dictionary Pair Learning Classifier

Carbon-fiber aluminum honeycomb sandwich panels are vulnerable to low-velocity impacts, which can cause structural damage and failures that reduce the bearing performance and reliability of the structure. Therefore, a method for locating such impacts through a sensor network is very important for structural health monitoring. Unlike composite laminates, the stress wave generated by an impact is damped rapidly in a sandwich panel, meaning that the signal qualities measured by different sensors vary greatly, thereby making it difficult to locate the impact. This paper presents a method for locating impacts on carbon-fiber aluminum honeycomb sandwich panels utilizing fiber Bragg grating sensors. This method is based on a projective dictionary pair learning algorithm and uses structural sparse representation for impact localization. The measurement area is divided into several sub-areas, and a corresponding dictionary is trained separately for each sub-area. For each dictionary, the sensors are grouped into main sensors within the sub-area and auxiliary sensors outside the sub-area. A balancing weight factor is added to optimize the proportion of the two types of sensor in the recognition model, and the algorithm for determining the balancing weight factor is designed to suppress the negative effects on the positioning of the sensors with poor signal quality. The experimental results show that on a 300 mm × 300 mm × 15 mm sandwich panel, the impact positioning accuracy of this method is 96.7% and the average positioning error is 0.85 mm, which are both sufficient for structural health monitoring.