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The purpose of this study is to improve the mechanical properties, thermal insulation properties and flame retardant properties of polyethylene terephthalate (PET), the organic montmorillonite (OMMT)/SiO₂ aerogel/PET composites and fibers were prepared, and the effects of the OMMT/SiO₂ aerogel on the structure, thermal conductivity and flame retardance of the OMMT/SiO₂ aerogel/PET composites and their fibers were systematically investigated.

The OMMT/SiO₂ aerogel/PET composites and fibers were prepared by in-situ polymerization and melt spinning using SiO₂ aerogel as thermal insulation filler and OMMT (DK2) as comodified filler.

The experimental results showed that OMMT improved the crystallization properties of PET. Compared with the crystallinity of SiO₂ aerogel/PET composites (34.8%), SiO₂ aerogel/PET composites and their fibers reached 45.1% and 49.2%, respectively. The breaking strength of the OMMT/SiO₂ aerogel/PET composite fibers were gradually increased with the OMMT content. When the content of OMMT was 0.8 wt.%, the fracture strength of the composite fibers reached 4.40 cN/dtex, which was 54% higher than that of the SiO₂ aerogel/PET fiber. In addition, the thermal insulation properties of the composites and their fibers were improved by addition of fillers, and at the same time reached the flame retardant level. The thermal conductivity of the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites was 101.78 mW/(m·K), which was 49.3% and 58.8% lower than that of the SiO₂ aerogel/PET composites and the pure PET, respectively. The thermal conductivity of the fiber fabrics woven from the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites was 28.18 mW/(m·K), which was 29.0% and 44.6% lower than that of the SiO₂ aerogel/PET composite fiber fabrics and PET fiber fabrics. The flame retardancy of the composites was improved, with an limiting oxygen index value of 29.2% for the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites, which was 4.1% higher compared to the SiO₂ aerogel/PET composites, and achieved the flame retardant level.

The SiO₂ aerogel/PET composites and their fibers have good mechanical properties, flame retardant properties and thermal insulation properties, exhibited good potential for application in the field of thermal insulation, such as warm clothing. Nowadays, as the energy crisis is becoming more and more serious, it is very important to improve the thermal insulation properties of PET to reduce energy losses and mitigate the energy crisis.

In this study, PET based composites and their fibers with excellent mechanical properties, thermal insulation properties and flame retardant property were obtained by using three-dimensional network porous silica aerogel with low density and low thermal conductivity as the thermal insulation functional filler and two-dimensional layered OMMT as the synergetic modified filler.

Crystallization kinetics is a key factor that controls the crystallization process of polymers and influences the crystallinity and morphology of polymers. This study aims to explore the effects of functional filler SiO₂ aerogel and co-modified filler organic montmorillonite (OMMT) on the crystallization process of polyester polyethylene terephthalate (PET). In this study, the nonisothermal crystallization kinetics of OMMT/SiO₂ aerogel/PET composites were studied by Jeziorny method.

The catalyst (Sb₂O₃), OMMT and SiO₂ aerogel were uniformly dispersed in ethylene glycol (EG). Then, the mixture and terephthalic acid (PTA) were put into a semicontinuous polyester synthesis reactor, and the SiO₂ aerogel/PET composites were prepared by esterification and polycondensation.

Non-isothermal kinetic results showed that the high cooling rate hindered the movement of the molecular chain of the composites and made the crystallization peak move toward the low-temperature direction. With the increase of crystallization temperature (T_c), the melt crystallization rate decreases, but the cold crystallization rate increases. The introduction of OMMT and SiO₂ aerogel condensation affected the nucleation and growth mode of crystals, lengthened the time with a relative crystallinity of 50% (t_1/2) and decreased the crystallization rate. OMMT improved the crystallinity and Avrami index of the composites.

Effects of thermal insulation functional filler SiO₂ aerogel and co-modified filler OMMT on the crystallization process of PET were studied by non-isothermal crystallization kinetics, and the effects of SiO₂ aerogel and OMMT on the nucleation mechanism of PET were clarified, which provided a theoretical reference for the preparation and performance optimization of PET matrix composites.

In this study, the OMMT/SiO₂ aerogel/PET composites were prepared by in-situ polymerization, the crystallinity of PET matrix composites was improved, and the effects of OMMT and SiO₂ aerogel on the crystallization process of PET were clarified.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.