Recyclable and Biobased Vitrimers for Carbon Fibre-Reinforced Composites-A Review.

Economic and environmental concerns over the accumulation of end-of-life carbon fibre composite waste have led to increased attention to sustainable materials with low environmental impact. Over decades of research, vitrimers, a modern class of covalent adaptable networks, have bridged the gap between thermoplastics and thermosets. With the distinguishing feature of dynamic covalent bonds, vitrimers can be rearranged and reprocessed within their existing network structures in response to external stimuli such as heat or light. This poses a unique solution to repairing damaged composites, extending their service life, and reducing post-consumer waste. However, the synthesis of vitrimers often requires petrochemical consumption, which increases their carbon footprint. Using bio-based materials could be a promising solution to reduce the reliance on petrochemicals and their related pollution. This review compiles the contemporary requirements for bio-based vitrimers regarding their properties, scalability, and recycling features. This article also presents a comprehensive overview of the pathways to produce sustainable bio-based vitrimers and an overview of promising studies showing the potential uses of bio-derived vitrimers on carbon fibre composite productions.

Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps.

Monitoring object displacement is critical for structural health monitoring (SHM). Radio frequency identification (RFID) sensors can be used for this purpose. Using more sensors enhances displacement estimation accuracy, especially when it is realized through the use of machine learning (ML) algorithms for predicting the direction of arrival of the associated signals. Our research shows that ML algorithms, in conjunction with adequate RFID passive sensor data, can precisely evaluate azimuth angles. However, increasing the number of sensors can lead to gaps in the data, which typical numerical methods such as interpolation and imputation may not fully resolve. To overcome this challenge, we propose enhancing the sensitivity of 3D-printed passive RFID sensor arrays using a novel photoluminescence-based RF signal enhancement technique. This can boost received RF signal levels by 2 dB to 8 dB, depending on the propagation mode (near-field or far-field). Hence, it effectively mitigates the issue of missing data without necessitating changes in transmit power levels or the number of sensors. This approach, which enables remote shaping of radiation patterns via light, can herald new prospects in the development of smart antennas for various applications apart from SHM, such as biomedicine and aerospace.

Review of Approaches to Minimise the Cost of Simulation-Based Optimisation for Liquid Composite Moulding Processes.

The utilisation of numerical process simulation has greatly facilitated the challenging task of liquid composite moulding (LCM) process optimisation, providing ease of solution evaluation at a significantly reduced cost compared to complete reliance on physical prototyping. However, due to the process complexity, such process simulation is still considerably expensive at present. In this paper, cost-saving approaches to minimising the computational cost of simulation-based optimisation for LCM processes are compiled and discussed. Their specific applicability, efficacy, and suitability for various optimisation/moulding scenarios are extensively explored in detail. The comprehensive analysation and assimilation of their operation alongside applicability for the problem domain of interest are accomplished in this paper to further complement and contribute to future simulation-based optimisation capabilities for composite moulding processes. The importance of balancing the cost-accuracy trade-off is also repeatedly emphasised, allowing for substantial cost reductions while ensuring a desirable level of optimization reliability.

Three-Dimensional Printing of Shape Memory Liquid Crystalline Thermoplastic Elastomeric Composites Using Fused Filament Fabrication.

Liquid crystalline elastomers (LCEs) are stimuli-responsive materials utilised in shape memory applications. The processability of these materials via advanced manufacturing is being paid increasing attention to advance their volume production on an industrial scale. Fused filament fabrication (FFF) is an extrusion-based additive manufacturing (AM) technique that offers the potential to address this. The critical challenge, however, is the rheological characteristics of LCEs that need to be tuned to achieve a facile processability through the extrusion-based method. In this work, new filaments of liquid crystalline thermoplastic elastomer (LCTPE) and its composites with lignin were made by the ternary system of LCE, thermoplastic polyurethane (TPU), and lignin. The results showed that TPU improves the melt flow index of the LCTPE system to approximately 10.01 g/10 min, while adding lignin further enhances the value of this index for the composites up to 21.82 g/10 min. The microstructural analysis indicated that the effective distribution of lignin and reduced domain size of the LCEs in the ternary blend contribute to the enhanced flowability of this filament through 3D printing. Samples of 3D-printed LCTPE and LCTPE/lignin composites maintained their shape memory characteristics via thermomechanical activation. Full shape recovery of the new LCTPE matrix and its composites with lignin was achieved in 39 s and 32 s at 130 °C, followed by 28 s and 24 s at 160 °C, respectively. The successful fabrication of LCTPE and LCTPE/lignin composite samples through 3D printing demonstrates a potential procedure for processing these shape memory materials using the FFF technique, and lignin offers a sustainable and cost-effective material solution that enhances the properties of this composite material.

Application of KNN and ANN Metamodeling for RTM Filling Process Prediction.

Process simulation is frequently adopted to facilitate the optimization of the resin transfer molding process. However, it is computationally costly to simulate the multi-physical, multi-scale process, making it infeasible for applications involving huge datasets. In this study, the application of K-nearest neighbors and artificial neural network metamodels is proposed to build predictive surrogate models capable of relating the mold-filling process input-output correlations to assist mold designing. The input features considered are the resin injection location and resin viscosity. The corresponding output features investigated are the number of vents required and the resultant maximum injection pressure. Upon training, both investigated metamodels demonstrated desirable prediction accuracies, with a low prediction error range of 5.0% to 15.7% for KNN metamodels and 6.7% to 17.5% for ANN metamodels. The good prediction results convincingly indicate that metamodeling is a promising option for composite molding applications, with encouraging prospects for data-intensive applications such as process digital twinning.

Influence of Three-Dimensional Printing Parameters on Compressive Properties and Surface Smoothness of Polylactic Acid Specimens.

While the mechanical performance of fused filament fabrication (FFF) parts has been extensively studied in terms of the tensile and bending strength, limited research accounts for their compressive performance. This study investigates the effect of four process parameters (layer height, extrusion width, nozzle temperature, and printing speed) on the compressive properties and surface smoothness of FFF parts made of Polylactic Acid (PLA). The orthogonal Taguchi method was employed for designing the experiments. The surface roughness and compressive properties of the specimens were then measured and optimized using the analysis of variance (ANOVA). A microscopic analysis was also performed to identify the failure mechanism under static compression. The results indicated that the layer height had the most significant influence on all studied properties, followed by the print speed in the case of compressive modulus, hysteresis loss, and residual strain; extrusion width in the case of compressive strength and specific strength; and nozzle temperature in the case of toughness and failure strain. The optimal design for both high compressive properties and surface smoothness were determined as a 0.05 mm layer height, 0.65 mm extrusion width, 205 °C nozzle temperature, and 70 mm/s print speed. The main failure mechanism observed by SEM analysis was delamination between layers, occurring at highly stressed points near the stitch line of the PLA prints.

Thermomechanical Properties and Fracture Toughness Improvement of Thermosetting Vinyl Ester Using Liquid Metal and Graphene Nanoplatelets.

In this study, a eutectic gallium-indium (EGaIn) alloy and graphene nanoplatelets (GnPs) were employed as reinforcements for a comonomer vinyl ester (cVE) resin at different weight fractions up to 2% via a direct polymerization process. First, the effect of EGaIn on the curing kinetics of cVE was evaluated. The thermal and mechanical properties, and the fracture toughness of two types of cVE composites consisting of EGaIn and GnPs were then studied. The results showed that sub-micron sized EGaIn (≤1 wt.%) could promote the curing reaction of cVE without changing the curing mechanism. However, with further increases in EGaIn loading between 1 and 2 wt.%, the curing reaction rate tends to decrease. Both EGaIn and GnPs showed a significant enhancement in strengthening and toughening the cVE matrix with the presence of filler loading up to 1 wt.%. EGaIn was more effective than GnPs in promoting the flexural and impact strength. An increase of up to 50% and 32% were recorded for these mechanical properties, when EGaln was used, as compared to 46%, and 18% for GnPs, respectively. In contrast, the GnPs/cVE composites exhibited a greater improvement in the fracture toughness and fracture energy by up to 50% and 56% in comparison with those of the EGaIn/cVE ones by up to 32% and 39%, respectively. Furthermore, the stiffness of both the EgaIn/cVE and GnPs/cVE composites showed a significant improvement with an increase of up to 1.76 and 1.83 times in the normalized storage modulus, respectively, while the glass transition temperature (Tg) values remained relatively constant. This work highlights the potential of EGaIn being employed as a filler in creating high-performance thermoset composites, which facilitates its widening applications in many structural and engineering fields, where both higher toughness and stiffness are required.

Evaluation of the Failure Mechanism in Polyamide Nanofibre Veil Toughened Hybrid Carbon/Glass Fibre Composites.

The interface of hybrid carbon/E-glass fibres composite is interlayered with Xantu.layr® polyamide 6,6 nanofibre veil to localise cracking to promote a gradual failure. The pseudo-ductile response of these novel stacking sequences examined under quasi-static three-point bending show a change to the failure mechanism. The change in failure mechanism due to the interfacial toughening is examined via SEM micrographs. The incorporation of veil toughening led to a change in the dominant failure mechanism, resulting in fibre yielding by localised kinking and reduced instances of buckling failure. In alternated carbon and glass fibre samples with glass fibre undertaking compression, a pseudo-ductile response with veil interlayering was observed. The localisation of the fibre failure, due to the inclusion of the veil, resulted in kink band formations which were found to be predictable in previous micro buckling models. The localisation of failure by the veil interlayer resulted in a pseudo-ductile response increasing the strain before failure by 24% compared with control samples.

Upcycling Polystyrene.

Several environmental and techno-economic assessments highlighted the advantage of placing polystyrene-based materials in a circular loop, from production to waste generation to product refabrication, either following the mechanical or thermochemical routes. This review provides an assortment of promising approaches to solving the dilemma of polystyrene waste. With a focus on upcycling technologies available in the last five years, the review first gives an overview of polystyrene, its chemistry, types, forms, and varied applications. This work presents all the stages that involve polystyrene's cycle of life and the properties that make this product, in mixtures with other polymers, command a demand on the market. The features and mechanical performance of the studied materials with their associated images give an idea of the influence of recycling on the structure. Notably, technological assessments of elucidated approaches are also provided. No single approach can be mentioned as effective per se; hybrid technologies appear to possess the highest potential. Finally, this review correlates the amenability of these polystyrene upcycling methodologies to frontier technologies relating to 3D printing, human space habitation, flow chemistry, vertical farming, and green hydrogen, which may be less intuitive to many.

Displacement Estimation Using 3D-Printed RFID Arrays for Structural Health Monitoring.

Radio frequency identification (RFID) tags are small, low-cost, wearable, and wireless sensors that can detect movement in structures, humans, or robots. In this paper, we use passive RFID tags for structural health monitoring by detecting displacements. We employ a novel process of using 3D printable embedded passive RFID tags within uniform linear arrays together with the multiple signal classification algorithm to estimate the direction of arrival using only the phase of the backscattered signals. We validate our proposed approach via data collected from real-world experiments using a unipolar RFID reader antenna and both narrowband and wideband measurements.

Three-Dimensional Printing of Customized Scaffolds with Polycaprolactone-Silk Fibroin Composites and Integration of Gingival Tissue-Derived Stem Cells for Personalized Bone Therapy.

Regenerative biomaterials play a crucial role in the success of maxillofacial reconstructive procedures. Yet today, limited options are available when choosing polymeric biomaterials to treat critical size bony defects. Further, there is a requirement for 3D printable regenerative biomaterials to fabricate customized structures confined to the defect site. We present here a 3D printable composite formulation consisting of polycaprolactone (PCL) and silk fibroin microfibers and have established a robust protocol for fabricating customized 3D structures of complex geometry with the composite. The 3D printed composite scaffolds demonstrated higher compressive modulus than 3D printed scaffolds of PCL alone. Furthermore, the compressive modulus of PCL-Antheraea mylitta (AM) silk scaffolds is higher than that of the PCL-Bombyx mori (BM) silk scaffolds at their respective ratios. Compressive modulus of PCL-25AM silk scaffolds (73.4 MPa) is higher than that of PCL-25BM silk scaffolds (65.1 MPa). Compressive modulus of PCL-40AM silk scaffolds (106.1 MPa) is higher than that of PCL-40BM silk scaffolds (77.7 MPa). Moreover, we have isolated, characterized, and integrated human gingival mesenchymal stem cells (hGMSCs), an effective autologous cell source, onto the 3D printed scaffolds to evaluate their bone regeneration potential. The results demonstrated that PCL-silk microfiber composite scaffolds of Antheraea mylitta origin showed much higher bioactivity than the Bombyx mori ones because of arginine-glycine-aspartic acid (RGD) sequences present in the Antheraea mylitta silk fibroin protein favoring cell attachment and proliferation. By day 14, the metabolic activity of hGMSCs was highest in PCL-40AM (4.5 times higher than that at day 1). In addition, to show the translational potential of this work, we have fabricated a patient defect-specific model (mandible) using the CT scan obtained by the micro-CT imaging to understand the printability of the composite for fabricating complex structures to restore maxillofacial bony defects with precision when applied in a clinical scenario.

Silk fibroin microfiber-reinforced polycaprolactone composites with enhanced biodegradation and biological characteristics.

There is an enormous demand for bone graft biomaterials to treat developmental and acquired bony defects arising from infections, trauma, tumor, and other conditions. Polycaprolactone (PCL) has been extensively utilized for bone tissue engineering but limited cellular interaction and tissue integration are the primary concerns. PCL-based composites with different biomaterials have been attempted to improve the mechanical and biological response. Interestingly, a few studies have tried to blend PCL with aqueous silk fibroin solution, but the structures prepared with the blend were mechanically weak due to phase mismatch. As a result, silk microparticle-based PCL composites have been prepared, but the microfibers-reinforced composites could be superior to them due to significant fiber-matrix interaction. This study aims at developing a unique composite by incorporating 100-150 µm long (aspect ratio; 8:1-5:1) silk-fibroin microfibers into the PCL matrix for superior biological and mechanical properties. Two silk variants were used, that is, Bombyx mori and a wild variant, Antheraea mylitta, reported to have cell recognizable Arginine-Glycine-Aspartic acid (RGD) sequences. A. mylitta silk fibroin microfibers were produced, and composites were made with PCL for the first time. The morphological, tensile, thermal, biodegradation, and biological properties of the composites were evaluated. Importantly, we tried to optimize the silk concentration within the composite to strike a balance among the cellular response, biodegradation, and mechanical strength of the composites. The results indicate that the PCL-silk fibroin microfiber composite could be an efficient biomaterial for bone tissue engineering.

Evolving Strategies for Producing Multiscale Graphene-Enhanced Fiber-Reinforced Polymer Composites for Smart Structural Applications.

Graphene has become an important research focus in many current fields of science including composite manufacturing. Developmental work in the field of graphene-enhanced composites has revealed several functional and structural characteristics that promise great benefits for their use in a broad range of applications. There has been much interest in the production of multiscale high-performance, lightweight, yet robust, multifunctional graphene-enhanced fiber-reinforced polymer (gFRP) composites. Although there are many reports that document performance enhancement in materials through the inclusion of graphene nanomaterials into a matrix, or its integration onto the reinforcing fiber component, only a few graphene-based products have actually made the transition to the marketplace. The primary focus of this work concerns the structural gFRPs and discussion on the corresponding manufacturing methodologies for the effective incorporation of graphene into these systems. Another important aspect of this work is to present recent results and highlight the excellent functional and structural properties of the resulting gFRP materials with a view to their future applications. Development of clear standards for the assessment of graphene material properties, improvement of existing materials and scalable manufacturing technologies, and specific regulations concerning human health and environmental safety are key factors to accelerate the successful commercialization of gFRPs.

Production of Cellulose Nanocrystals from Australian Wood Sources.

Two Australian native wooden sources (Acacia Mangium and Eucalyptus Globulus) derived pulps were explored as raw feed stocks to prepare the valuable nanomaterial of cellulose nanocrystals (CNC). After bleaching and acid hydrolysis, cellulose nanocrystals were successfully produced with high yields of approximately 60% for both kraft pulps. According to the characterization of SEM and AFM, the as prepared CNC had a rod like structure with the length and diameter in the range of 200~1000 nm and 10~100 nm, respectively based on the initial wooden source. XRD confirmed the crystalline structure of the resulting CNC. Further characterisation by TGA showed that the chemical treatment of the wood pulp had impact upon the thermal stability, evidenced by a lower onset temperature of the thermal decomposition of CNC.