The elastic response of vertically aligned-carbon nanotube/polydimethylsiloxane (A-CNT/PDMS) nanocomposites is presented in this study and related to the underlying aligned-CNT morphology. Multiwalled carbon nanotubes (MWCNTs) at 1% V_f are embedded in a flexible substrate of PDMS to create a flexible polymer nanocomposite (PNC). The PNC properties are evaluated using scanning electron microscopy (SEM), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA) and tensile mechanical tests, and the full linearly elastic constitutive relation is established for such a PNC. The results suggest that the CNTs retain the alignment after wetting and curing of PDMS. PDMS is significantly modified by the reinforcing aligned-CNT fibers, demonstrating non-isotropic (as opposed to the isotropic neat PDMS) elastic properties all different from PDMS (Young's modulus of 0.8 MPa), including an anisotropy ratio of 4.8 and increases in the modulus of A-CNT/PDMS over PDMS by more than 900% and 100%, in the CNT longitudinal and transverse directions, respectively. This study reports the first full constitutive relation that may be useful in modeling PNCs as composites or as elements of hierarchical nanoengineered composites, particularly PDMS-CNT PNCs are envisioned as elements in biomedical devices such as pressure transducers and energy harvesters.

Plastic injection molding is a fundamental manufacturing process used in various industries, accounting for approximately 30% of the global plastic product market. A significant challenge of this process lies in the need to employ sophisticated computational techniques to optimize the various phases. This review examines the optimization methodologies in injection molding, with a focus on integrating advanced modeling, surrogate models, and multi-objective optimization techniques to enhance efficiency, quality, and sustainability. Key phases such as plasticizing, filling, packing, cooling, and ejection are analyzed, each presenting unique optimization challenges. The review emphasizes the importance of cooling, which accounts for 50–80% of the cycle time, and examines innovative strategies, such as conformal cooling channels (CCCs), to enhance uniformity and minimize defects. Various computational tools, including Moldex3D and Autodesk Moldflow, are discussed due to their role in process simulation and optimization. Additionally, optimization algorithms such as evolutionary algorithms, simulated annealing, and multi-objective optimization methods are explored. The integration of surrogate models, such as Kriging, response surface methodology, and artificial neural networks, has shown promise in addressing computational cost challenges. Future directions emphasize the need for adaptive machine learning and artificial intelligence techniques to optimize molds in real time, offering more innovative and sustainable manufacturing solutions. This review is a comprehensive guide for researchers and practitioners, bridging theoretical advancements with practical implementation in injection molding optimization.

A new approach for the fabrication of flexible pressure sensors based on aligned carbon nanotubes (A-CNTs) is described in this paper. The technology is suitable for blood pressure sensors that can be attached to a stent-graft and deployed during an endovascular aneurysm repair (EVAR) procedure. Given the specifications of EVAR, the device should be foldable (extremely flexible) and characterized by a (very small) profile that integrates with the stent-graft cross section. A-CNTs embedded in a flexible substrate of polydimethylsiloxane (PDMS), a transparent, nontoxic and biocompatible silicone elastomer, are used to fabricate elements of the capacitive sensors. Fabricated prototypes validate our approach and show that CNTs/PDMS layered composites can be used to create highly flexible pressure sensors