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This study aims to develop a comprehensive process to evaluate the leaching behavior of 3D-printed nanocomposite samples as candidate materials for potential use in wearable devices. The study involves the immersion of the 3D-printed test coupons, produced via Fused Filament Fabrication (FFF), into artificial sweat and deionized water in a controlled environment provided by a dissolution apparatus. Three distinct nanocomposite filaments were used, each consisting of different polymer matrices: thermoplastic polyurethane (TPU), copolyester (TX1501), and polyamide (PA12). The additives incorporated within these filaments encompassed silver nanoparticles (AgNPs), chopped carbon fibers (CCFs), and super paramagnetic iron oxide nanoparticles (SPIONs), respectively. The current study aims to identify potential risks associated with the release of nanomaterials and additives, through SEM/EDX analysis and in vitro measurements of proinflammatory cytokines. Furthermore, this research contributes to the advancement of safe and reliable 3D-printed materials for wearable technologies, fostering their widespread adoption in various applications.

Process reliability and quality output are critical indicators for the upscaling potential of a fabrication process on an industrial level. Fused filament fabrication (FFF) is a versatile additive manufacturing (AM) technology that provides viable and cost-effective solutions for prototyping applications and low-volume manufacturing of high-performance functional parts, yet is defect-prone due to the inherent aspect of parametrization. A systematic yet parametric workflow for quality inspection is therefore required. The work presented describes a versatile and reliable framework for automatic defect detection during the FFF process, enabled by artificial intelligence-based computer vision. Specifically, state-of-the-art deep learning models are developed for in-line inspection of individual thermoplastic strands’ surface morphology and weld quality, thus defining acceptable limits for FFF process parameter values. We examine the capabilities of an NVIDIA Jetson Nano, a low-power, high-performance computer with an integrated graphical processing unit (GPU). The developed deep learning models used in this analysis use a pre-trained model combined with manual configurations in order to efficiently identify the thermoplastic strands’ surface morphology. The proposed methodology aims to facilitate process parameter selection and the early identification of critical defects, toward an overall improvement in process reliability with reduced operator intervention.

Bi-material composite structures with continuous fibers embedded on polymer substrates exhibit self-morphing under thermal stimulus induced by the different coefficients of thermal expansion (CTE) between the two constituent materials. In this study, a series of such structures are investigated in terms of fiber patterns and materials to achieve programmable and reversible transformations that can be exploited for thermal management applications. Stemming from this investigation’s results, an axial cooling fan prototype is designed and fabricated with composite blades that passively alter their shape, specifically their curvature and twist angle, under different operating temperatures. A series of computational fluid dynamics (CFD) simulations are performed, subjecting the fan’s geometry to different flow temperatures to measure differences in airflow deriving from the induced shape transformations. Corresponding experimental trials are additionally performed, aiming to validate the simulation results. The results indicate the potential of utilizing bilayer self-morphing configurations for the fabrication of smart components for cooling purposes.

Fused Filament Fabrication (FFF) has been established as a widely practiced Additive Manufacturing technique, using various thermoplastic filaments. Carbon fibre (CF) additives enhance mechanical properties of the materials. The main operational hazard of the FFF technique explored in the literature is the emission of Ultrafine Particles and Volatile Organic Compounds. Exposure data regarding novel materials and larger scale operations is, however, still lacking. In this work, a thorough exposure assessment measurement campaign is presented for a workplace applying FFF 3D printing in various setups (four different commercial devices, including a modified commercial printer) and applying various materials (polylactic acid, thermoplastic polyurethane, copolyamide, polyethylene terephthalate glycol) and CF-reinforced thermoplastics (thermoplastic polyurethane, polylactic acid, polyamide). Portable exposure assessment instruments are employed, based on an established methodology, to study the airborne particle exposure potential of each process setup. The results revealed a distinct exposure profile for each process, necessitating a different safety approach per setup. Crucially, high potential for exposure is detected in processes with two printers working simultaneously. An updated engineering control scheme is applied to control exposures for the modified commercial printer. The establishment of a flexible safety system is vital for workplaces that apply FFF 3D printing.

Curved bladelets on wind turbine blades play an important role in improving the performance and efficiency of wind turbines. Implementing such features on the tip of wind turbine blades can improve their overall aerodynamic characteristics by reducing turbulence and loading without hindering lift generation and overall efficiency, thus leading to increased energy capture and reduced costs over the life of the turbine. Subjecting the integrated blade tip to optimization procedures can maximize its beneficial contribution to the assembly in general. Within this context, a systemic workflow is proposed for the optimization of a curved bladelet implemented on a wind turbine blade. The approach receives input in the form of an initial tip geometry and performs improvements in two distinct stages. Firstly, shape optimization is performed directly on the outer shape to enhance its aerodynamic properties. Subsequently, the topology of its interior structure is refined to decrease its mass while retaining its improved airflow characteristics. The proposed workflow aims to approach blade tip optimization holistically, both in terms of aerodynamic performance and structural capabilities; is computationally validated via fluid dynamics studies and finite element analysis to evaluate the performance augmentation achieved through it; and is further coupled with additive manufacturing for the production of prototype parts, benefiting from the manufacturing flexibility offered by digital fabrication technologies. The optimized bladelet model presented an approximate 30% improvement in the torque generated on it, while maintaining only 70% of its original mass, effectively contributing to a 0.81% increase to the total torque generated by the blade, consequently confirming the effectiveness of the proposed methodology.

Additive manufacturing holds promise for the fabrication of three-dimensional scaffolds with precise geometry, to serve as substrates for the guided regeneration of natural tissue. In this work, a bioinspired approach is adopted for the synthesis of hybrid hydroxyapatite hydrogels, which were subsequently printed to form 3D scaffolds for bone tissue engineering applications. These hydrogels consist of hydroxyapatite nanocrystals, biomimetically synthesized in the presence of both chitosan and l -arginine. To improve their mechanical properties, chemical crosslinking was performed using a natural crosslinking agent (genipin), and their rheology was modified by employing an acetic acid/gelatin solution. Regarding the 3D printing process, several parameters (flow, infill and perimeter speed) were studied in order to accurately produce scaffolds with predesigned geometry and micro-architecture, while also applying low printing temperature (15 °C). Following the printing procedure, the 3D scaffolds were freeze dried in order to remove the entrapped solvents and therefore, obtain a porous interconnected network. Evaluation of porosity was performed using micro-computed tomography and nanomechanical properties were assessed through nanoindentation. Results of both characterization techniques, showed that the scaffolds' porosity as well as their modulus values, fall within the corresponding range of the respective values of cancellous bone. The biocompatibility of the 3D printed scaffolds was assessed using MG63 human osteosarcoma cells for 7 days of culturing. Cell viability was evaluated by MTT assay as well as double staining and visualized under fluorescence microscopy, while cell morphology was analyzed through scanning electron microscopy. Biocompatibility tests, revealed that the scaffolds constitute a cell-friendly environment, allowed them to adhere on the scaffolds' surface, increase their population and maintain high levels of viability.

In this work, a methodology is presented in order to assess the microstructure of Portland Cement through advanced characterisation. Nanoindentation is used to determine reduced elastic modulus and hardness, which are involved in the design of construction materials. Elemental mapping and X-ray scanning microtomography were also used to establish structure-property relations through porosity distribution and morphology as well as phase identification by nanoindentation and image analysis. The variation of carbon nanotubes (CNTs) concentration between 0.02 and 1.0 wt% demonstrated alterations in phase composition, and thus the effect of CNTs reinforcement in hydration progress was clarified. Characterisation by all techniques identified 0.5 wt% concentration as promising due to the formation of higher density hydrated phases, even though total porosity was not minimized. This fact was explained by nanoindentation as enhanced Calcium-Silicate-Hydrates connectivity in the porous network was detected. The voids of cement were filled with low stiffness hydrated gel phases as a result of enhanced hydration in the presence of CNTs.

In this work, materials that as additives in cement promote self-sealing/healing properties by the gradual release of water they absorb were synthesized, characterized and evaluated. Specifically, hybrid SAPs that absorb high ammounts of water encapsulated with SiO2 that facilitates their incorporation in the matrix since it improves their chemical affinity were investigated. The structure and morphology of the fabricated SAPs were characterized analytically and confirmed the synthesis of P(MAA-co-EGDMA)@SiO2 nanocomposite. Its particle size is expected to reduce the size of the pores formed due to the absorbing/desorbing water process during the mixing and curing of cement. Moreover, the water absorbance of the above mentioned material as well as its ability to maintain its original structure during subsequent cycles of absorbing/desorbing water from different mediums and specifically from distilled water (DW) and cement slurry filtrate (CS) were evaluated. CS was chosen to mimic the cementitious environment considering the presence of various ions and its pH value (~ 12). The results revealed that the absorption ratio of P(MAA-co-EGDMA)@SiO2 in DW and CS was higher than 1500 wt.% its original dry weight, while SEM pictures proved that the hybrid SAPs maintained their structure after the water absorption tests.

In the present work we study the glass transition and molecular dynamics in new amphiphilic block copolymers based on hydrophilic poly(oligo ethylene glycol methacrylate) (POEGMA) and water-insoluble poly(hydroxyl propyl methacrylate) (PHPMA) prepared by RAFT polymerization. To that aim, we employ here differential scanning calorimetry (DSC) and dielectric spectroscopy (BDS). At dry conditions and up to ~50 wt% PHPMA, the POEGMA phase dominates on the glass transition (Tg ~ −60 °C) and on local dynamics of the copolymers, while for larger PHPMA contents (80 wt%) the behaviour is that of the strongly plasticized PHPMA phase (neat PHPMA exhibits remarkably high calorimetric glass transition temperature, Tg ~80 °C). Interestingly, a single glass transition step was observed for all compositions. For the copolymer rich in PHPMA (80 wt% PHPMA) the endothermic calorimetric step related to glass transition is much broader than in other copolymers, indicating higher spatial heterogeneity in the copolymer. Molecular dynamics by BDS, namely the secondary-local γ and β relaxations and segmental α relaxations (related with the glass transition) were mapped, especially for neat PHPMA for the first time. The presence of polar hydroxyl group in the side group of PHPMA made possible the dielectric study of the molecular mobility within the nano-domains formed by the side chains, like in n-alkyl methacrylates with long alkyl side chains (PE-like mobility). On the other hand, the dynamics in the brush-like structuring POEGMA is recorded quite similar to that in linear poly(ethylene glycol) chains. In the copolymers our results reveal strong intermolecular interactions at the atomic level among the side chains/groups of the two components and a pronounced nano-structured morphology.

A polyamide (PA) 12-based thermoplastic composite was modified with carbon nanotubes (CNTs), CNTs grafted onto chopped carbon fibers (CFs), and graphene nanoplatelets (GNPs) with CNTs to improve its thermal conductivity for application as a heat sink in electronic components. The carbon-based nanofillers were examined by SEM and Raman. The laser flash method was used to measure the thermal diffusivity in order to calculate the thermal conductivity. Electrical conductivity measurements were made using a Keithley 6517B electrometer in the 2-point mode. The composite structure was examined by SEM and micro-CT. PA12 with 15 wt% of GNPs and 1 wt% CNTs demonstrated the highest thermal conductivity, and its processability was investigated, utilizing sequential interdependence tests to evaluate the composite material behavior during fused filament fabrication (FFF) 3D printing processing. Through this assessment, selected printing parameters were investigated to determine the optimum parametric combination and processability window for the composite material, revealing that the selected composition meets the necessary criteria to be processable with FFF.

Carbon-based nanomaterials are promising reinforcing elements for the development of “smart” self-sensing cementitious composites due to their exceptional mechanical and electrical properties. Significant research efforts have been committed on the synthesis of cement-based composite materials reinforced with carbonaceous nanostructures, covering every aspect of the production process (type of nanomaterial, mixing process, electrode type, measurement methods etc.). In this study, the aim is to develop a well-defined repeatable procedure for the fabrication as well as the evaluation of pressure-sensitive properties of intrinsically self-sensing cementitious composites incorporating carbon- based nanomaterials. Highly functionalized multi-walled carbon nanotubes with increased dispersibility in polar media were used in the development of advanced reinforced mortar specimens which increased their mechanical properties and provided repeatable pressure-sensitive properties.