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.
their solubilization characteristics towards specific drugs. [4,5] The major advantage of AmBCs is that they can be prepared from a huge variety of different monomers in order to achieve the desired properties of the final AmBC chain according to the demands of specific applications. [10] An increasing number of copolymers possessing a stimuli-responsive block, responding to the alteration of its environment, [11,12] such as temperature, [6,9,13] pH, [3,14] or ionic strength, [4][5][6][7]14] has appeared in the literature, [3,4,15] The response (which in most cases leads to selfassembly) takes place when an AmBCs is dissolved in an aqueous medium. The phenomenon can be reversible, upon the variation of its environmental conditions (e.g., pH and temperature), [13] Thermoresponsive polymers show a distinct change in properties upon a large or small change in solution temperature. With regards to the thermoresponsive transition, depending on the system under investigation, there is a critical temperature at which segregation is due to the solvophobic block self-assembly, and in particular formation/disassembly of polymer aggregates in solution, [13,16] Thermoresponsive polymers are used for biomedical applications including drug delivery, [17] tissue engineering and gene delivery, [18,19] Materials with custom-designed responsive elements have the ability to significantly enhance the delivery of therapeutics agents. [17] In the center of interest of the present study is the development of new thermoresponsive AmBCs. The RAFT polymerization process has quickly become a powerful and versatile technique for the synthesis of a wide range of organic diblock copolymers and nano-objects of controllable size, morphology and surface functionality. This approach offers many potential applications, such as efficient microencapsulation vehicles and sterilizable thermoresponsive hydrogels for the cost-effective long-term storage of mammalian cells. [1] More recently, block copolymer self-assembly in solution to form spherical micelles, worm-like micelles and vesicles has been studied with potential applications in the field of drug, protein/peptide, and nucleic acid delivery. [1][2][3][4]13] In practice, relatively few vinyl monomers that may lead to thermoresponsive polymers have been tested for RAFT polymerization, such as N-isopropyl acrylamide (NIPAM), [7,14,[20][21][22][23][24] oligo(ethylene glycol) methacrylates, [25,26] vinyl-caprolactam, [27] and 2-hydroxypropyl methacrylate (HPMA). [1,21,25,28,29] In each case the corresponding Polymer Chemistry Thermoresponsive amphiphilic poly(hydroxyl propyl methacrylate)-b-poly(oligo ethylene glycol methacrylate) block copolymers (PHPMA-b-POEGMA) are synthesized by RAFT polymerization, with different compositions and molecular weights. The copolymers are molecularly characterized by sizeexclusion chromophotography, and 1 H NMR spectroscopy. Dynamic light scattering (DLS) and static light scattering (SLS) experiments in aqueous solutions show that the copolymers respond to temperatu...
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.
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