The composite material based on reinforcement of polyamide filaments enclosed by a nonwoven matrix of nanoscaled bioresorbable poly(3-hydroxybutyrate) fibers was developed for application as an artificial ligament implant. The aim of this study was to investigate biodegradability and biocompatibility of the developed implant, as well as its stress-strain properties. The study results show the polyamide core of the implant has stress-strain properties comparable with a natural ligament. Simultaneously, the polyhydroxybutyrate external layer provides high biocompatibility and bioresorbability of the developed implant. The material has proven to be effective under in vivo tests with experimental rats as a ligament replacement for damaged Achilles tendons. Due to cell attachment and growth on the fibrous matrix during 5 weeks postsurgery, regenerated connective tissue was formed substituting for the polymeric implant, which confirmed its efficiency in contrast to the polyamide filament implant with a much longer resorption time. The results obtained indicate application prospects of polyamide-polyhydroxybutyrate implants for reconstructive surgery. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 2708-2713, 2018.
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http://dx.doi.org/10.1002/jbm.a.36469 | DOI Listing |
3D Print Addit Manuf
December 2024
Institute of Materials Science, Joining and Forming (IMAT), BMK Endowed Professorship for Aviation, Graz University of Technology, Graz, Austria.
Polymers (Basel)
November 2024
Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy.
Usage of continuous fibers as a reinforcement would definitely increase the mechanical properties of 3D-printed materials. The result is a continuous fiber-reinforced composite obtained by additive manufacturing that is not limited to prototyping or non-structural applications. Among the available continuous reinforcing fibers, basalt has not been extensively studied in 3D printing.
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November 2024
Department of Plastics Engineering, University of Massachusetts Lowell, 1 University Ave, Lowell, MA, 01854, USA.
In this work, a heat transfer model is developed for thermally-driven material extrusion additive manufacturing of semicrystalline polymers that considers the heat generated during crystallization by coupling crystallization kinetics with heat transfer. The materials used in this work are Technomelt PA 6910, a semicrystalline hot melt adhesive with sub-ambient glass transition temperature (T) and slow crystallization, and PA 6/66, a traditional semicrystalline polyamide with a higher T and fast crystallization. The coupled model shows that the released heat during crystallization depends on material selection, with Technomelt PA 6910 and PA 6/66's temperatures increased by less than 1 °C and up to 6.
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October 2024
School of Textile Science and Engineering, Shaoxing University, Shaoxing, Zhejiang 312000, China.
Flexible capacitive sensors have attracted the attention of researchers owing to their simple structure, ease of realization, and wearability. Currently, flexible capacitive sensors mainly have three-dimensional and two-dimensional structures, which are subject to several limitations in their applications. A low-cost, high-efficiency, and continuously processable process was used to wrap nylon DTY (PA) filaments on the surface of silver-coated nylon (SCN) core yarns and impregnate them with waterborne polyurethane (WPU) to obtain SCN/PA/WPU composite yarns, which were then utilized in the design of SCN/PA/WPU for the preparation of one-dimensionally structured flexible capacitive sensors.
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October 2024
Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran.
Different forms of close-packed yarns can be produced by varying the number of monofilaments in the core region, ranging from one to five. Numerous efforts have been made to model or simulate the mechanical response of close-packed yarns; however, previous studies have predominantly focused on one or two monofilaments in the core. In this study, we propose an analytical approach that combines a geometrical model with an artificial neural network (ANN) to predict the tensile behavior of close-packed yarns containing 2 to 5 monofilaments in the core region.
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