Abstract. We introduced a general concept to create smart, (multi)functional interphases in polymer composites with layered reinforcements, making use of 3D printing. The concept can be adapted for both thermoplastic and thermoset matrix-based composites with either thermoplastic-or thermoset-enriched interphases. We showed feasibility using an example of a composite containing a thermoset matrix/thermoplastic interphase. Carbon fiber unidirectional reinforcing layers were patterned with poly(ɛ-caprolactone) (PCL) through 3D printing, then infiltrated with an amine-cured epoxy (EP). The corresponding composites were subjected to static and dynamic flexure tests. The PCL-rich interphase markedly improved the ductility in static tests without deteriorating the flexural properties. Its effect was marginal in Charpy impact tests, which can be explained with effects of specimen and PCL pattern sizes. The PCL-rich interphase ensured self-healing when triggered by heat treatment above the melting temperature of PCL.
We studied the effect of a multilevel presence of carbon-based reinforcements—a combination of conventional load-bearing unidirectional carbon fiber (CF) with multiwalled carbon nanotubes (CNT) and conductive CNT-containing nonwoven carbon nanofabric (CNF(CNT))—on the fire performance, thermal conductivity, and mechanical properties of reference and flame-retarded epoxy resin (EP) composites. The inclusion of carbon fibers and flame retardant reduced the peak heat release rate (pHRR) of the epoxy resins. The extent to which the nanoreinforcements reduced the pHRR depended on their influence on thermal conductivity. Specifically, high thermal conductivity is advantageous at the early stages of degradation, but after ignition it may lead to more intensive degradation and a higher pHRR; especially in the reference samples without flame retardant. The lowest pHRR (130 kW/m2) and self-extinguishing V-0 UL-94 rating was achieved in the flame-retarded composite containing all three levels of carbon reinforcement (EP + CNF(CNT) + CNT + CF FR). The plasticizing effect of the liquid flame retardant impaired both the tensile and flexural properties; however, it significantly enhanced the impact resistance of the epoxy resin and its composites.
Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Project no. RRF-2.3.1-21-2022-00004 (MILAB) has been implemented with the support provided by the European Union. Introduction Cardiopulmonary exercise testing (CPET)-derived peak oxygen uptake (VO2/kg) is a well-established parameter of exercise capacity allowing the quantification of athletic performance. Although VO2/kg is mainly influenced by anthropometric and demographic factors, several studies demonstrated strong associations between resting echocardiography-based measures and VO2/kg. Artificial intelligence could incorporate information from both features, thus enabling a more accurate prediction of exercise capacity in athletes. Aim Accordingly, we aimed to implement a deep-learning (DL) model that uses 2D echocardiography (2DE)-based apical 4-chamber view videos on top of the anthropometric features (age, sex, body surface area [BSA]) to predict VO2/kg and then assess the model’s performance in a large cohort of athletes. Methods We retrospectively identified 422 competitive athletes (15.4±7.3 training hours/week) who underwent resting 2DE evaluation and then CPET to determine VO2/kg (52.7 ± 7.7 mL/kg/min). To predict VO2/kg values, we trained a deep neural network that can process both modalities of the inputs (i.e. 2DE videos and anthropometric data such as age, sex and BSA) simultaneously (Figure 1). We applied 5-fold cross-validation and used mean squared error (MSE), mean absolute error (MAE), and R squared (R2) metrics to measure our model’s performance. Then, we compared the results with linear regression that was trained using only the 3 anthropometric factors (age, gender, BSA). Additionally, after finalization of the DL-based model, we prospectively recruited further 25 competitive athletes with both 2DE and CPET performed to validate our model. Results Using 2DE videos, our DL-based model was able to achieve an accurate prediction of VO2/kg with an MSE of 35.27, MAE of 4.62, and an R2 of 0.393. In comparison, the linear regression model using only anthropometric factors had worse predictive performance in all metrics with an MSE of 40.51, MAE of 4.88, and R2 of 0.303. In addition, we compared the predictive performance of the DL-based and the linear regression models by their respective squared error values using the Wilcoxon test. Our DL-based model had a significantly better performance compared to the linear regression model (Wilcoxon p = 0.006). In the prospective dataset, our DL-based model achieved an MSE of 16.69, MAE of 3.42, and an R2 of 0.169, whereas the linear regression model was inferior with an MSE of 25.43, MAE of 4.51, and an R2 of −0.268. The DL-based model showed a significantly better performance (Wilcoxon p<0.001). Conclusions Using our DL-based approach on our large athlete database, we were able to implement and prospectively validate a model that incorporated 2DE videos to predict VO2/kg more accurately compared to using anthropometric factors alone. DL techniques may advance sports medicine by personalized monitorization of training phases and accurate prediction of athletic performance.
This article presents an investigation of the properties of interfacial engineered carbon fiber (CF) reinforced polymer (CFRP) composites. Poly(ϵ-caprolactone) (PCL) is used as an interface engineering/interlayer material to modify the interfacial shear strength (IFSS) between the phases, with which it is aimed to increase the pseudo-ductility of CFRPs. A stable crack propagation behavior can be achieved through interfacial engineering due to the locally weakened connection between the resin and the fiber reinforcement. Various grid patterns of PCL interfacial additive are designed, and 3D printed on the unidirectional (UD) CF (UDCF) surfaces by fused deposition modeling (FDM) and the UDCF is infiltrated with an amine-cured epoxy (EP). The resulting composites are subjected to mechanical tests. In each case, the PCL-rich interphase increases ductile pseudo-behavior, and the type of grid affects the mechanical response.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.