Production of singlet oxygen at 530 nm in a flow reactor using novel BODIPY-based polymers as heterogeneous photosensitisers.
Composites based on fully bio-based epoxy-amine resins reinforced with recycled carbon fibers were obtained and compared to petro-and partially bio-based benchmark materials. Rheology measurements showed that fully bio-based resins are better suited for resin infusion processes as they have higher gel-point temperatures than the benchmark formulations. Thermogravimetric analyses demonstrated that the resin thermal resistance was increased in the presence of recycled fibers. Dynamic mechanical analyses showed that the glass transition temperatures of fully bio-based composites are higher by 30 °C than other reported bio-based materials, making them better suited for industrial applications. Fully bio-based composites exhibited an improvement of mechanical properties between 5 and 85% compared to the benchmark materials. Indeed, a better fiber wetting behavior was observed by scanning electron microscopy analyses, enhancing fiber−matrix interfaces, resulting in higher mechanical behavior. This unprecedented work highlights fully bio-based epoxy-amine resins reinforced with recycled carbon fibers as a realistic low carbonfootprint alternative for functional applications.
Thermal conductivity is an important material property for thermo-mechanical calculations, as mechanical properties strongly depend on the temperature and heat distribution in the manufactured parts. Although several suggestions for approximation formulae have been made, existing experimental data are rare and are not comparable due to different measurement methods. In addition, scarcely has the thermal conductivity in both the fiber direction and transverse direction been studied. The aim of the current research is to show the influence of carbon fiber volume content on the thermal conductivity of laminates. The values are then used to verify the micromechanical models used in the literature. A strong influence on the thermal conductivity could be determined. For the transverse thermal conductivity, the correlation was exponential; for the conductivity in the fiber direction, a linear correlation was found.
In this study, an applicative-oriented epoxy−amine thermoset was toughened with 1, 3, and 5 wt % of polydimethylsiloxane (PDMS) core−shell-particles (CSP), and the influence of the materials morphology on their functional mechanical properties was investigated for the first time by a multiscale experimental approach. First, rheological measurements showed faster gel behavior in matrices containing PDMS-CSP. Then, 1 wt % PDMS-CSP increased the fracture toughness K IC by more than 100%, as well as giving an increase on the glass transition temperature (T g ) from 90 to 98 °C and Young's modulus E by 15%. More interestingly, the increase in T g and E is nonlinear with PDMS-CSP content, with the highest values observed for 1 wt % toughener. DMTA and time domain NMR measurements showed that the matrices cross-link density and network degree of homogeneity do not depend linearly with PDMS-CSP content, and they follow the same trend as for T g and E. By associating both techniques, a linear relationship was obtained between the network structure probed by NMR and the thermomechanical behavior by DMTA, meaning that the macroscopic properties depend additionally on the network morphology. Finally, by combining all of the aforementioned techniques through a multiscale approach, it was shown that PDMS-CSP acts as a cross-link nucleating agent for the epoxy−amine matrix, inducing higher cross-link densities, thus yielding higher functional mechanical properties. Such an unprecedented synergetic experimental approach associated with a multiscale nanoparticle interaction and monomer cross-linking provides an original and robust manner to determine and deepen the understanding of key structure−macroscopic property relationships for applicativeoriented epoxy thermosets.
The use of machine learning (ML) models to screen new materials is becoming increasingly common as they accelerate material discovery and increase sustainability. In this work, the chemical structures of 16 epoxy resins and 19 curing agents were used to build an ML ensemble model to predict the glass transition ($$T_g$$ T g ) of 94 experimentally known thermosets. More than 1400 molecular descriptors were calculated for each molecule, of which 119 were chosen based on feature selection performed by principal component analysis. The quality of the trained model was evaluated using leave-one-out cross-validation, which yielded a mean absolute error of 16.15$$^{\circ }$$ ∘ C and an $$R^2$$ R 2 value of 0.86. The trained model was also used to predict $$T_g$$ T g for 4 randomly selected resin/hardener combinations for which no experimental data were available. The same combinations were then prepared and measured in the laboratory to further validate the ML model. Excellent agreement was found between experimental and predicted $$T_g$$ T g values. The current ML model was created using only theoretical features, but could be further improved by adding experimental or quantum mechanical properties of the individual molecules as well as experimental processing parameters. The results presented here contribute to improving sustainability and accelerating the discovery of novel materials with desired target properties. Graphical Abstract
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