Although jute fiber-reinforced PLA composites show strong application prospects, their low mechanical properties limit their applications to some extent. In this paper, nano-SiO2 particles as well as nano SiO2 modified by coupling agents which can efficiently improve the strength and toughness of composite materials are introduced into the PLA matrix. The bending, stretching and thermal properties of designed jute/PLA nonwoven composites were studied. The study shows that the nano-SiO2 particles are beneficial to the interface performance between the PLA matrix and jute leading to improvement in the mechanical properties and thermal stability. Moreover, thermomechanical properties indicate that the addition of SiO2 can improve the jute/PLA interfacial adhesion and increase the glass transition temperature of the material. Finally, toughening mechanism of nano-SiO2 particles in the jute/PLA composite was analyzed.
Cu/Ag(I) were introduced into iodoplumbate systems to produce two new heterometallic iodoplumbates with viologen as templates, i.e. (PV)2(Pb2Cu2I10) (1) and [(BV)(Pb2AgI7)]n (2) (PV2+ = propyl viologen, BV2+ = benzyl viologen), in which the common connection of PbI6 units have been remarkably altered. In (PV)2(Pb2Cu2I10) (1), two PbI6 octahedra are bridged by two CuI4 tetrahedra via face-sharing to give a (Pb2Cu2I10)4? cluster, but the ternary one-dimensional polymeric (Pb2AgI7)n2n? of [(BV)(Pb2AgI7)]n (2) is assembled from edge-sharing AgI4 tetrahedra and PbI6 octahedra. Their optical band gaps and fluorescence were also discussed. The absorption edges of haloplumbates could be engineered by introduction of suitable conjugated molecules as templates. 相似文献
Riboflavin (RF) was considered to be possessed of photoactivity to generate reactive oxygen species (ROS) under ultraviolet (UV) light, which is thought to be a favorable antibacterial candidate. Herein, RF was incorporated into chitosan (CS) coatings and treated under UV with different exposure times (2, 4, and 6 h) to improve the physicochemical and antibacterial properties. The results showed that the light transmittance and antibacterial performance of chitosan coatings gradually increased with the extension of the UV irradiation time. The antibacterial ability of chitosan coatings correlated with the generation of ROS: ∙OH and H2O2, which achieved 1549.08 and 95.48 μg/g, respectively, after 6 h irradiation. Furthermore, the chitosan coatings with UV irradiation also reduced the pH value, total volatile basic nitrogen (TVB-N), ΔE, and total viable counts (TVC) and improved sensory attributes of pork. In conclusion, the UV irradiated chitosan coatings could be used as an environmentally friendly antimicrobial packaging material to effectively delay the spoilage of pork, maintain its sensory quality and prolong its shelf life. 相似文献
Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.Based on GNNs and active learning, DeepReac+ is designed as a universal framework for quantitative modeling of chemical reactions. It takes molecular structures as inputs directly and adapts to various prediction tasks with fewer training data.相似文献