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The poor mechanical properties and disadvantages of catalysts limit the application of self-healing materials. To address these issues, catalyst-free self-healing bio-based polymers (AESO−EMPA polymers) with robust mechanical properties were prepared using epoxidized maleopimaric anhydride (EMPA) and aminated epoxidized soybean oil (AESO). The AESO−EMPA polymers are recyclable and exhibit self-healing and shape memory because of the dual-dynamic network of multiple H-bonds and dynamic ester bonds in the structure. Under the synergistic catalysis of the tertiary amines and hydroxyl groups originated from the polymers, the polymers in this study achieve network rearrangement without the need for additional catalysts. The polymers also exhibit excellent mechanical properties with a tensile strength of 29.1 ± 0.25 MPa and a T g of 80.2 °C owing to the unique rigid backbone of rosin and the dual-dynamic network. The AESO−EMPA polymers can be used as reusable adhesives and exhibit excellent shear strength and repair rates.

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The application of carbon fiber-reinforced composites (CFRCs) is limited owing to the difficulty of chemically recycling carbon fibers (CFs). To address this problem, we cured tung oil-based triglycidyl ester (TOTGE) with menthane diamine (MDA) in order to obtain a chemically degradable bio-based vitrimer matrix. The obtained vitrimer matrix could undergo a dynamic transesterification reaction catalyzed by the tertiary amines generated from the curing reaction. The TOTGE−MDA vitrimer matrix shows excellent self-healing performance, physical reprocessing, and shape memory properties. Meanwhile, CFRCs based on the TOTGE−MDA vitrimer matrix also exhibited excellent reprocessing, self-adhesive, and shape memory properties. The CFRC underwent rapid chemical degradation with ethanolamine at 90 °C. The performance of the recycled CFs was similar to that of the virgin CFs. This work provides an effective solution to facilitate the sustainable development of CFRCs.

In this study, we will report on the synthesis and application of efficient botanical agrochemicals from turpentine for sustainable crop protection. Two series of turpentine derived secondary amines were synthesized and identified by FT-IR, 1H NMR, 13C NMR, and HRMS. The herbicidal activities against Echinochloa crus-galli were evaluated. The potential toxicity of the synthesized compounds was tested by MTT cytotoxicity analysis. The effect of structure of the synthesized secondary amines and corresponding Schiff base compounds on their activities was investigated by quantitative structure–activity relationship (QSAR) study. All target products were found to be low toxicity, with similar or higher herbicidal activities than commercial herbicides diuron and Glyphosate. Results of QSAR study showed that a best four-descriptor QSAR model with R 2 of 0.880 and R loo 2 of 0.818 was obtained. The four descriptors most relevant to the herbicidal activities are the min valency of a N atom, the max total interaction for a C−H bond, the relative number of aromatic bonds, and the min partial charge (Q min ).

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Isopropyl cresols were prepared by a two-step process: monoterpene oxidation catalyzed by CrO 3 and terpenone isomerization catalyzed by 13X molecular sieves. 5-Isopropyl-3methylphenol and carvacrol were prepared from 3-carene, whereas thymol and 4-isopropyl-3-methylphenol were synthesized from α-pinene. The two reactions followed an identical procedure, but the reaction of 3-carene was more efficient, benefiting from the three-membered ring in the structure. 5-Isopropyl-3-methylphenol, thymol, and carvacrol were found to show higher herbicidal activity than glyphosate toward the root growth of barnyard grass. However, they exhibited slightly lower activity in preventing shoot growth. Moreover, 5isopropyl-3-methylphenol showed higher herbicidal activity than its isomers, which indicates that the relative position of substitutes is significantly associated with the herbicidal activity of isopropyl cresols.[a] J.

For traditional network anomaly detection system, the detection performance is related to the selected features and training dataset. But traditional methods adopt handcraft feature selection, which requires heavy human labour and relies on the experts’ knowledge and experience. Besides, the collected dataset for training is not balanced, which makes the prediction of the trained model tends to be biased to the majority class. In this paper, a one-class network anomaly detection model based on the stacked autoencoders was proposed. We use the stacked autoencoders to select the prominent features from the raw collected data, then apply the one-class classification algorithm support vector data description to train a classifier to identify the network traffic into normal data and anomalous data. The experimental results demonstrate the promising results of our approach for network anomaly detection.