Effect of Brucea javanica Oil on the Toxicity of ß-Cypermethrin Emulsifiable Concentrate Formulation.

Enhancing the performance of traditional pesticide formulations by improving their leaf surface wetting capabilities is a crucial approach for maximizing the pesticide efficiency. This study develops an emulsifiable concentrate (EC) of 4.5% ß-cypermethrin containing Brucea javanica oil (BJO). The incorporation of BJO aims to improve the leaf-wetting properties of the EC formulation and enhance its insecticidal effectiveness. The droplet size and emulsion characteristics of ß-CYP EC emulsion with varying concentrations of the emulsifier were evaluated, and changes after incorporating BJO were assessed to develop the optimal formulation. A comprehensive comparison was conducted among commercial 4.5% ß-cypermethrin EC (ß-CYP EC-1), 4.5% ß-cypermethrin EC with BJO (ß-CYP EC-2), and 4.5% ß-cypermethrin EC without BJO (ß-CYP EC-3). This comparison encompassed various factors including storage stability, insecticidal activity, cytotoxicity, and wetting performance on cabbage leaves. The results indicated that the ideal emulsifier concentration was 15% emulsifier 0201B. ß-CYP EC-2 demonstrated superior wetting properties on cabbage leaves (the wetting performance of ß-CYP EC-2 emulsion on cabbage leaves is 2.60 times that of the ß-CYP EC-1 emulsion), heightened insecticidal activity against the third larvae of Plutella xylostella [diamondback moth (DBM)] [the insecticidal activity of the ß-CYP EC-2 emulsion against the third larvae of DBM is 1.93 times that of the ß-CYP EC-1 emulsion (12 h)], and more obvious inhibitory effects on the proliferation of DBM embryo cells than the other tested formulations. These findings have significant implications for advancing pest control strategies and promoting sustainable and effective agricultural practices.

Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr-Cr3C2 Coatings Based on a Hierarchical Neural Network.

High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr-Cr3C2 coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.