Xue-Xue Zhang,Su-Lan Yang,Zhi-Hua Xue,Shuwen Chen,Qi-Long Yan
High-energy solid propellants with lower pressure exponent, lower burn rate but with high combustion efficiency are usually desired. The use of catalysts in less amount but maintained high efficiency is essential for high-energy content and better combustion performances. The fine combination of catalyst with energetic fillers as oxidizer has been found to be a promising strategy to achieve this goal. In this paper, a widely used nitramine oxidizer, cyclotrimethylenetrinitramine (RDX) has been selected as a typical example, which was first intercalated for lower sensitivity and lower burn rate, and then it was coated with in-situ synthesized nanosized metal oxides as catalysts for lower pressure exponent using polydopamine as the interfacial binding layer. Two types of catalyst-coated hybrid RDX crystals (named as qy-RDX@Fe 2 O 3 and qy-RDX@CuO) have been prepared and characterized. It is found that the in-situ grafted nano-Fe 2 O 3 and CuO crystals have significant catalytic effect on qy-RDX, which increased the heat release of qy-RDX@Fe 2 O 3 and qy-RDX@CuO from 1747 J·g −1 to 2159 J·g −1 and 2133 J·g −1 , respectively. The results show that both qy-RDX@Fe 2 O 3 and qy-RDX@CuO could reduce the ignition delay time and largely improve the burn rate of corresponding propellants at lower pressure (e.g. increased from 3.14 mm·s −1 to 5.50 mm·s −1 at 1 MPa) by coating only 0.13 wt% Fe 2 O 3 and 0.15 wt% CuO catalysts, resulting in much lower pressure exponent (from 0.32 to 0.15). In particular, the decomposition heat of propellant containing only 10 wt% of qy-RDX@CuO is 1.5 times higher than the reference blank sample, so that its flame temperature was increased from 1800 °C to 2400 °C due to higher combustion efficiency with more energy release.
The introduction of organic solvent nanofiltration (OSN) technology into the crystallization process of energetic materials (EMs) can enhance solvent utilization and enable precise control of supersaturation during crystallization. However, incorporating OSN technology increases the number of tunable variables in the crystallization process, complicating the optimization of process parameters. Consequently, there is a pressing need to develop an efficient method for optimizing crystallization parameters based on experimental data. Herein, 1,3,5-trinitro-1,3,5-triazinane (RDX) crystals were selected as the research subject, and a method for optimizing experimental operating conditions for nanofiltration-assisted cooling crystallization (NF-CCr) was established using machine learning (ML) techniques. This method includes feature engineering, model prediction interpretation, and verification. In the feature engineering, 85 sets of high-quality experimental datasets were collected, and dimensionality reduction was performed using Principal Component Analysis (PCA) and correlation analysis to enhance the model’s trainability while preserving data variability. Four ML models were trained and optimized: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN). The prediction yield R2 of the BPNN regression model reached 0.89, while the RF binary classification model achieved an accuracy of 91.66% for particle size and 84.72% for sphericity. The SHapley Additive exPlanation (SHAP) method was employed to elucidate the intrinsic correlations between experimental operating conditions and product characteristics. Finally, under the experimental conditions predicted by the RF classification model, RDX crystals with high sphericity (>85%), large particle size (>100 μm), and high purity (>99%) were successfully obtained, achieving an experimental verification accuracy of 80%. This study provides a data-driven optimization solution for the development of complex crystallization processes for EMs.
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