A gem-dinitromethyl group was successfully introduced into the TNBI•2H 2 O structure (TNBI: 4,4′,5,5′-tetranitro-2,2′-bi-1Himidazole) to obtain 1-(dinitromethyl)-4,4′,5,5′-tetranitro-1H,1′H-2,2′-biimidazole (DNM-TNBI). Benefiting from the transformation of an N-H proton into a gem-dinitromethyl group, the current limitations of TNBI were well solved. More importantly, DNM-TNBI has high density (1.92 g•cm −3 , 298 K), good oxygen balance (15.3%), and excellent detonation properties (D v = 9102 m•s −1 , P = 37.6 GPa), suggesting that it has great potential as an oxidizer or a highperformance energetic material. Letter pubs.acs.org/OrgLett

In this article, selective laser melting (SLM) equipment is used to print 316L stainless steel parts under different process parameters, and the surface roughness of the parts is measured. Based on back propagation neural networks (BP neural networks, BPNN), the upper surface roughness prediction model is established. The laser power, scanning speed, and scanning interval are used as model input, and the surface roughness of the workpiece is output. This model can easily and quickly predict the surface roughness of SLM metal printing, with high prediction accuracy, and can provide a basis for the optimization of SLM process parameters.

3D printed metal crowns can be used for dental restorations. The main quality control challenge of these dental metal is the method of quality inspection. Electronic quality is a process by which the quality of the process and the parts produced can be checked online, thereby improving the process and reducing the time it takes for the entire process. Here, we propose a combination of 3D scanning and 3D measurement for 3D inspection of metal crowns. The data extracted from the 3D printed metal crowns were used as case studies to prove the proposed methodology. The obtained results confirm that the new method has very high classification accuracy compared with the traditional inspection methods, and thus yields excellent results. Moreover, the proposed approach is capable to archive 3D models of the parts and achieve rapid quality control. This paper forms the basis for solving many other similar problems that occur in 3D printing related industries.