Size, Morphology and Crystallinity Control Strategy of Ultrafine HMX by Microfluidic Platform

The crystal structure has a great influence on mechanical sensitivity and detonation performance of energetic materials. An efficient microfluidic platform was applied for size, morphology, and crystallinity controllable preparation of ultrafine HMX. The microfluidic platform has good mixing performance, quick response, and less reagent consumption. The ultrafine γ-HMX was first prepared at room temperature by microfluidic strategy, and the crystal type can be controlled accurately by adjusting the process parameters. With the increase in flow ratio, the particle size decreases gradually, and the crystal type changed from β-HMX to γ-HMX. Thermal behavior of ultrafine HMX shows that γ→δ is easier than β→δ, and the phase stability of HMX is β > γ > δ. Furthermore, the ultrafine β-HMX has higher thermal stability and energy release efficiency than that of raw HMX. The ultrafine HMX prepared by microfluidic not only has uniform morphology and narrow particle size distribution, but also exhibits high density and low sensitivity. This study provides a safe, facile, and efficient way of controlling particle size, morphology, and crystallinity of ultrafine HMX.

A deep learning-based framework for automatic analysis of the nanoparticle morphology in SEM/TEM images

Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are important tools for characterizing nanomaterial morphology. Automatic analysis of the nanomaterial morphology in SEM/TEM images plays a crucial role in accelerating research on nanomaterials science. However, achieving a high-throughput automated online statistical analysis of the nanomaterial morphology in various complex SEM/TEM images is still a challenging task. In this paper, we propose a universal framework based on deep learning to perform a fast and accurate online statistical analysis of the nanoparticle morphology in complex SEM/TEM images. The proposed framework consists of three stages that are nanoparticle segmentation using a powerful light-weight deep learning network (NSNet), nanoparticle shape extraction, and statistical analysis. The experimental results show that NSNet in the proposed framework has achieved an accuracy of 86.2% and can process 11 SEM/TEM images per second on an embedded processor. Compared with other semantic segmentation models, NSNet is an optimal choice to ensure that the proposed framework still achieves accurate and fast segmentation even in SEM/TEM images with high background interference, extremely small nanoparticles and dense nanoparticles. Meanwhile, the equivalent diameter and Blaschke shape coefficient of the nanoparticle obtained by the proposed framework are 17.14 ± 5.9 and 0.18 ± 0.04, which are well consistent with those of manual statistical analysis. In short, the proposed framework has a promising future in driving the development of automatic and intelligent analysis technology for nanomaterial morphology.

Microchannel-confined crystallization: shape-controlled continuous preparation of a high-quality CL-20/HMX cocrystal

Shape-controlled continuous preparation of a high-quality CL-20/HMX cocrystal has been realized through a microchannel-confined crystallization strategy.

Nonlinear microfluidics: device physics, functions, and applications

The microfluidic flow is typically laminar due to the dominant viscous effects. At Reynolds numbers far below 1 (Re ≪ 1), the fluid inertia can be neglected. For the steady flow of incompressible Newtonian fluids, it approaches linear Stokes flow. At intermediate Re, there exists a weak-inertia flow regime where secondary flows such as Dean vortices are accessible for microfluidic manipulations. Apart from the fluid inertia, other nonlinear factors such as the non-Newtonian fluid properties, concurrent flow of dissimilar fluids, compliant fluidic structures and stimuli-responsive materials can also cause intriguing flow behaviours. Through proper designs, they can be applied for a variety of microfluidic components including mixers, valves, oscillators, stabilizers and auto-regulators etc., greatly enriching the microfluidic flow control and manipulation strategies. Due to its unique working characteristics and advantages, nonlinear microfluidics has increasingly attracted extensive attention. This review presents a systematic survey on this subject. The designs of typical nonlinear microfluidic devices, their working mechanisms, key applications, and the perspective of their future developments will be discussed. The nonlinear microfluidic techniques are believed to play an essential role in the next generation of highly-integrated, automated, and intelligent microfluidics.

Multi-size control of homogeneous explosives by coaxial microfluidics

A coaxial microfluidic platform was developed for the multi-size control of homogeneous explosives.