Microfluidic preparation of surfactant-free ultrafine DAAF with tunable particle size for insensitive initiator explosives

High purity and ultrafine DAAF(u-DAAF)is an emerging insensitive charge in initiators.Although there are many ways to obtain u-DAAF,developing a preparation method with stable operation,accurate control,good quality consistency,equipment miniaturization,and minimum manpower is an inevitable requirement to adapt to the current social technology development trend.Here reported is the microfluidic preparation of u-DAAF with tunable particle size by a passive swirling microreactor.Under the guidance of recrystallization growth kinetics and mixing behavior of fluids in the swirling microreactor,the key parameters(liquid flow rate,explosive concentration and crystallization temperature)were screened and optimized through screening experiments.Under the condition that no surfactant is added and only experimental parameters are controlled,the particle size of recrystallized DAAF can be adjusted from 98 nm to 785 nm,and the corresponding specific surface area is 8.45 m^(2)·g^(-1)to 1.33 m^(2)·g^(-1).In addition,the preparation method has good batch stability,high yield(90.8%-92.6%)and high purity(99.0%-99.4%),indicating a high practical application potential.Electric explosion derived flyer initiation tests demonstrate that the u-DAAF shows an initiation sensitivity much lower than that of the raw DAAF,and comparable to that of the refined DAAF by conventional spraying crystallization method.This study provides an efficient method to fabricate u-DAAF with narrow particle size distribution and high reproducibility as well as a theoretical reference for fabrication of other ultrafine explosives.

Model-free conditional independence feature screening for ultrahigh dimensional data

Feature screening plays an important role in ultrahigh dimensional data analysis.This paper is concerned with conditional feature screening when one is interested in detecting the association between the response and ultrahigh dimensional predictors(e.g.,genetic makers)given a low-dimensional exposure variable(such as clinical variables or environmental variables).To this end,we first propose a new index to measure conditional independence,and further develop a conditional screening procedure based on the newly proposed index.We systematically study the theoretical property of the proposed procedure and establish the sure screening and ranking consistency properties under some very mild conditions.The newly proposed screening procedure enjoys some appealing properties.(a)It is model-free in that its implementation does not require a specification on the model structure;(b)it is robust to heavy-tailed distributions or outliers in both directions of response and predictors;and(c)it can deal with both feature screening and the conditional screening in a unified way.We study the finite sample performance of the proposed procedure by Monte Carlo simulations and further illustrate the proposed method through two real data examples.