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.

De novo identification and quantification of single amino-acid variants in human brain

The detection of single amino-acid variants （SAVs） usually depends on single-nucleotide polymorphisms （SNPs） database. Here, we describe a novel method that discovers SAVs at proteome level independent of SNPs data. Using mass spectrometry-based de novo sequencing algorithm, peptide-candidates are identified and compared with theoretical protein database to generate SAVs under pairing strategy, which is followed by database re-searching to control false discovery rate. in human brain tissues, we can confidently identify known and novel protein variants with diverse origins. Combined with DNA/RNA sequencing, we verify SAVs derived from DNA mutations, RNA alternative splicing, and unknown post-transcriptional mechanisms. Furthermore, quantitative analysis in human brain tissues reveals several tissue-specific differential expressions of SAVs. This approach provides a novel access to high-throughput detection of protein variants, which may offer the potential for clinical biomarker discovery and mechanistic research.

Preprocessing of Tandem Mass Spectrometric Data Based on Decision Tree Classification

In this study, we present a preprocessing method for quadrupole time-of-flight （Q-TOF） tandem mass spectra to increase the accuracy of database searching for peptide （protein） identification. Based on the natural isotopic information inherent in tandem mass spectra, we construct a decision tree after feature selection to classify the noise and ion peaks in tandem spectra. Furthermore, we recognize overlapping peaks to find the monoisotopic masses of ions for the following identification process. The experimental results show that this preprocessing method increases the search speed and the reliability of peptide identification.