Machine learning methods for the detection of explosives, drugs and precursor chemicals gathered using a colorimetric sniffer sensor.

Colorimetric sensing technology for the detection of explosives, drugs, and their precursor chemicals is an important and effective approach. In this work, we use various machine learning models to detect these substances from colorimetric sensing experiments conducted in controlled environments. The detection experiments based on the response of a colorimetric chip containing 26 chemo-responsive dyes indicate that homemade explosives (HMEs) such as hexamethylene triperoxide diamine (HMTD), triacetone triperoxide (TATP), and methyl ethyl ketone peroxide (MEKP) used in improvised explosives devices are detected with true positive rate (TPR) of 70-75%, 73-90% and 60-82% respectively.

The Influence of Structure Heights and Opening Angles of Micro- and Nanocones on the Macroscopic Surface Wetting Properties.

We discuss the influence of surface structure, namely the height and opening angles of nano- and microcones on the surface wettability. We show experimental evidence that the opening angle of the cones is the critical parameter on sample superhydrophobicity, namely static contact angles and roll-off angles. The textured surfaces are fabricated on silicon wafers by using a simple one-step method of reactive ion etching at different processing time and gas flow rates.