Machine Learning Improves Trace Explosive Selectivity: Application to Nitrate-Based Explosives

Aggregation-induced emission enhancement (AIEE) active bispyrene-based fluorescent probe: "turn-off" fluorescence for the detection of nitroaromatics

The detection of nitroaromatic explosives in real samples is essential for environmental monitoring because of their strongly powerful nature and wide applications in industries. Aggregation-induced emission enhancement (AIEE) active fluorescent probe has been widely employed to detect nitroaromatic explosives. Hereby, a simple V-shaped bispyrene-based fluorescent probe (called py-o) with AIEE properties was designed and synthesized, which was fully charactered by 1D NMR, ESI, FTIR, and 2D NOESY spectra. The py-o displayed bright blue-green fluorescence excimer emission at 480 nm in DMF/H2O (v/v 1:1). It is observed that the fluorescence excimer emission of py-o at 480 nm was quenched by PA in solution with a quenching constant of 5.45 × 104 M-1, and the limit of detection was approximately 0.139 μM. The details of the sensing mechanism were explained using 1H NMR titrations, Job's plot and Bensi-Hildebrand methods, which revealed a 1:1 binding ratio via the π-π interactions between PA and py-o. Meanwhile, it exhibited outstanding anti-interference ability in the detection of PA when interfering analytes were added under the same conditions. Furthermore, low-cost thin-layer chromatography (TLC) plates coated with py-o were developed as fluorescent tools for naked-eye detection of PA in the solid state. Therefore, this work provides a new method for constructing an AIEE fluorescent probe for the detection of nitroaromatic explosives to utilize in environmental monitoring.

Membrane-Based Pulsed Sampling Method for Extended Dynamic Range of Ion Mobility Spectrometry

Ion mobility spectrometry (IMS) has been widely studied and applied as an effective analytical technology for the on-site detection of volatile organic compounds (VOCs). Despite its superior selectivity compared with most gas sensors, its limited dynamic range is regarded as a major drawback, limiting its further application in quantitative measurements. In this work, we proposed a novel sample introduction method based on pulsed membrane adsorption, which effectively enhanced IMS's ability to measure analytes at higher concentrations. Taking N-methyl-2-pyrrolidone (NMP) as an example, this new sampling method expanded the dynamic range from 1 ppm to 200 ppm. The working principle and measurement strategy of this sampling method were also discussed, providing new insights for the design and application of IMS-based instruments.

Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning

This study investigates four machine-learning (ML) models to predict the redox potentials of phenazine derivatives in dimethoxyethane using density functional theory (DFT). A small data set of 151 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Prediction accuracy was improved by a combined strategy of feature selection and hyperparameter optimization, using the external validation set. Models were evaluated on the external test set containing new functional groups and diverse molecular structures. High prediction accuracies of R ² > 0.74 were obtained on the external test set. Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with good accuracies (R ² > 0.7). This type of performance for predicting redox potential from such a small and simple data set of phenazine derivatives has never been reported before. Redox flow batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives, thus saving computational and experimental costs. Using this approach, we have identified promising phenazine derivatives for green energy storage systems such as RFBs.