The possibilities of the application of Data Science Methods in predicting certain macroscopic properties have been examined in energetic compounds. Artificial neural networks, one of the most promising methods of Data Science, has been used for predicting detonation velocity based on a trained set comprising of a large data set containing 104 data points extracted from over 65 explosive compounds and compositions with diverse characteristics and properties. The utility of the method has been demonstrated through validation for over 37 explosive compounds again with diverse characteristics constituting to a data set of 74 data points. The usefulness and versatility of the method is clear as it exhibits similar predictive accuracy on comparison with the similar data derived from two other well‐known empirical models. Such predictive capabilities will be a great tool for engineers and scientists working with high energetic explosives for quick and simple prediction of detonation velocity given the chemical composition and vice versa.

The application of carbyne-enriched nanomaterials opens unique possibilities for enhancing the functional properties of several nanomaterials and unlocking their full potential for practical applications in high-end devices. We studied the ethanol-vapor-sensing performance of a carbyne-enriched nanocoating deposited onto surface acoustic wave (SAW) composite substrates with various electrode topologies. The carbyne-enriched nanocoating was grown using the ion-assisted pulse-plasma deposition technique. Such carbon nanostructured metamaterials were named 2D-ordered linear-chain carbon, where they represented a two-dimensionally packed hexagonal array of carbon chains held by the van der Waals forces, with the interchain spacing approximately being between 4.8 and 5.03 Å. The main characteristics of the sensing device, such as dynamic range, linearity, sensitivity, and response and recovery times, were measured as a function of the ethanol concentration. To the authors’ knowledge, this was the first time demonstration of the detection ability of carbyne-enriched material to ethanol vapors. The results may pave the path for optimization of these sensor architectures for the precise detection of volatile organic compounds, with applications in the fields of medicine, healthcare, and air composition monitoring.

Due to the unique combination of physicochemical and structural properties of carbyne-enriched nanocoatings, they can be used for the development of high-end electronic devices. We propose using it for the development of sensor platforms based on silicon bulk micromachined membranes that serve as a part of microcapacitors with flexible electrodes, with various sizes and topologies. The carbyne-enriched nanocoating was grown using the ion-assisted pulse-plasma deposition method in the form of 2D-ordered linear-chain carbon with interchain spacing in the range of approximately 4.8–5.03 Å. The main characteristics of the fabricated sensors, such as dynamic range, sensitivity, linearity, response, and recovery times, were measured as a function of the ethanol concentration and compared for the different sizes of the micromembranes and for the different surface states, such as patterned and non-patterned. The obtained results are the first step in the further optimization of these sensor platforms to reach more precise detection of volatile organic compounds for the needs of the healthcare, air monitoring, and other relevant fields of human health.

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