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There is a compelling reason to design cost-effective sensors to detect and measure harmful molecules such as dichlorosilane ($\mathrm{H}_{2}\mathrm{SiCl}_{2}$) in the air. In this work, density functional theory (DFT) has been used to study the nature of the intermolecular interactions between the $\mathrm{H}_{2}\mathrm{SiCl}_{2}$ gas molecule with a single-walled pristine, Al-doped, and Ga-doped boron nitride nanotubes (BNNT, BNAlNT, and BNGaNT, respectively) to investigate their potential in gas-sensing applications. Full-dimensional geometry optimization and adsorption energies were calculated with four functionals: PBE0, M06-2X, ${\omega }$B97XD, and B3LYP-D3 with a 6-311G(d) basis set. We find that the B, Al, or Ga atoms provide the most favorable sites for adsorption of the $\mathrm{H}_{2}\mathrm{SiCl}_{2}$ molecule. The adsorbate is more tightly bound to the surface of the doped rather than of the pristine BNNT nanotubes, demonstrating a larger energy gain due to adsorption. This is due to the fact that $\mathrm{H}_{2}\mathrm{SiCl}_{2}$ interacts with pristine BNNT through weak Van der Waals forces but seemingly has stronger ionic interactions with the doped variants. In general, introducing impurities can improve the selectivity and reactivity of the BNNT toward $\mathrm{H}_{2}\mathrm{SiCl}_{2}$. Among all of the absorbents, we find that BNGaNT exhibits the highest affinity toward $\mathrm{H}_{2}\mathrm{SiCl}_{2}$, and therefore holds a higher potential compared to the rest of the nanotubes investigated here for designing materials for dichlorosilane sensors.

Direct Metal Deposition (DMD) is one of the underwater marine additive manufacturing (MAM) technologies known for its capability to build up on semi-finished products. This allows for the creation of complex structures and repair the damaged or worn-out areas. Employing this underwater technology needs a lot of consideration regarding the harsh environment of the ocean. This research endeavours to identify nickel-aluminium bronze’s structural characteristics printed underwater. Simulation studies can help to analyse grain and phase evolution, defects, and melt pool behaviour, enabling the optimization of printing parameters for high-quality marine alloy components. To achieve that a control systems and machine learning algorithms need to developed to enhance precision in the 3D printing process on a moving platform, addressing the challenges of six distinct vessel movements at sea. This integration aims to improve accuracy, contributing to optimal performance in dynamic maritime environments.