Borophene, a monolayer of boron atoms, belongs to intensively studied two-dimensional beyond-graphene materials. The B36 borophene nanoflake is a finite size model system, containing a hexagonal vacancy similar to the ones present in (312 and chi 3 borophene sheets. The hydrogen binding performance of B36 decorated with various transition metal atoms is investigated using density functional theory and quantum theory of atoms in molecules. Hydrogen is considered to become one of the crucial energy sources in future, hence, a search for effective hydrogen storage materials is of urge importance. Obtained results suggest that B36 decorated with Co, Ni, Fe, and Cu possess strong affinity to bind the H2 molecule via formation of eta 2-dihydrogen bonds. Among them, the strongest H2 binding is found for Co- and Ni-decorated B36. Furthermore, B36 decorated with Sc and Ti behave like H-H bond breakers while B36 decorated with Zn possess only negligible affinity to bind H2 molecule. The stability of the B36 decorated with Co and Ni is verified by ab initio molecular dynamics. The presented data may also serve as a basis for reference in future large-scale computational studies of borophene-based materials.

This study investigates the structural and dynamic properties of ternary mixtures composed of NaPF6, ethylene carbonate (EC), and the ionic liquid choline glycine (ChGly), with a focus on their potential as electrolytes for supercapacitors. The combination of NaPF6-EC, known for its high ionic conductivity, with the biodegradable and environmentally friendly ChGly offers a promising approach to enhancing electrolyte performance. Through molecular simulations, we analyze how the inclusion of small concentrations of ChGly affects key properties such as density, cohesive energy, and ion mobility. Our findings demonstrate that the NaPF6-EC-ChGly mixture exhibits a complex network of electrostatic interactions and hydrogen bonding, with the glycine anion significantly influencing the liquid structure. In mixtures with small additions of ChGly, we observed an optimal balance of diffusion and ionic mobility. These results highlight the potential of ChGly as a green additive to conventional electrolytes, paving the way for more sustainable and high-performance energy storage devices.

Electrolytes play a crucial role in enhancing the performance of energy storage devices, including batteries and supercapacitors. However, traditional electrolytes, such as aqueous solutions, organic solvents, and ionic liquids, exhibit inherent limitations and challenges. Deep eutectic solvents have recently emerged as promising alternatives due to their environmentally friendly nature and favorable properties. Despite their widespread applications in various domains, their potential as electrolytes remains relatively underexplored. This study investigates three distinct types of deep eutectic solvents derived from different isomers of butanediols combined with choline chloride. Ab initio molecular dynamics simulations are employed to analyze the microstructure of these deep eutectic solvents, focusing on non-covalent electrostatic interactions, hydrogen bonding patterns, and vibrational spectra. The results reveal significant differences in the structural configuration of hydrogen bond acceptors and hydrogen bond donors and their interactions within the deep eutectic solvents. Specifically, the positioning of functional groups in hydrogen bond donors significantly impacts the hydrogen bonding network and the interaction with monoatomic ions. Moreover, the vibrational spectra analysis highlights the existence of hydrogen bonds involving stretching modes of the OH group, as evidenced by redshift deviations. Overall, this study provides valuable insights into the unique features of deep eutectic solvents as potential electrolytes for energy storage applications. The comprehensive analysis of their microstructure and vibrational properties enhances our understanding of deep eutectic solvent utilization and opens avenues for further research in sustainable energy storage.

The accurate prediction of standard vaporization enthalpy (ΔvapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔvapHm° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications. In this work, we present a data driven, explainable supervised ML model to predict ΔvapHm° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' ΔvapHm° with a mean absolute error of 3.02 kJ mol−1 and a 95% test score. The model was successfully validated through the prediction of ΔvapHm° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs. © 2024 The Authors