Machine learning approach to map the thermal conductivity of over 2,000 neoteric solvents for green energy storage applications

Molecular Guide for Selecting Green Deep Eutectic Solvents with High Monosaccharide Solubility for Food Applications

“…Conversely, it can be observed that the choline cation possesses a more localized charge (to the left). This occurs because the impact of the N + is more evident as fewer carbons are available for charge stabilization and due to the H + part of the hydroxy functional group . Moving on to Figure B, it can be observed that tetraethylene glycol, glucose, and fructose showcase relatively large peaks in both the HBA and the HBD regions, suggesting that these molecules can function as both an HBA and an HBD.…”

“…This occurs because the impact of the N + is more evident as fewer carbons are available for charge stabilization and due to the H + part of the hydroxy functional group. 36 Moving on to Figure 3B, it can be observed that tetraethylene glycol, glucose, and fructose showcase relatively large peaks in both the HBA and the HBD regions, suggesting that these molecules can function as both an HBA and an HBD. In addition, it can be observed that the sizes of the glucose and fructose profiles are very similar due to their similarity in nature and molecular weight.…”

Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical conductivity and gas sensing performance

“…σ-profiles offer a unique capability to capture polarizability and asymmetric electron densities arising from covalent bonds between atoms with differing electronegativities. These attributes make σ-profiles particularly valuable for characterizing non-covalent interactions between molecules and properties that depend significantly on intermolecular interactions, 84–86 such as electrical conductivity. By utilizing σ-profiles, we can gain valuable insights into the molecular interactions that govern the behavior of nanocomposite materials, leading to advancements in material design and applications in various fields.…”

Layered nanomaterials for renewable energy generation and storage

“…The main advantage of this approach is the minimization of harmful effects during the production of certain materials. 181,182 Therefore, research on machine learning and materials optimization is rapidly developing, especially in the last 10 years, and is already for design of 2D materials, 183–185 hydrogels, 186–189 supramolecular structures, 190 processes of photocatalysis, 191–194 energy storage, 195–197 layer-by-layer structures 198,199 and others (Fig. 7b).…”