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Nanostructured materials have gained significant attention as anode material in rechargeable lithium-ion batteries due to their large surface-to-volume ratio and efficient lithium-ion intercalation. Herein, we systematically investigated the electronic and electrochemical performance of pristine and endohedral doped (O and Se) Ge 12 C 12 and Si 12 C 12 nanocages as a prospective negative electrode for lithium-ion batteries using high-level density functional theory at the DFT/B3LYP-GD3(BJ)/6-311 + G(d, p)/GEN/LanL2DZ level of theory. Key findings from frontier molecular orbital (FMO) and density of states (DOS) revealed that endohedral doping of the studied nanocages with O and Se tremendously enhances their electrical conductivity. Furthermore, the pristine Si 12 C 12 nanocage brilliantly exhibited the highest V cell (1.49 V) and theoretical capacity (668.42 mAh g − 1 ) among the investigated nanocages and, hence, the most suitable negative electrode material for lithium-ion batteries. Moreover, we utilized four machine learning regression algorithms, namely, Linear, Lasso, Ridge, and ElasticNet regression, to predict the V cell of the nanocages obtained from DFT simulation, achieving R 2 scores close to 1 (R 2  = 0.99) and lower RMSE values (RMSE < 0.05). Among the regression algorithms, Lasso regression demonstrated the best performance in predicting the V cell of the nanocages, owing to its L1 regularization technique.

Tetrahydroacridines arouse particular interest due to the potential possibilities of application in the medical field and protection against corrosion. Bis-tetrahydroacridines were newly synthesized by Pfitzinger condensation of 5,5′-(ethane-1,2-diyl) diindoline-2,3-dione with several cyclanones. NMR, MS, and FT-IR were used to prove their molecular structure. In addition, a computer-aided study was performed for the lowest energy conformers of each structure, in vacuum conditions, at ground state using DFT models to assess their electronic properties. UV–Vis and voltammetric methods (cyclic voltammetry, differential pulse voltammetry, and rotating disk electrode voltammetry) were used to investigate their optical and electrochemical properties. The results obtained for these π-conjugated heteroaromatic compounds lead to the conclusion that they have real potential in applications in different fields such as pharmaceuticals and especially as corrosion inhibitors.

Herein, theoretical study on the adsorption and detection of hydroxymethanesulfonate ( HMS-CH 3 O 4 S ) gas onto the surfaces of transition metal dopedfullerene: C 23 Cr, C 23 Fe, C 23 Ni, C 23 Ti, and C 23 Zn nanomaterial has been conducted using the first-principle density functional theory (DFT) with theωB97XD/GEN/6-311++G(d,p)/LanL2DZ methods. The adsorbing properties of the transition metal doped fullerene have been evaluated via adsorption energy, quantum descriptors, natural bond orbital (NBO) analysis and sensor mechanisms. It was found that the adsorption energy follows a decreasing pattern: T1 (−2.2816 eV ) > C1 (−2.1809 eV ) > F1 (−1.7796)  > N1 (−1.6868)  > Z1 (−1.4967 eV). Due to negative adsorption enthalpy observed, the adsorption phenomenon is best described as chemisorptions. Smaller energy gap values of 3.621 and 4.235 eV associated with HMS@C 23 Cr (C1) and HMS@C 23 Ti ( T1 ) indicates relatively better conductivity, reactivity and sensitivity. Weak interactions have been studied via the reduced density gradient (RDG) and quantum theory of atoms in molecule (QTAIM) analysis which provides that weak van der Waal’s interaction was more intense in HMS-C 23 Cr (C1) and due to the presence of spike at the blue region of the RDG isosurface, strong hydrogen bond interaction was observed in HMS-C 23 Ti. Additionally, all the complexes portrayed partial covalent nature of interactions as a result of H ( r ) < 0 and ∇ 2 ρ ( r ) > 0. This investigation confirmed that C 23 Cr and C 23 Ti surfaces possess better sensing properties as compared to their studied counterparts. Hence, the sensor material can be utilized in detecting HMS-CH 3 O 4 S .

Nanostructured materials have gained significant attention as anode material in rechargeable lithium-ion batteries due to their large surface-to-volume ratio and efficient lithium-ion intercalation. Herein, we systematically investigated the electronic and electrochemical performance of pristine and endohedral doped (O and Se) Ge C and Si C nanocages as a prospective negative electrode for lithium-ion batteries using high-level density functional theory at the DFT/B3LYP-GD3(BJ)/6-311 + G(d, p)/GEN/LanL2DZ level of theory. Key findings from frontier molecular orbital (FMO) and density of states (DOS) revealed that endohedral doping of the studied nanocages with O and Se tremendously enhances their electrical conductivity. Furthermore, the pristine Si C nanocage brilliantly exhibited the highest V (1.49 V) and theoretical capacity (668.42 mAh g ) among the investigated nanocages and, hence, the most suitable negative electrode material for lithium-ion batteries. Moreover, we utilized four machine learning regression algorithms, namely, Linear, Lasso, Ridge, and ElasticNet regression, to predict the V of the nanocages obtained from DFT simulation, achieving R scores close to 1 (R  = 0.99) and lower RMSE values (RMSE < 0.05). Among the regression algorithms, Lasso regression demonstrated the best performance in predicting the V of the nanocages, owing to its L1 regularization technique.