Neutron scattering study of the proton dynamics in aqueous solutions of sulphuric acid and caesium sulphate

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors

Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m²/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm² of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H₂SO₄. This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

First-principles molecular dynamics study on aqueous sulfuric acid solutions

The properties of aqueous sulfuric acid have been studied employing density functional theory-based molecular dynamics simulations in conjunction with norm-conserving pseudopotentials. The simulations were carried out for two different concentrations whose molar concentrations were fixed at 0.84 and 10.2 mol/l. The structural features of aqueous sulfuric acid solutions show a strong dependency on the concentration. The Grötthuss-type proton transfer mechanism is not effectively operative at the higher concentration because of the broken hydrogen bond network of water induced by ions generated by the dissociation of sulfuric acid. In addition, to evaluate electrical properties, we carried out a simulation that takes an electric field into account. Results are compared with those of the simulation undertaken with no external electric field.

Proton dynamics in the perchloric acid clathrate hydrate HClO4⋅5.5H2O

In the perchloric acid clathrate hydrate HClO4.5.5H2O, the perchlorate anions are contained inside an aqueous host crystalline matrix, positively charged because of the presence of delocalized acidic protons. Our experimental results demonstrate that the microscopic mechanisms of proton conductivity in this system are effective on a time scale ranging from nanosecond to picosecond. In the present paper, we discuss more specifically on the relaxation processes occurring on a nanosecond time scale by combining high-resolution quasielastic neutron scattering and 1H pulse-field-gradient nuclear magnetic resonance experiments. The combination of these two techniques allows us to probe proton dynamics in both space and time domains. The existence of two types of proton dynamical processes has been identified. The slowest one is associated to long-range translational diffusion of protons between crystallographic oxygen sites and has been precisely characterized with a self-diffusion coefficient of 3.5 x 10(-8) cm2/s at 220 K and an activation energy of 29.2+/-1.4 kJ/mol. The fastest dynamical process is due to water molecules' reorientations occurring every 0.7 ns at 220 K with an activation energy of 17.4+/-1.5 kJ/mol. This powerful multitechnique approach provides important information required to understand the microscopic origin of proton transport in an ionic clathrate hydrate.