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

Quantitative Analysis of the Reduction Kinetics Responsible for the One-Pot Synthesis of Pd–Pt Bimetallic Nanocrystals with Different Structures

We report a quantitative understanding of the reduction kinetics responsible for the formation of Pd–Pt bimetallic nanocrystals with two distinctive structures. The syntheses involve the use of KBr to manipulate the reaction kinetics by influencing the redox potentials of metal precursor ions via ligand exchange. In the absence of KBr, the ratio between the initial reduction rates of PdCl₄^2– and PtCl₄^2– was about 10.0, leading to the formation of Pd@Pt octahedra with a core–shell structure. In the presence of 63 mM KBr, the products became Pd–Pt alloy nanocrystals. In this case, the ratio between the initial reduction rates of the two precursors dropped to 2.4 because of ligand exchange and, thus, the formation of PdBr₄^2– and PtBr₄^2–. The alloy nanocrystals took a cubic shape owing to the selective capping effect of Br^– ions toward the {100} facets. Relative to the alloy nanocubes, the Pd@Pt core–shell octahedra showed substantial enhancement in both catalytic activity and durability toward the oxygen reduction reaction (ORR). Specifically, the specific (1.51 mA cm^–2) and mass (1.05 A mg^–1 _Pt) activities of the core–shell octahedra were enhanced by about four- and three-fold relative to the alloy nanocubes (0.39 mA cm^–2 and 0.34 A mg^–1 _Pt, respectively). Even after 20000 cycles of accelerated durability test, the core–shell octahedra still exhibited a mass activity of 0.68 A mg^–1 _Pt, twice that of a pristine commercial Pt/C catalyst