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Supported noble metal catalysts, ubiquitous in chemical technology, often undergo dynamic transformations between reduced and oxidized states—which influence the metal nuclearities, oxidation states, and catalytic properties. 

Abstract 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/cm²of 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. 

Abstract Selective conversion of methane (CH 4 ) into value-added chemicals represents a grand challenge for the efficient utilization of rising hydrocarbon sources. We report here dimeric copper centers supported on graphitic carbon nitride (denoted as Cu 2 @C 3 N 4 ) as advanced catalysts for CH 4 partial oxidation. The copper-dimer catalysts demonstrate high selectivity for partial oxidation of methane under both thermo- and photocatalytic reaction conditions, with hydrogen peroxide (H 2 O 2 ) and oxygen (O 2 ) being used as the oxidizer, respectively. In particular, the photocatalytic oxidation of CH 4 with O 2 achieves >10% conversion, and >98% selectivity toward methyl oxygenates and a mass-specific activity of 1399.3 mmol g Cu −1 h −1 . Mechanistic studies reveal that the high reactivity of Cu 2 @C 3 N 4 can be ascribed to symphonic mechanisms among the bridging oxygen, the two copper sites and the semiconducting C 3 N 4 substrate, which do not only facilitate the heterolytic scission of C-H bond, but also promotes H 2 O 2 and O 2 activation in thermo- and photocatalysis, respectively.