Electrochemical power devices with a long lifespan, long-term energy retention and great cycle stability are extremely important for periodic energy store/supply, especially for solar energy storage for space equipment and for power electronics in integrated circuits. In this report, we have systematically investigated the effects of the charging current density and temperature over the self-discharge (SDC) process of activated carbon fabric-based (ACF) supercapacitors with 1 M LiPF 6 /EC-DEC (v/v ¼ 1) and 1 M TEABF 4 /PC as electrolytes, respectively. The experimental results have shown that a different control mechanism governs the SDC process in each electrolyte system. Significant energy retention (in excess of 70%) was obtained in the ACF-TEABF 4 system after 36 h. SDC at room temperature. A dualmechanism control model is proposed for the first time which describes perfectly the SDC process of the supercapacitor using 1 M TEABF 4 /PC as the electrolyte over different charge current densities and at different SDC temperatures.

No abstract

Due to the scarcity and high cost of precious metals, the hydrogen economy would ultimately rely on non-platinum-group-metal (non-PGM) catalysts. The non-PGM-catalyzed oxygen reduction reaction, which is the bottleneck for the application of hydrogen fuel cells, is challenging because of the limited activity and durability of non-PGM catalysts. A stabilized single-atom catalyst may be a possible solution to this issue. In this work, we employ a coordination-assisted polymerization assembly strategy to synthesize an atomic Fe and N co-doped ordered mesoporous carbon nanosphere (denoted as meso-Fe–N–C). The meso-Fe–N–C possesses a hierarchical structure with a high surface area of 494.7 m2 g–1 as well as a high dispersion of Fe (2.9 wt %) and abundant N (4.4 wt %). With these beneficial structural properties, the meso-Fe–N–C exhibits excellent activity and durability toward the oxygen reduction reaction, outperforming the state-of-the-art Pt/C electrocatalysts.

A fluorescent emitter simultaneously transmits its identity, location, and cellular context through its emission pattern. We developed smNet, a deep neural network for multiplexed single-molecule analysis to enable retrieving such information with high accuracy. We demonstrate that smNet can extract three-dimensional molecule location, orientation, and wavefront distortion with precision approaching the theoretical limit and therefore will allow multiplexed measurements through the emission pattern of a single molecule.