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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.

Despite the large number of reports on colloidal nanocrystals, very little is known about the mechanistic details in terms of nucleation and growth at the atomistic level. Taking bimetallic core-shell nanocrystals as an example, here we integrate in situ liquid-cell transmission electron microscopy with first-principles calculations to shed light on the atomistic details involved in the nucleation and growth of Pt on Pd cubic seeds. We elucidate the roles played by key synthesis parameters, including capping agent and precursor concentration, in controlling the nucleation site, diffusion path, and growth pattern of the Pt atoms. When the faces of a cubic seed are capped by Br-, Pt atoms preferentially nucleate from corners and then diffuse to edges and faces for the creation of a uniform shell. The diffusion does not occur until the Pt deposited at the corner has reached a threshold thickness. At a high concentration of the precursor, self-nucleation takes place and the Pt clusters then randomly attach to the surface of a seed for the formation of a non-uniform shell. These atomistic insights offer a general guideline for the rational synthesis of nanocrystals with diverse compositions, structures, shapes, and related properties.

The electroreduction of C1 feedgas to high-energy-density fuels provides an attractive avenue to the storage of renewable electricity. Much progress has been made to improve selectivity to C1 and C2 products, however, the selectivity to desirable high-energy-density C3 products remains relatively low. We reason that C3 electrosynthesis relies on a higher-order reaction pathway that requires the formation of multiple carbon-carbon (C-C) bonds, and thus pursue a strategy explicitly designed to couple C2 with C1 intermediates. We develop an approach wherein neighboring copper atoms having distinct electronic structures interact with two adsorbates to catalyze an asymmetric reaction. We achieve a record n-propanol Faradaic efficiency (FE) of (33 ± 1)% with a conversion rate of (4.5 ± 0.1) mA cm−2, and a record n-propanol cathodic energy conversion efficiency (EEcathodic half-cell) of 21%. The FE and EEcathodic half-cell represent a 1.3× improvement relative to previously-published CO-to-n-propanol electroreduction reports. Catalysts for CO electroreduction have focused on Cu, and their main products have been C2 chemicals. Here authors use the concept of asymmetric active sites to develop a class of doped Cu catalysts for C-C coupling, delivering record selectivity to n-propanol. [ABSTRACT FROM AUTHOR]

Owing to their attractive application potentials in both non-volatile memory and unconventional computing, memristive devices have drawn substantial research attention in the last decade. However, major roadblocks still remain in device performance, especially concerning relatively large parameter variability and limited cycling endurance. The response of the active region in the device within and between switching cycles plays the dominating role, yet the microscopic details remain elusive. This Review summarizes recent progress in scientific understanding of the physical origins of the non-idealities and propose a synergistic approach based on in situ characterization and device modeling to investigate switching mechanism. At last, the Review offers an outlook for commercialization viability of memristive technology. Memristor as the fourth basic element of electric circuits has drawn substantial attention for developing future computing technologies. Sun et al. report the progress and the challenges facing researchers on understanding memristive switching, and advocate continuous studies using a synergistic approach. [ABSTRACT FROM AUTHOR]

Production of syngas with tunable CO/H 2 ratio from renewable resources is an ideal way to provide a carbon-neutral feedstock for liquid fuel production. Ag is a benchmark electrocatalysts for CO 2 -to-CO conversion but high overpotential limits the efficiency. We synthesize AgP 2 nanocrystals (NCs) with a greater than 3-fold reduction in overpotential for electrochemical CO 2 -to-CO reduction compared to Ag and greatly enhanced stability. Density functional theory calculations reveal a significant energy barrier decrease in the formate intermediate formation step. In situ X-ray absorption spectroscopy (XAS) shows that a maximum Faradaic efficiency is achieved at an average silver valence state of +1.08 in AgP 2 NCs. A photocathode consisting of a n + p-Si wafer coated with ultrathin Al 2 O 3 and AgP 2 NCs achieves an onset potential of 0.2 V vs. RHE for CO production and a partial photocurrent density for CO at -0.11 V vs. RHE (j -0.11, CO ) of -3.2 mA cm -2 .