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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/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.

 

A review highlights improvements in synthesizing and stabilizing multielement nanoparticles. 

Abstract 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. 

In this investigation, the improved electrochemical behavior in Si-doped Li-rich cathodes is studied with scanning transmission electron microscopy (STEM) and electron energy loss spectroscopy (EELS). Z-contrast images show a layered structure that develops a thin, spinel-like surface layer after the first charge cycle. Si-doping increases discharge capacity by ∼25% and appears to retard the surface phase transformation. Based on electron energy loss spectra, the surface layer in the doped material has an altered oxygen electronic environment, which supports the STEM findings. Furthermore, Si-doping changes the redox behavior during the activation cycle. Density functional theory calculations indicate that Si-doping can increase oxygen vacancy formation, and change the sequence of the redox couples by introducing more oxygen vacancies before or during the typical high voltage activation process. The results of this work indicate that the type of doping employed here is an effective strategy for controlling the complex charge compensation mechanisms in lithium-rich cathodes.