Iron-Doped Cobalt Phosphide Nanohoops for Electroreduction of Nitrate to Ammonia.

Heteroatom doping can effectively tune the electronic structure of an electrocatalyst to accelerate the adsorption/desorption of reaction intermediates, which sharply increases their intrinsic electroactivity. Herein, we successfully prepare iron (Fe)-doped cobalt phosphide (CoP) nanohoops (Fe/CoP NHs) with different Fe/Co atomic ratios as highly active electrocatalysts for the nitrate electrocatalytic reduction reaction (NIT-ERR). Electrochemical measurements reveal that appropriate Fe doping can improve the electroactivity of cobalt phosphide nanohoops for the NIT-ERR.

Holey platinum nanotubes for ethanol electrochemical reforming in aqueous solution.

The catalytic/electrocatalytic performance of platinum (Pt) nanostructures highly relates to their morphology. Herein, we propose a facile self-template pyrolysis strategy at high temperature to synthesize one-dimensionally holey Pt nanotubes (Pt-hNTs) using Pt-dimethylglyoxime complex (Pt-DMG) nanorods as the reaction precursor. The coordination capability of DMG results in the generation of Pt-DMG nanorods, whereas the reducibility of DMG at high temperature leads to the reduction of Pt species in Pt-DMG nanorods.

Bifunctional Pd@RhPd Core-Shell Nanodendrites for Methanol Electrolysis.

Methanol electrolysis is a promising strategy to achieve energy-saving and efficient electrochemical hydrogen (H) production. In this system, the advanced electrocatalysts with high catalytic performance for both the methanol oxidation reaction (MOR) and hydrogen evolution reaction (HER) are highly desirable. Inspired by the complementary catalytic properties of rhodium (Rh) and palladium (Pd) for MOR and HER, herein, several Pd core-RhPd alloy shell nanodendrites (Pd@RhPd NDs) are synthesized through the galvanic replacement reaction between Pd nanodendrites (Pd NDs) and rhodium trichloride.

A Novel Semi-Supervised Feature Extraction Method and Its Application in Automotive Assembly Fault Diagnosis Based on Vision Sensor Data.

The fault diagnosis of dimensional variation plays an essential role in the production of an automotive body. However, it is difficult to identify faults based on small labeled sample data using traditional supervised learning methods. The present study proposed a novel feature extraction method named, semi-supervised complete kernel Fisher discriminant (SS-CKFDA), and a new fault diagnosis flow for automotive assembly was introduced based on this method.