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. In a 1 M KOH electrolyte, the Fe/CoP NHs with the optimized chemical composition can achieve an efficient ammonia (NH3) generation rate of 27.6 mg h-1 mgcat-1 for the conversion of NO3- into NH3 and a Faradaic efficiency of 93.3% at a -0.25 V potential, which exceed the values of various previously reported nanomaterials in an alkaline electrolyte.

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

Methanol electrolysis is a promising strategy to achieve energy-saving and efficient electrochemical hydrogen (H2) 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. For MOR, Pd@RhPd NDs exhibit Rh content-determined catalytic activity, in which Pd@Rh0.07Pd NDs have an optimal combination of oxidation potential and oxidation current due to the synergistic catalytic process of Pd/Rh double active sites. For HER, the introduction of Rh greatly improves the catalytic activity of Pd@RhPd NDs compared to that of Pd NDs, suggesting that Rh is the main activity site for HER. Unlike MOR, however, the HER activity of Pd@RhPd NDs is not sensitive to the Rh content. Using Pd@Rh0.07Pd NDs as robust bifunctional electrocatalysts, the as-constructed two-electrode methanol electrolysis cell shows a much lower voltage (0.813 V) than that of water electrolysis (1.672 V) to achieve electrochemical H2 production at 10 mA cm-2, demonstrating the application prospect of methanol electrolysis for H2 production.

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 PtII-dimethylglyoxime complex (PtII-DMG) nanorods as the reaction precursor. The coordination capability of DMG results in the generation of PtII-DMG nanorods, whereas the reducibility of DMG at high temperature leads to the reduction of PtII species in PtII-DMG nanorods. During the reaction process, the inside-out Ostwald ripening phenomenon leads to the hollow morphology of Pt-hNTs. Benefiting from the physical characteristics of hollow and holey structure, Pt-hNTs with clean surface show superior electroactivity and durability for catalyzing ethanol electrooxidation as well as hydrogen evolution reaction in alkaline media. Under optimized experimental conditions, the constructed symmetric Pt-hNTs||Pt-hNTs ethanol electrolyzer only requires an electrolysis voltage of 0.40 V to achieve the electrochemical hydrogen production, demonstrating a highly energy saving strategy relative to traditional water electrolysis.

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. SS-CKFDA is a combination of traditional complete kernel Fisher discriminant (CKFDA) and semi-supervised learning. It adjusts the Fisher criterion with the data global structure extracted from large unlabeled samples. When the number of labeled samples is small, the global structure that exists in the measured data can effectively improve the extraction effects of the projected vector. The experimental results on Tennessee Eastman Process (TEP) data demonstrated that the proposed method can improve diagnostic performance, when compared to other Fisher discriminant algorithms. Finally, the experimental results on the optical coordinate data proves that the method can be applied in the automotive assembly process, and achieve a better performance.