CoNi-N/P-Doped Carbon Nanotubes as Catalysts for Efficient Oxygen Reduction Reaction.

Transition metals (TMs) supported by heteroatom-doped carbon materials are considered to be the potential alternatives to the Pt/C catalyst owing to their low cost, outstanding electrocatalytic efficiency, and excellent electrochemical durability. In this paper, N/P-doped carbon nanotube (CNT) (N/P-CNT)-supported monometallic (Co, Ni) and bimetallic (CoNi) catalysts were synthesized by one-step pyrolysis using diammonium hydrogen phosphate, 2-methylimidazole and organometallic salts as precursors, and the CNT as the catalyst carrier; the effects of transition TM types and pyrolysis temperature (Tp) on the microstructure and electrochemical properties were explored. The analysis exhibited that the CoNi bimetallic catalyst was superior to both Co and Ni monometallic catalysts, and the catalysts pyrolyzed at 900 °C exhibited a better graphitization degree. The optimal CoNi-N/P-CNT-900 displayed remarkable oxygen reduction reaction electrocatalytic performance with a half-wave potential (E1/2) of 0.86 V and excellent methanol tolerance and stability. Moreover, the Zn-air battery coated with CoNi-N/P-CNT-900 demonstrated a larger open circuit voltage of 1.577 V, a larger peak power density of 212.89 mW cm-2 at 357.8 mA cm-2, as well as a higher specific capacity of 799 mA h gZn-1, superior to that of the Pt/C catalyst (1.492 V, 96.04 mW cm-2 at 216.8 mA cm-2, 735 mA h gZn-1), showing outstanding practical value. This study is expected to promote the commercialization of the electrocatalysts.

PtRuFe/Carbon Nanotube Composites as Bifunctional Catalysts for Efficient Methanol Oxidation and Oxygen Reduction.

The design of bifunctional catalysts with high performance and low platinum for the oxygen reduction reaction (ORR) and the methanol oxidation reaction (MOR) is of significant implication to promote the industrialization of fuel cells. In our work, Pt/carbon nanotube (CNT), Pt3Ru/CNT, and PtRu/CNT catalysts were synthesized by plasma heat treatment, in which the pyrolysis reduction of organometallic salts and the dispersion of CNTs were achieved simultaneously, and catalytic nanoparticles with uniform particle size were anchored on the dispersed CNT surface. Later, Fe was further introduced, and PtFe/CNT, Pt3RuFe/CNT, and PtRuFe/CNT catalysts were synthesized by calcination, and the structure and electrochemical properties in both MOR and ORR of all as-synthesized catalysts were investigated. The results indicated that plasma thermal treatment has the advantage of rapidness and immediacy in the synthesis of catalysts, and the Pt/CNT, Pt3Ru/CNT, and PtRu/CNT catalysts exhibited better electrocatalytic properties than commercial platinum (JM-Pt/C) catalysts. Meanwhile, the introduction of Fe during the calcination further changed the surface electronic properties of catalytic nanoparticles and enhanced the graphitization degree of catalysts; the PtRuFe/CNT catalyst exhibited outstanding electrocatalytic properties with a mass activity of 834.3 mA mg-1 for MOR and a half-wave potential of 0.928 V in alkaline media for ORR. The combination of plasma thermal treatment and calcination puts forward a novel strategy for the optimization of catalysts, and the synthesis method based on plasma dispersion needs to be further optimized to achieve its large-scale promotion.

Efficient Synthesis of Fe/N-Doped Carbon Nanotube as Highly Active Catalysts for Oxygen Reduction Reaction in Alkaline Media.

It is of significant implication to fabricate high-performance, durable and low-cost catalysts toward to oxygen reduction reaction (ORR) to drive commercial application of fuel cells. In our work, we synthesize the Fe/N-CNT catalyst via one-pot grinding combined with calcination using a mixture of carbamide, CNTs and iron salts as precursors, the as-synthesized catalysts show the structure that Fe nanoparticles are encapsulated in the tube of intertwined CNTs with abundant active sites. The catalyst is synthesized at 800 °C (Fe/N-CNT-800-20) obtain high graphitization degree and high N doped content, especially the high content and proportion of Fe-N and pyridinic-N, exhibiting outstanding ORR activity. Moreover, too high calcination temperature (850 °C) and high Fe content (25%) lead to the agglomeration of Fe during the calcination, which blocked some catalytic sites, leading to poor ORR activity. This facile synergy route will provide new thoughts for the fabrication and optimization of catalysts.

Multi-scale information with attention integration for classification of liver fibrosis in B-mode US image.

BACKGROUND AND OBJECTIVE: Chronic hepatitis B (CHB) is one of the most common liver diseases in the world, which threats a lot to people's usual life. The increased deposition of fibrotic tissues in livers for patients with CHB may lead to the development of liver cirrhosis, hepatocellular carcinoma, or even liver failure. Accurate fibrosis staging is very important for the targeted treatment of liver fibrosis and its recovery. METHODS: In this paper, we propose a new deep convolutional neural network (DCNN) with functions of multi-scale information extraction and attention integration for more accurate liver fibrosis classification from ultrasound (US) images. The proposed network uses two pyramid-structured CNN elements to extract multi-scale features from US images. Such a design significantly enlarges the receptive field of the convolution layer, such that more useful information can be explored by the neural network to associate with the final classification. Based on this, a new feature distillation method is also proposed to enhance the ability of deep features derived from multi-scale information. The proposed distillation method employs attention maps to automatically extract class-related features from multi-scale information, which effectively suppress the influence of potential distractors. RESULTS: Experimental results on the US liver fibrosis dataset collected from 286 participants show that the proposed deep framework achieves promising classification performance. The proposed method achieves a classification accuracy of 95.66% on the test dataset. CONCLUSION: Our proposed framework could stage liver fibrosis highly accurately. It might provide effective suggestions for the clinical treatment of liver fibrosis that can facilitate its recovery.