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Currently, dual atomic catalysts (DACs) with neighboring active sites for oxygen reduction reaction (ORR) still meet lots of challenges in the synthesis, especially the construction of atomic pairs of elements from different blocks of the periodic table. Herein, a "rare earth (Ce)-metalloid (Se)" non-bonding heteronuclear diatomic electrocatalyst has been constructed for ORR by rational coordination and carbon support defect engineering. Encouraging, the optimized Ce−Se diatomic catalysts (Ce−Se DAs/NC) displayed a half-wave potential of 0.886 V vs. reversible hydrogen electrode (RHE) and excellent stability, which surpass those of separate Ce or Se single atoms and most single/dual atomic catalysts ever reported. In addition, a primary zinc-air battery constructed using Ce−Se DAs/NC delivers a higher peak power density (209.2 mW·cm−2) and specific capacity (786.4 mAh·gZn−1) than state-of-the-art noble metal catalysts Pt/C. Theoretical calculations reveal that the Ce−Se DAs/NC has improved the electroactivity of the Ce−N4 region due to the electron transfer towards the nearby Se specific activity (SA) sites. Meanwhile, the more electron-rich Se sites promote the adsorptions of key intermediates, which results in the optimal performances of ORR on Ce−Se DAs/NC. This work provides new perspectives on electronic structure modulations via non-bonded long-range coordination micro-environment engineering in DACs for efficient electrocatalysis. [ABSTRACT FROM AUTHOR]

In the multi-type & small batch production mode, jobs of small quantity (i.e., hybrid jobs) are implemented in the job shop. The production scheduling is a significant activity and it is necessary to predict the disturbance in advance. Some methods and tools to tackle the production scheduling have been provided. From the perspective of duration, our work extends the method of dynamic scheduling with incorporation of machine health prediction. The primary result of this work is the efficient generation of feasible scheduling solution when the machine health is warned. The machining quality data-based method is proposed to predict the machine health status and the relation between machine and quality characteristic is established. Combination of K-means, data equalization algorithm and particle swarm optimization (PSO) is designed to predict the machine health. Mathematical model in terms of duration is proposed and improved genetic algorithm (GA) is applied to generate the feasible scheduling solution. A prototype system is developed and a case study of a metalworking workshop is implemented. The results show that the work can reduce the risk of machine health to production. Using the prototype system, the engineers can filter out infeasible scheduling solutions automatically and acquire a successful one efficiently. [ABSTRACT FROM AUTHOR]

New stricter environmental regulations and consumer rising issues are making greenhouse gases (GHG) emission an increasing and urgent concern for manufacturing companies. Companies and researchers are seeking appropriate methods to reduce GHG emission of the manufactured products. Previous studies on low-carbon product design mainly concern on a single product. Currently, it is common to design a product family instead of a single product for increasing varieties to satisfy customers’ requirements. Owing to the difference in design methods, the low-carbon design method for a single product cannot handle a product family. In addition, nowadays, the sourcing strategy is widely adopted by companies. A key problem of the procurement is supplier selection. The supplier selection affects not only profit but also GHG emission. However, it has not been simultaneously considered in low-carbon product design. In this article, an optimization model for coordinating low-carbon design of product family and supplier selection is proposed. In the model, the profit and the GHG emission of a product family are taken into consideration at the same time. Moreover, a genetic algorithm is developed to solve the established model. Finally, a case study is performed to verify the validity of the proposed approach. [ABSTRACT FROM AUTHOR]