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Mesoporous carbon (MC) spheres with hierarchical pores, controlled pore volume and high specific surface areas have been prepared by a mass-producible spray drying assisted template method using sodium alginate as carbon precursor and commercial colloidal silica particles as hard template. The resulting MC spheres, possessing hierarchical pores in the range of 3–30 nm, are employed as conductive matrices for the preparation of cathode materials for lithium–sulfur batteries. A high pressure induced one-step impregnation of elemental sulfur into the pore of the MC spheres has been exploited. The electrochemical performances of sulfur-impregnated MC spheres (S-MC) derived from MC spheres with different pore volume and specific surface area but with the same sulfur loading ratio of 60 wt% (S-MC-X-60) have been investigated in details. The S-MC-4-60 composite cathode material displayed a high initial discharge capacity of 1388 mAhg −1 and a good cycling stability of 857 mAhg −1 after 100 cycles at 0.2C, and shows also excellent rate capability of 864 mAhg −1 at 2C. More importantly, the sulfur loading content in MC-4 spheres can reach as high as 80%, and it still can deliver a capacity of 569 mAhg −1 after 100 cycles at 0.2C. [ABSTRACT FROM AUTHOR]

Proactive human-robot collaborative assembly, a cognitively driven human-robot collaboration, requires research into robot cognitive strategy reasoning to ensure that the robot actively collaborates with the operator in task completion. However, current methods primarily focus on the pairwise relationships of assembly components in discrete snapshots. They could fail to represent the interconnected status of dynamic assembly, leading to inaccurate task allocation, thereby affecting robotic cognitive strategy. To address this problem, we propose a stacked graph neural network (GNN) with self-exciting process to capture the correlation and triggering mechanisms between time-varying tasks. Firstly, a temporal hypergraph with assembly knowledge is constructed to represent the non-pairwise relationships among assembly components in time-varying tasks, aiming to reduce the redundant information brought by pairwise relationships. Then, considering the characteristic of mutual influence between assembly events, a Hawkes process is introduced into the stacked GNN architecture to learn the event correlation representation in the temporal hypergraph. This point process models the self-exciting process of assembly events for simultaneously capturing the individual and collective features of events, thereby revealing the triggering mechanisms of the dynamic events. Finally, the effectiveness of proposed method is demonstrated by comparative experiments and the results of robotic cognitive strategy reasoning on dynamic assembly. [ABSTRACT FROM AUTHOR]

In this paper, a novel method to separate and determine oleanolic acid and ursolic acid isomers in Ligustrum lucidum Ait was studied by micellar electrokinetic capillary chromatography (MECC). The baseline separation of the two analytes were obtained on the condition that the buffer contained 15 mmol/l disodium hydrogen phosphate, 15 mmol/l disodium tetreborate, 10 mmol/l SDS and 5% (v/v) alcohol. The contents of the oleanolic acid and ursolic acid were determined in L. lucidum Ait, Folium photiniae and Flos campsis; they were 78.3 (R.S.D.=2.75%) and 20.7 mg/g (R.S.D.=2.97%), 27.9 (R.S.D.=3.67%) and 79.8 mg/g (R.S.D.=3.44%), 65.5 (R.S.D.=3.73%) and 60.4 mg/g (R.S.D.=4.06%) (n=5), respectively. The recoveries of the analytes in the extract of L. lucidum Ait were 102% (R.S.D.=2.85%) for oleanolic acid and 104% (R.S.D.=3.21%) for ursolic acid (n=5). With the emphasis on the effects of SDS and alcohol concentrations on the separation of the isomers were investigated. [Copyright &y& Elsevier]