Nitrogen (N)-doped carbon materials exhibit high electrocatalytic activity for the oxygen reduction reaction (ORR), which is essential for several renewable energy systems. However, the ORR active site (or sites) is unclear, which retards further developments of high-performance catalysts. Here, we characterized the ORR active site by using newly designed graphite (highly oriented pyrolitic graphite) model catalysts with well-defined π conjugation and well-controlled doping of N species. The ORR active site is created by pyridinic N. Carbon dioxide adsorption experiments indicated that pyridinic N also creates Lewis basic sites. The specific activities per pyridinic N in the HOPG model catalysts are comparable with those of N-doped graphene powder catalysts. Thus, the ORR active sites in N-doped carbon materials are carbon atoms with Lewis basicity next to pyridinic N.

We identified B. crassa-like infection in people in northeastern China that caused mild to moderate symptoms. The possibility of more severe disease in immunocompromised patients and of transmission through the blood supply due to asymptomatic infections justifies further investigation of this reported infection.

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The charge carriers in graphene are massless Dirac fermions and exhibit a relativistic Landau-level quantization in a magnetic field. Recently, it has been reported that, without any external magnetic field, quantized energy levels have been also observed from strained graphene nanobubbles on a platinum surface, which were attributed to the Landau levels of massless Dirac fermions in graphene formed by a strain-induced pseudomagnetic field. Here we show the generation of the Landau levels of massless Dirac fermions on a partially potassium-intercalated graphite surface without applying external magnetic field. Landau levels of massless Dirac fermions indicate the graphene character in partially potassium-intercalated graphite. The generation of the Landau levels is ascribed to a vector potential induced by the perturbation of nearest-neighbour hopping, which may originate from a strain or a gradient of on-site potentials at the perimeters of potassium-free domains.

BackgroundInferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions.ResultsWe introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm. Then, a statistical technique is used to refine the predictions in our method. The proposed method was evaluated on the DREAM4 and DREAM5 benchmark datasets and achieved higher accuracy than the winners of those competitions and other state-of-the-art GRN inference methods.ConclusionsSuperior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1398-6) contains supplementary material, which is available to authorized users.