Analyzing the time frame for the transition from leisure-cyclist to commuter-cyclist

Commuter-cyclist, Leisure-cyclist, Hazard, Duration, Covariate effect,

Classification of crystal structure using a convolutional neural network

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds

Chemical and structural engineering of transition metal boride towards excellent and sustainable hydrogen evolution reaction

Herein, holey, thin, conductive nickel substituted cobalt molybdenum boride (Ni-CMB) nanosheets have been designed to obtain superior electrochemical HER performance with small overpotential of 69 mV at 10 mA cm(-2) current density and lower Tafel slope of 76.3 mV dec(-1) in alkaline medium. Incorporation of Ni leads to improved conductivity and favorable hydrogen adsorption on Mo sites, which collectively yield efficient electrocatalytic H-2 production from Ni-CMB catalyst. The ultrathin nature (thickness = 5.0 nm) of the designed material expectedly helps to attain high exposure of active sites and facile charge transportation through the nanosheets. Additionally, the decorated mesopores (average size = 3.86 nm) on nanosheets have benefitted towards faster electrolyte diffusion, easy gas escape from catalyst surface to support high electrocatalytic performance. Finally, well-maintained morphology of the sample and evolution of HER active sites in the material have guaranteed long-term, sustainable hydrogen production even at high current densities, which clearly demonstrate its superiority over an expensive electrolyzer (Pt-C) in alkaline water