The dehydrogenation reaction mechanisms of methane catalyzed by a ligated transition metal MH(+) (M = Ru, Rh, and Pd) have been investigated theoretically. Activation of methane by MH(+) complexes is proposed to proceed in a one-step manner via one transition state: MH(+) + CH(4) --> MH(+)CH(4) --> [TS] --> (MCH(3)(+))H(2) -->MCH(3)(+) + H(2). Both high-spin and low-spin potential energy surfaces are characterized in detail. Our calculations indicate that the ground-states species have low electron spin and a dominant 4d(n) configuration for RuH(+), RhH(+), and PdH(+), and the whole reaction proceeds on the ground-states potential energy surfaces with a spin-allowed manner. The MH(+) (M = Ru, Rh, and Pd) complexes are expected from the general energy profiles of the reaction pathways to efficiently convert methane to metal methyl, thus RuH(+), RhH(+), and PdH(+) are likely to be excellent mediators for the activity of methane. In the reactions of MH(+) with methane, the H(2) elimination from the dihydrogen complex is quite facile without barriers. The exothermicities of the reactions are close for Ru, Rh, and Pd: 11.1, 1.2, and 5.2 kcal/mol, respectively.

Terrain factors,although different in the definition and calculation method,relate each other at different extent.Such relationship can be represented by a correlation index,which reveals the process and stage of terrain development as well.This paper focuses mainly on the correlation between different terrain factors and the mean-slope by means of the Back Propagation model of Neural Network with a latent layer.Furthermore,the regression model and the NN model without a latent layer are compared with the NN model with a latent layer.Fifteen loess gully-hill areas are selected as the experimental area,and the relevant 1∶10 000 scale DEMs(5 m×5 m grid) are applied as the basic data.From the results of the NN model with a latent layer,it is found that roughness and undulation are the most closely correlated with mean-slope.Compared with others,channel density and mean elevation are the least correlated with mean-slope.Experiment results show the NN model with a latent layer is better than the others and it can effectively evaluate the correlation between the terrain factors extracted from DEMs.This method provides a new methodology in the selection of suitable and available terrain factors and the estimation of the relevancy between these factors.

不同的地形因子从不同侧面反映地面的起伏特征或空间变异,各因子之间所存在的相互关联、相互制约、相互影响的程度与特征,在很大程度上揭示了地形发育与空间变异的内在本质,因而是地形学研究的重要内容.他以黄土高原丘陵沟壑区的16个样本地区为实验样区,以高分辨率、高精度的1:1万比例尺DEM为基准数据,应用BP神经网络模型,探讨地面坡度与其他地形因子之间的关联性特征.实验结果表明,利用神经网络分析方法可以有效评价地形因子对地面平均坡度的关联性.该研究方法为进行地貌多定量指标的的选择和多因子之间关联性的量化提供了一种新的方法.

Different terrain factors express the undulating characteristics and spatial variations of the true surface from different aspects. The relationships among them can play a key role in revealing the mechanism and development of the terrain and geomorphologic situation to a great extent. The relationships and their variance discipline between the terrain factors and mean slope are discussed in this paper via the Back Propagation model of Neural Network. Fifteen loess gully-hilly areas are selected as the test areas for experiment, and the relevant 1∶10 000 and 1∶50 000 map scale DEMs of high resolution and high precision are also selected as the basic data. The results show this method can effectively evaluate the relevancy of terrain factors on the mean slope extracted from DEMs at the two scales. It is hoped that this result can be helpful in evaluating the availability of the DEM scale applied, determining the relevancies among multiple topographical factors as well as selecting suitable terrain variables for different applications.