Stand basal area modelling for Chinese fir plantations using an artificial neural network model

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

Surface modification of CeO_(2-x) nanorods with Sn doping for enhanced nitrogen electroreduction

While the electrochemical nitrogen reduction reaction(NRR) represents a prospective blueprint for environmentally renewable ammonia generation,it has yet to overcome the limitations of weak activity and inferior selectivity.In this regard,surface modification tactic was constructed to markedly enhance the activity and selectivity via introducing Sn atoms into the surface of defective cerium oxide(denoted as Sn-CeO_(2-x)) as the active and robust electrocatalyst for NRR under benign environment.The introduction of Sn atoms in CeO_(2-x)can not only inhibit the HER activity of the catalyst but also modulate the electronic structure of ceria and optimize N-Ce interaction,thus enhancing NRR activity and selectivity.Outperforming all previous CeO_(2)-based NRR catalysts,this catalyst has demonstrated an ammonia yield rate of 41.1 μg mg_(cat)^(-1) h^(-1) and an exceptional Faradic efficiency of 35.3%.This work presents a viable approach for the development of advanced NRR electrocatalysts.

Construction of cobalt vacancies in cobalt telluride to induce fast ionic/electronic diffusion kinetics for lithium-ion half/full batteries

Designing novel electrode materials with unique structures is of great significance for improving the performance of lithium ion batteries(LIBs).Herein,copper-doped Co_(1-x)Te@nitrogen-doped carbon hollow nanoboxes(Cu-Co_(1-x)Te@NC HNBs)have been fabricated by chemical etching of Cu Co-ZIF nanoboxes,followed by a successive high-temperature tellurization process.The as-synthesized Cu-Co_(1-x)Te@NC HNBs composite demonstrated faster ionic and electronic diffusion kinetics than the pristine Co Te@NC HNBs electrode.The existence of Co-vacancy promotes the reduction of Gibbs free energy change(ΔG_(H^(*)))and effectively improves the Li~+diffusion coefficient.XPS and theoretical calculations show that performance improvement is ascribed to the electronic interactions between Cu-Co_(1-x)Te and nitrogen-doped carbon(NC)that trigger the shift of the p-band towards facilitation of interfacial charge transfer,which in turn helps boost up the lithium storage property.Besides,the proposed Cu-doping-induced Co-vacancy strategy can also be extended to other conversion-type cobalt-based material(CoSe_(2))in addition to asobtained Cu-Co_(1-x)Se_(2)@NC HNBs anodes for long-life and high-capacity LIBs.More importantly,the fabricated LiCoO_(2)//Cu-Co_(1-x)Te@NC HNBs full cell exhibits a high energy density of 403 Wh kg^(-1)and a power density of 6000 W kg^(-1).We show that the energy/power density reported herein is higher than that of previously studied cobalt-based anodes,indicating the potential application of Cu-Co_(1-x)Te@NC HNBs as a superior electrode material for LIBs.