Among them, one of the most important but challenging reactions is the alkynes' semihydrogenation to corresponding alkenes because of their poor alkene selectivity due to excessive hydrogenation and desorption barriers of alkenes, especially when the conversion is high. [7][8][9][10] The current problem-solving technique mainly relies on the development of diverse supported metal catalysts, [11][12][13][14] while the precondition is to exploit available support material that can stabilize and promote the active metal sites to own outstanding catalytic activity and selectivity for the semihydrogenation of alkynes.In this respect, nitrogen-doped carbons, as a type of promising support materials, have attracted tremendous attention due to their unique electron effects, tailorable surface characteristics, and excellent chemical and thermal stability. [15][16][17][18][19][20][21][22] In the regime of strong metal-support interactions, there are electron interactions in the form of charge redistribution and structural interactions in the form of mass redistribution between nitrogen-doped carbon and the anchored active metals. [23][24][25] Consequently, accurately adjusting the composition and spatial structure of nitrogen-doped carbon is a key point. High nitrogen contents can effectively limit the size of the metal, and more importantly, abundant coordination sites formed with metal can greatly lower the reaction energy barrier, resulting in high catalytic activity. And the electron effect generated by the nitrogen species in the supports can alter the electron density of the active metal sites, [26][27][28] which favors desorption of the monoene to obtain high catalytic selectivity. Following these principles, plentiful nitrogen-doped carbon-supported metals have been proven to be appealing catalysts. However, due to the proximity of nitrogen dopants' generation energy, the incorporation of nitrogen atoms into the carbon skeleton leads to the simultaneous doping of various nitrogen configurations including pyridinic N, pyrrolic N, graphitic N, and oxidized N. [29][30][31] Under this circumstance, most of the previous works simply manifest that active metal centers (M) are coordinated with nitrogen atoms (N) embedded in the matrix of carbon to form M-N x as active sites, [32][33][34] but it is still controversial for researchers to sufficiently understand which nitrogen configuration can dominant strong metal-support interactions to Supported metal catalysts have played an important role in optimizing selective semihydrogenation of alkynes for fine chemicals. There into, nitrogendoped carbons, as a type of promising support materials, have attracted extensive attentions. However, due to the general phenomenon of random doping for nitrogen species in the support, it is still atremendous challenge to finely identify which nitrogen configuration dominates the catalytic property of alkynes' semihydrogenation. Herein, it is reported that uniform mesoporous N-doped carbon spheres derived from mesoporous polypyrrole spheres ...

The ordered mesoporous perovskite oxides with well-defined mesostrcture and versatile metal sites are attractive, but their successful synthesis faces challenges of complicated assembly dynamics and pore collapse in crystalline calcination. Here, we propose an energy balance concept to reveal interplay relationship in assembly process and realize regulation of porous structure for mesoporous perovskite oxides. A series of ordered mesoporous perovskite oxides with unique porous structure were prepared by a modular coassembly method. Mesoporous La 2 Zr 2 O 7 shows 94 % conversion and 99 % selectivity for hydrogenation of 5hydroxymethylfurfural (HMF) to 2,5-bis(hydroxymethyl)furan. Experiments reveal that rich Lewis acid sites, active Zr species, and favorable porous structure promote interaction between mesoporous La 2 Zr 2 O 7 and HMF and reduce catalytic energy barrier. This work provides the insight into molecule co-assembly and developing multiple component ordered mesoporous materials.

Monitoring the temporal and spatial variation of soil properties is helpful to understand the evolution of soil properties and adjust the management method in time. Soil fertility evaluation is an urgent need to understand soil fertility level and prevent soil degradation. Here, we conducted an intensive field investigation in Chinese hickory (Carya cathayensis Sarg.) plantation to clarify the spatial and temporal variation of soil properties and its influencing factors, and to evaluate the change of soil fertility. The results showed that the soil pH and soil organic carbon (SOC) significantly increased from 2008 to 2018, while available nitrogen (AN) significantly decreased from 2008 to 2018. The semi-variance revealed that except available phosphorus (AP), the spatial dependencies of soil properties increased from 2008 to 2018. An increasing south-north gradient was found for soil AN, AP, available potassium (AK) and SOC and a decreasing south-north gradient was found for soil pH. The average soil fertility in the whole area was increased from 2008 to 2018. Our findings demonstrated that the changes of the management measures were the reason for the change of soil properties from 2008 to 2018. Therefore, rational fertilization strategies and sod cultivation are recommended to maintain the long-term development of the producing forest.

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With the deepening reform of the power system, power sales companies need to adopt new power sales strategies to provide customers with better economic marketing solutions. Customer-side configuration of an energy storage system (ESS) can participate in power-related policies to reduce the comprehensive cost of electricity for commercial and industrial customers and improve customer revenue. For power sales companies, this can also attract new customers, expand sales and quickly capture the market. However, most of the ESS evaluation models studied so far are based on historical data configuration of typical daily storage capacity and charging and discharging scheduling instructions. In addition, most models do not adequately consider the performance characteristics of the ESS and cannot accurately assess the economics of the energy storage model. This study proposes an intelligent power sales strategy based on load forecasting with the participation of optimal allocation of ESS. Based on long short-term memory (LSTM) artificial neural network for predictive analysis of customer load, we evaluate the economics of adding energy storage to customers. Based on the premise of the two-part tariff, the ESS evaluation model is constructed with the objective of minimizing the annual comprehensive cost to the user by considering the energy tariff and the savings benefits of the basic tariff, assessing the annualized cost of ESS over its entire life cycle, and the impact of battery capacity decay on economics. The particle swarm optimization (PSO) algorithm is introduced to solve the model. By simulating the arithmetic example for real customers, their integrated electricity costs are significantly reduced. Moreover, this smart power sales strategy can provide different sales strategies according to the expected payback period of customers. This smart sales strategy can output more accurate declared maximum demand values than other traditional sales strategies, providing a more economical solution for customers.