Relationship between the microstructure and reaction performance of aluminosilicate

A systematic study was conducted to comprehend the mechanism of thermal activation of silica-alumina materials by using ^29Si and ^27Al magnetic angle spinning nuclear magnetic resonance （MAS NMR） spectroscopy. The reaction performance of silica-alumina-based materials with different molar ratios of Si/Al, which were thermally activated, was also investigated. With the increase in calcining temperature, the coordination of Al in metakaolin becomes four, five, and six firstly, and then transforms completely to four and six. It is indicated by identical coupled plasma optical emission spectroscopy （ICP） and NMR that, the reaction performance of monomeric silicate anions is better than that of polymeric silicate anions which are primarily cross-linked in the alkali solution. Moreover, it also shows that the thermal activation temperature, cooling method, and the molar ratio of Na/Ca have remarkable effects on the reaction performance.

Ag3PO4 Microcrystals Synthesized by Room-Temperature Solid State Reaction:Enhanced Photocatalytic Activity and Photoelectronchemistry Performance

Ag3PO4 microcrystals with highly enhanced visible light photocatalytic activity are prepared by a facile and simple solid state reaction at room temperature. The composition, morphology and optical properties of the asprepared Ag3PO4 microcrystMs are characterized by x-ray diffraction, scanning electron microscopy and UV-vis diffuse reflectance spectra. The photocatalytie properties of Ag3PO4 are investigated by the degradation of both methylene blue and methyl orange dyes under visible light irradiation. The as-prepared Ag3PO4 microcrystals possess high photocatalytic oxygen production with the rate of 673μmolh-1g-1. Moreover, the as-prepared Ag3PO4 microcrystals show an enhanced photoelectrochemistry performance under irradiation of visible light.

Machine Learning Prediction of Structure-Performance Relationship in Organic Synthesis

Data-driven approach has emerged as a powerful strategy in the construction of structure-performance relationships in organic synthesis.To close the gap between mechanistic understanding and synthetic prediction,we have made efforts to implement mechanistic knowledge in machine learning modelling of organic transformation,as a way to achieve accurate predictions of reactivity,regio-and stereoselectivity.We have constructed a comprehensive and balanced computational database for target radical transformations(arene C—H functionalization and HAT reaction),which laid the foundation for the reactivity and selectivity prediction.Furthermore,we found that the combination of computational statistics and physical organic descriptors offers a practical solution to build machine learning structure-performance models for reactivity and regioselectivity.To allow machine learning modelling of stereoselectivity,a structured database of asymmetric hydrogenation of olefins was built,and we designed a chemical heuristics-based hierarchical learning approach to effectively use the big data in the early stage of catalysis screening.Our studies reflect a tiny portion of the exciting developments of machine learning in organic chemistry.The synergy between mechanistic knowledge and machine learning will continue to generate a strong momentum to push the limit of reaction performance prediction in organic chemistry.

Organic-acid-assisted synthesis of a 3D lasagna-like Fe-N-doped CNTs-G framework： An efficient and stable electrocatalyst for oxygen reduction reactions

The scalable preparation of multi-functional three-dimensional （3D） carbon nanotubes and graphene （CNTs-G） hybrids via a well-controlled route is urgently required and challenging. Herein, an easily operated, oxalic acid-assisted method was developed for the in situ fabrication of a 3D lasagna-like Fe-N-doped CNTs-G framework （LMFC） from a precursor designed at the molecular level. The well-organized architecture of LMFC was constructed by multi-dimensionally interconnected graphene and CNTs which derived from porous graphene sheets, to form a fundamentally robust and hierarchical porous structure, as well as favorable conductive networks. The impressive oxygen reduction reaction （ORR） performances in both alkaline and acidic conditions helped confirm the significance of this technically favorable morphological structure. This product was also the subject of research for the exploration of decisive effects on the performance of ORR catalysts with reasonable control variables. The present work further advances the construction of novel 3D carbon architectures via practical and economic routes.

Ultrastable bimetallic Fe_(2)Mo for efficient oxygen reduction reaction in pH-universal applications

Iron-based nanostructures represent an emerging class of catalysts with high electroactivity for oxygen reduction reaction(ORR)in energy storage and conversion technologies.However,current practical applications have been limited by insufficient durability in both alkaline and acidic environments.In particular,limited attention has been paid to stabilizing iron-based catalysts by introducing additional metal by the alloying effect.Herein,we report bimetallic Fe_(2)Mo nanoparticles on N-doped carbon(Fe_(2)Mo/NC)as an efficient and ultra-stable ORR electrocatalyst for the first time.The Fe_(2)Mo/NC catalyst shows high selectivity for a four-electron pathway of ORR and remarkable electrocatalytic activity with high kinetics current density and half-wave potential as well as low Tafel slope in both acidic and alkaline medias.It demonstrates excellent long-term durability with no activity loss even after 10,000 potential cycles.Density functional theory(DFT)calculations have confirmed the modulated electronic structure of formed Fe_(2)Mo,which supports the electron-rich structure for the ORR process.Meanwhile,the mutual protection between Fe and Mo sites guarantees efficient electron transfer and long-term stability,especially under the alkaline environment.This work has supplied an effective strategy to solve the dilemma between high electroactivity and long-term durability for the Fe-based electrocatalysts,which opens a new direction of developing novel electrocatalyst systems for future research.

Radical recombination in a hydrocarbon-fueled scramjet nozzle

To reveal the radical recombination process in the scramjet nozzle flow and study the effects of various factors of the recombination, weighted essentially non-oscillatory（WENO）schemes are applied to solve the decoupled two-dimensional Euler equations with chemical reactions to simulate the hydrocarbon-fueled scramjet nozzle flow. The accuracy of the numerical method is verified with the measurements obtained by a shock tunnel experiment. The overall model length is nearly 0.5 m, with inlet static temperatures ranging from 2000 K to 3000 K, inlet static pressures ranging from 75 k Pa to 175 k Pa, and inlet Mach numbers of 2.0 ± 0.4 are involved.The fraction Damkohler number is defined as functions of static temperature and pressure to analyze the radical recombination progresses. Preliminary results indicate that the energy releasing process depends on different chemical reaction processes and species group contributions. In hydrocarbon-fueled scramjet nozzle flow, reactions with H have the greatest contribution during the chemical equilibrium shift. The contrast and analysis of the simulation results show that the radical recombination processes influenced by inflow conditions and nozzle scales are consistent with Damkohler numbers and potential dissociation energy release. The increase of inlet static temperature improves both of them, thus making the chemical non-equilibrium effects on the nozzle performance more significant. While the increase of inlet static pressure improves the former one and reduces the latter, it exerts little influence on the chemical non-equilibrium effects.