Universal Formation of Single Atoms from Molten Salt for Facilitating Selective CO2 Reduction.

Clarifying the formation mechanism of single-atom sites guides the design of emerging single-atom catalysts (SACs) and facilitates the identification of the active sites at atomic scale. Herein, a molten-salt atomization strategy is developed for synthesizing zinc (Zn) SACs with temperature universality from 400 to 1000/1100 °C and an evolved coordination from Zn-N2Cl2 to Zn-N4. The electrochemical tests and in situ attenuated total reflectance-surface-enhanced infrared absorption spectroscopy confirm that the Zn-N4 atomic sites are active for electrochemical carbon dioxide (CO2) conversion to carbon monoxide (CO). In a strongly acidic medium (0.2 m K2SO4, pH = 1), the Zn SAC formed at 1000 °C (Zn1NC) containing Zn-N4 sites enables highly selective CO2 electroreduction to CO, with nearly 100% selectivity toward CO product in a wide current density range of 100-600 mA cm-2. During a 50 h continuous electrolysis at the industrial current density of 200 mA cm-2, Zn1NC achieves Faradaic efficiencies greater than 95% for CO product. The work presents a temperature-universal formation of single-atom sites, which provides a novel platform for unraveling the active sites in Zn SACs for CO2 electroreduction and extends the synthesis of SACs with controllable coordination sites.

An adaptive adjacency matrix-based graph convolutional recurrent network for air quality prediction.

In recent years, air pollution has become increasingly serious and poses a great threat to human health. Timely and accurate air quality prediction is crucial for air pollution early warning and control. Although data-driven air quality prediction methods are promising, there are still challenges in studying spatial-temporal correlations of air pollutants to design effective predictors. To address this issue, a novel model called adaptive adjacency matrix-based graph convolutional recurrent network (AAMGCRN) is proposed in this study. The model inputs Point of Interest (POI) data and meteorological data into a fully connected neural network to learn the weights of the adjacency matrix thereby constructing the self-ringing adjacency matrix and passes the pollutant data with this matrix as input to the Graph Convolutional Network (GCN) unit. Then, the GCN unit is embedded into LSTM units to learn spatio-temporal dependencies. Furthermore, temporal features are extracted using Long Short-Term Memory network (LSTM). Finally, the outputs of these two components are merged and air quality predictions are generated through a hidden layer. To evaluate the performance of the model, we conducted multi-step predictions for the hourly concentration of PM2.5, PM10 and O3 at Fangshan, Tiantan and Dongsi monitoring stations in Beijing. The experimental results show that our method achieves better predicted effects compared with other baseline models based on deep learning. In general, we designed a novel air quality prediction method and effectively addressed the shortcomings of existing studies in learning the spatio-temporal correlations of air pollutants. This method can provide more accurate air quality predictions and is expected to provide support for public health protection and government environmental decision-making.

Photosynthesis of Hydrogen Peroxide Based on g-C3 N4 : The Road of a Cost-Effective Clean Fuel Production.

Emerging artificial photosynthesis promises to offer a competitive means for solar energy conversion and further solves the energy crisis facing the world. Hydrogen peroxide (H2 O2 ), which is considered as a benign oxidant and a prospective liquid fuel, has received worldwide attention in the field of artificial photosynthesis on account of the source materials are just oxygen, water, and sunlight. Graphitic carbon nitride (g-C3 N4 )-based photocatalysts for H2 O2 generation have attracted extensive research interest due to the intrinsic properties of g-C3 N4 . In this review, research processes for H2 O2 generation on the basis of g-C3 N4 , including development, fabrication, merits, and disadvantages, and the state-of-the-art methods to enhance the performance are summarized after a brief introduction and the mechanism analysis of an efficient catalytic system. Also, recent applications of g-C3 N4 -based photocatalysts for H2 O2 production are reviewed, and the significance of active sites and synthetic pathways are highlighted from the view of reducing barriers. Finally, this paper ends with some concluding remarks to reveal the issues and opportunities of g-C3 N4 -based photocatalysts for producing H2 O2 in a high yield.

Decomposition of gaseous chlorobenzene using a DBD combined CuO/α-Fe2O3 catalysis system.

Copper oxide and hematite (CuO/α-Fe2O3) composite catalysts were prepared by using goethite as precursor adopted impregnation way and applied to the dielectric barrier discharge (DBD) catalytic decomposition of gaseous chlorobenzene. The CuO/α-Fe2O3 composite was characterised by X-ray diffraction, Brunauer-Emmett-Teller method, scanning electron microscopy and X-ray photoelectron spectrometer technique. The decomposition efficiency and energy yield of gaseous chlorobenzene in DBD catalysis system were studied by a function of gas flow rate, initial concentration and input voltage. The results showed that the CuO/α-Fe2O3 composite catalyst exhibited remarkable performance on chlorobenzene decomposition when the molar ratio was 0.4 and calcination temperature was 450°C. When the chlorobenzene initial concentration was 230 mg m-3, the chlorobenzene decomposition efficiency and mineralisation rate on the DBD catalysis system reached 73.33% and 63.37%, respectively, its decomposition and mineralisation efficiency were enhanced about 20.5% and 16.61%, respectively, compared with the bare DBD system, and it also benefited to significantly reduce the ozone and NO2 by-products. The possible pathway of chlorobenzene decomposition in the DBD catalytic hybrid system was proposed based on the products analysis.

A Conductive Network and Dipole Field for Harnessing Photogenerated Charge Kinetics.

Photogenerated charge separation and directional transfer to active sites are pivotal steps in photocatalysis, which limit the efficiency of redox reactions. Here, a conductive network and dipole field are employed to harness photogenerated charge kinetics by using a Ti3 C2 /TiO2 network (TTN). The TTN exhibits a prolonged charge-carrier lifetime (1.026 ns) and an 11.76-fold increase in hexavalent chromium photoreduction reaction kinetics compared to TiO2 nanoparticles (TiO2 NPs). This super photocatalytic performance is derived from the efficient photogenerated charge kinetics, which is steered by the conductive network and dipole field. The conductivity enhancement of the TiO2 network is achieved by continuous chemical bonds, which promotes electron-hole (e-h) separation. In addition, at the interface of Ti3 C2 and TiO2 , band bending induced by the dipole field promotes photogenerated electron spatially directed transfer to the catalytic sites on Ti3 C2 . This study demonstrates that a conductive network and dipole field offer a new concept to harness charge kinetics for photocatalysis.

Aerogel doped by sulfur-functionalized graphene oxide with convenient separability for efficient patulin removal from apple juice.

Patulin (PAT) contaminant causes severe food safety issue throughout apple industry. Although adsorption is the feasible approach to remove PAT, the limited adsorption capacity and separation difficulty of most adsorbent is the major drawback that remains to be overcome. Here GO-SH doped aerogel was prepared and used for removal PAT from apple juice. The intrinsic porous of the aerogel and abundant active sites including -COOH, -NH2 and -SH offered the PAT adsorption capacity of 24.75 µg/mg that superior to most reported adsorbents. Furthermore, it could reduce 89 ± 1.23% PAT in real apple juice without juice quality deterioration and cytotoxicity. Importantly, the aerogel with good mechanical strength and structure stability could endure the complex juice solution so that there was no any residue after convenient separation of the aerogel, which proved that the proposed aerogel was a promising adsorbent to be applied to apple juice industry for PAT removal.