[en] A new and facile method was presented to graft molecularly imprinted polymers (MIPs) on carbon nanotubes (CNTs) for 2,4-dichlorophenoxyacetic acid (2,4-D) analysis. In brief, CNTs were firstly coated with a layer of vinyl group modified silica, followed by a common precipitation polymerization with 2,4-D as the template, ethylene glycol dimethacrylate (EGDMA) as the crosslinker and 2,2-azobisisobutyronitrile (AIBN) as the initiator. The imprinted effects obtained by using different monomers were investigated, and the results showed that acrylamide (AM) and styrene as mixed monomers was the best choice. This functionalized material was characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and thermogravimetry (TG), which demonstrated a successful polymerization reaction on CNTs with MIPs grafting ratio of about 80%. The results of static adsorption experiments indicated the imprinted material possessed fast kinetics and good selectivity for 2,4-D molecules. A corresponding analytical method was developed and demonstrated to be applicable for the determination of 2,4-D in environmental water. The recoveries were in the range from 74.6% to 81.2% with relative standard deviation below 7.0%. To be emphasized, the method for MIPs coating proposed herein also provides a significant reference for other radical polymerization reactions based on CNTs.

[en] An effective adsorbent of diamine functionalized mesoporous silica on multi-walled carbon nanotubes (NN-mSiO₂@MWCNTs) has been prepared to remove heavy metals in aqueous solution. Structural characterization was conducted by Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), N₂ adsorption–desorption measurement and X-ray diffraction (XRD), which confirmed the successful grafting of organic moiety on mSiO2@MWCNTs. Metals removal from aqueous solution was examined for Cu(II), Ni(II), Pb(II) and Zn(II). In addition, Cu(II) adsorption process was thoroughly studied from both kinetic and equilibrium points of view. Adsorption kinetics could be well described by pseudo-second-order kinetic equation and exhibited 3-stage intraparticle diffusion mode. Adsorption isotherms fitted well with Langmuir model, exhibiting high adsorption capacity at low concentration. The thermodynamic analysis revealed that the adsorption of Cu(II) onto NN-mSiO₂@MWCNTs was endothermic and spontaneous. The prepared adsorbent is expected to be a new material for the removal and recovery of heavy metals from contaminated water.

[en] Objective: To determine the diagnostic value of CT in acute mesenteric infarction (AMI). Methods: Ten patients with mesenteric infarction (6 male, 4 female, average age 67.2 years old) were analysed from April 2003 to September 2004, whose symptoms include abdominal pain, melena, nausea and vomiting, etc. Nine cases were confirmed by surgery and pathology except one diedimmediately after CT scan. They included superior mesenteric arterial (SMA) thrombosis (n=4), superior mesenteric venous (SMV) thrombosis (n=5) and inferior mesenteric venous (IMV) thrombosis (n=1). Except one routine CT scan, all the other cases were performed by contrast-enhanced CT examination. Results: The direct sign of acute mesenteric infarction in CT images was filling defect in mesentery vessels (n=8). The indirect signs included dilatation of bowl loops (n=4), bowel wall thickening (n=6), the paper-thin wall sign (n=4), mesenteric stranding (n=5), mesenteric haziness (n=3), pneumatosis of bowel wall (n=2), portal veno gas (n=1) and ascites (n=3). Conclusion: Computed tomography is sensitive to acute mesenteric infarction and is valuable in diagnosis. (authors)

[en] The adsorption characteristics of O₂ and NO on Co-anchored different graphene-based substrates (single vacancy, double vacancy and N atoms doped) have been investigated using density functional theory. The geometric stability of the single atom catalysts, adsorption configurations of gas molecules, adsorption energies, electronic structure and thermodynamic analysis have been performed. Co/vacancy-graphene shows high thermodynamic stability through calculating and comparing the binding energy of Co-anchored single atom catalysts and the cohesive energy of Co bulk. For O₂ adsorption, it prefers to form two chemical bonds with the Co atom, and electron transfer dominates the formation of the strong chemical ionic bonds. While on Co single and double vacancy graphene substrates, N atom in NO invariably bonds to the Co atom, with electron transfer and orbital hybridization dominating the process of bonding formation respectively, afterwards ionic and covalent bonds formed between gas molecule and the metal atom. Additionally, electro-negativity and partial d-band centre are good descriptors of adsorption energies and can well reveal the relationship of adsorption energy with adsorption activity and the electronic structure. Co/single vacancy-graphene substrate with three pyridine nitrogen atoms (Co/SV-N123) is a promising catalyst in catalytic oxidation of NO. The results can provide reference for the further study of the NO oxidation mechanism on the Co/GN surface as well as the new non-noble-metal catalysts design.

[en] Density functional theory calculations were used to study the adsorption of NO on Ni_n cluster (n = 1, 2, 3 and 4) doped graphene with different graphene-based support (single vacancy, one nitrogen decorated, two nitrogen decorated and three nitrogen decorated). The adsorption configuration, adsorption energy, charge transfer, density of states of NO on Ni_n/graphene are thoroughly studied. In addition, the d-band center and Fermi softness have been performed to consider the support effect. It is found that the support effect has a significant effect on the adsorption characteristics of NO molecule, which depends on the electronic structure of graphene-based support. The electronic structure can be characterized by the Fermi softness of the catalyst. Ni atom plays a more and more obvious role in NO adsorption process, with the increase of the number of Ni atoms. The Fermi softness is a great descriptors for the adsorption activity of the Ni_n/graphene. This result can contribute to the systematic study of graphene catalysts supported on metal clusters.

[en] The electrocatalytic nitrogen reduction reaction (NRR) offers much prospect for substituting the energy-consuming Haber–Bosch process. Nevertheless, its sluggish reaction kinetics and the competing hydrogen evolution reaction always result in limited ammonia yield and low faradaic efficiency (FE). Here, an Fe-decorated porphyrinic metal–organic framework (MOF) is employed as a precursor to construct single-atom Fe implanted nitrogen-doped carbon catalysts (Fe₁-N-C) through a mixed ligand strategy. Benefiting from the highly dispersed single-atom Fe sites, hierarchically porous structure and good conductivity, Fe₁-N-C shows a FE of 4.51% and an ammonia yield rate of 1.56 × 10^-11 mol cm^-2 s^-1 at -0.05 V versus the reversible hydrogen electrode, superior to those of Co₁-N-C and Ni₁-N-C. Theoretical calculations show that Fe₁-N-C shows the lowest energy barrier of the rate-determining step during the NRR process, consistent with its highest activity obtained in experiments. This work reveals the unique potential of single-atom catalysts for the electrochemical NRR and provides in-depth insights into the catalytic mechanism of the NRR.

[en] Highlights: • The heterogeneous reduction mechanism of N₂O by char is revealed. • Char provides free sites and reduces the energy barrier of N₂O reduction. • The char edge structure has a notable influence on N₂O decomposition. • The participation of CO can reduce activation energy of N₂O reduction. - Abstract: In order to investigate heterogeneous reduction of N₂O by char, quantum chemistry theoretical calculation based on zigzag and armchair char edge model was conducted. Thermodynamics and kinetics calculation were carried out combined with density functional theory (DFT) and conventional transition state theory (TST). Theoretical calculation results indicate that heterogeneous reduction of N₂O by char undergoes two stages: N₂O decomposition on char edge and residual oxygen desorption from char edge. N₂O decomposition process is an exothermic reaction and takes place spontaneously and irreversibly. Char acts as an important catalyst which not only provides free sites for the heterogeneous reduction but also reduces the reaction energy barrier. The participation of CO can significantly reduce reaction activation energy of residual oxygen desorption. The char edge structure has notable influence on activation energy of N₂O decomposition on char edge. Activation energy values of N₂O decomposition on zigzag and armchair char edge are 33.91 kJ/mol and 163.58 kJ/mol, respectively. The calculation results can not only deepen understanding of the reaction mechanism but also provide theoretical guidance for operation optimization of low N₂O emission.

[en] As important parts of simultaneous removal of multiple pollutants in flue gas, catalytic oxidation of Hg⁰ and adsorption removal of SO₃, Pb species and As₂O₃ on the surface of (Fe,Co)@N-GN catalyst were systematically investigated in this work, using density functional theory. As a result, we found (Fe,Co)@N-GN not only showed great performance on Hg⁰ oxidation, but also exhibited outstanding removal capacity for SO₃, Pb species and As₂O₃ molecules. Besides, PDOS and EDD analysis were carried out and the result indicated that electron transfer and orbit hybridization played key roles in gas adsorption. In addition, thermodynamics analysis further evidenced (Fe,Co)@N-GN was a qualified sorbent under 550 K, and competitive analysis suggested that the adsorption order of pollution gas was determined by adsorption energy, rather than the volume fraction of corresponding gases in flue gas. We hoped this work can not only lay a foundation for theoretical investigation of Hg⁰ oxidation, but also provide an important guideline for simultaneous removal of multiple pollutants released from coal-fired power plants.

[en] Highlights: • Two novel high-capacity and high-stability anode materials for MXenes lithium-ion batteries. • A functional relationship that makes it easy to explore more MXenes materials. As an advanced battery technology, lithium-ion batteries have attracted extensive attention, especially in electric vehicles. However, low capacity and poor stability are two factors hindering more extensive application of lithium-ion batteries. Herein, we performed a screening study on MXenes including M₂C, MC₂, M₂N, MN₂ (M = Sc, Ti, V, Cr), in the search for promising lithium-ion battery anode materials by using density functional theory (DFT) calculations and ab initio molecular dynamic (AIMD) simulations. The theoretical capacities of Ti₂N and V₂N are 975 mAh/g and 924 mAh/g, respectively. Based on low deformation rate, and small energy variation, Ti₂N and V₂N have higher stability than that of graphite electrodes. The lower diffusion barrier accelerates the charging and discharging process of the battery. Considering their low diffusion barrier, excellent conductivity, high theoretical capacity, low deformation rate and high thermal stability, Ti₂N and V₂N are proposed as promising anode materials for lithium-ion batteries. The adsorption of lithium-ion belongs in the category of weak adsorption where the d-band center is negative. However, it is strong adsorption when the d-band center is positive. A linear relationship between the lithium-ion concentration and adsorption energy is developed for strong adsorption, which can be used to guide the design of high-capacity electrode materials.

[en] Highlights: • The eXtreme Gradient Boosting Regression (XGBR) algorithm was first applied to build a robust and predictive machine learning (ML) model for perovskite materials. • The method of combining machine learning and DFT calculation used in this article can greatly save computing resources and time, and accelerate the discovery of renewable energy materials. • Two lead-free perovskite structures screened out by the combination of machine learning and DFT calculation have reasonable band gap values, high environmental stability and good optical absorption properties. To accelerate the application of perovskite materials in photovoltaic solar cells, developing novel lead-free perovskite materials with suitable band gaps and high stability is vital. However, laborious experiment and density functional theory (DFT) calculation are time-consuming and incapable to screen promising perovskites rapidly and efficiently. Here, we proposed a novel search strategy combining machine learning and DFT calculation to screen 5,796 inorganic double perovskites. The eXtreme Gradient Boosting Regression (XGBR) algorithm was first applied to build a robust and predictive machine learning (ML) model for perovskite materials. XGBR algorithm yielded a lower mean square error (MSE) than both Artificial Neural Network (ANN) algorithm and Support Vector Regression (SVR) algorithm. From the ML model, two novel lead-free inorganic double perovskites: Na₂MgMnI₆, K₂NaInI₆, were obtained, suitable direct bandgaps of 1.46 eV for K₂NaInI₆ and 1.89 eV for Na₂MgMnI₆, which are similar to the organic–inorganic perovskite (MAPI3) CH₃NH₃PbI₃ (E_g = 1.6 eV), high thermal stability and good optical properties were also confirmed by DFT calculation. The method of combining ML and DFT exhibits high accuracy and significantly speeds up the discovery of promising perovskite materials.