Topics in Catalysis最新文献

In recent decades, the synthesis, characterization, and potential applications of metal oxide bronzes have been studied, with their potential applications as catalysts and electrocatalysts. Among these, materials based on mixed oxide bronzes, with specific crystalline structures, of molybdenum and/or tungsten have garnered some attention for their application in catalytic processes for the valorization of natural gas fractions (especially ethane and propane) and/or biomass derivatives (especially glycerol, but also other components). This paper presents a review of the types of synthesized materials and the catalytic processes in which molybdenum and/or tungsten oxides bronzes have been studied. In addition, it will be also presented P-containing catalysts as well as other pseudo-crystalline materials, which can be also of interest in these types of reaction.

This study presents the conversion of syngas (synthesis gas, CO + H₂) into isobutylene with monoclinic zirconia (m-ZrO₂), mixed monoclinic/tetragonal zirconia (m/t-ZrO₂), tetragonal zirconia (t-ZrO₂) and Ce/La-doped zirconia samples. The physical and chemical properties of the catalyst samples were characterised with nitrogen physisorption, NH₃ and CO₂ temperature-programmed desorption (TPD) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Sample material crystal structures were analysed with powder X-ray diffraction (PXRD) analysis. The pure monoclinic ZrO₂ sample showed the best catalytic performance with 66% CO conversion and 44 mol-% C/hydrocarbons isobutylene selectivity at reaction conditions of 450 °C, 45 bar, GHSV = 2000 h⁻¹, and CO: H₂ = 1:1. While pure monoclinic ZrO₂ was a highly efficient catalyst for the isosynthesis reaction, pure tetragonal ZrO₂ produced only a minor amount of isobutylene (~ 1 mol-%C/hydrocarbons). Interestingly, Ce/La-doped zirconia had a cubic/tetragonal crystal structure, while showing selectivity and activity comparable to m-ZrO₂ (CO conversion 64% and isobutylene selectivity of 40 mol-% C/hydrocarbons). Although samples with a purely t-ZrO₂ crystal phase significantly reduced the desired isobutylene selectivity, catalysts with relatively high Ce/La loadings (17% and 5% of the total catalyst mass) in the cubic/tetragonal crystal structure achieved high isobutylene activity and selectivity, owing to the dopant-induced high base-to-acid site ratio and enhanced synthesis gas adsorption capacity. According to the results from density functional theory (DFT) calculations, the poor selectivity of the t-ZrO₂ phase towards isobutylene was due to the promoted intermediate methanation on the coordinatively saturated sites of t-ZrO₂. Compared to the t-ZrO₂ sample, m-ZrO₂ showed high isobutylene selectivity and activity related to high base to acid site ratio, high synthesis gas adsorption capacity, and kinetically favourable crystal structure to form C₂₊ hydrocarbons from C₁ intermediates. These results are important factors in the future work to prepare intrinsically active and isobutylene selective catalysts for the isosynthesis reaction.

Voltammetric techniques have been widely used in developing analytical methods for antibiotic determination. These techniques are based on the oxidation and/or reduction of antibiotic molecules occurring at the surface of the working electrode. Carbon nanomaterials have been used to modify the working electrode surface to produce an analytical device with enhanced performance. Considering the importance of understanding the electrochemical mechanism of antibiotic molecules on the electrode surface, and the increasing number of authors proposing these reaction mechanisms for carbon nanomaterial-modified electrodes, this review compiles recent reports on the proposed redox mechanisms for selected antibiotics. Key aspects such as the type of carbon nanomaterial, redox potential, supporting electrolyte, and kinetic mechanism involved in these processes are considered. A review was organized in the ScienceDirect^® and Scopus^® databases, using the keywords “determination”, “voltammetry”, name of antibiotic, and combinations of the antibiotic name with “voltammetry”, covering publications from January 2023 to December 2024. Researchers interested in using voltammetric techniques for antibiotic determination will benefit from this review, gaining insights into their redox reaction mechanisms on carbon nanomaterials-based modified working electrodes.

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Studying the potential dependent interaction of non-aqueous electrolytes, such as ionic liquids (ILs), with model electrode surfaces plays a crucial role not only in the field of battery-related research. These electrolytes bear the advantage that their electrochemical stability windows often exceed that of water. However, comparing results using ILs as electrolytes reported in the literature reveals strong discrepancies in the reproducibility of the data. In this study, we show that parameters such as the supplier, the supplied batch, and the purification steps can have a huge impact on the electrochemical properties. As a reference system, these properties are studied by cyclic voltammetry on a Au(111) single crystal electrode in N-methyl-N-propylpiperidinium bis(trifluoromethane)sulfonimide ([MPPip][TFSI]). Analysing the different features observed in the cyclic voltammograms, we were to some extent able to deconvolute the features that are related to the interaction of ILs with the substrate and impurities added from the pretreatment due to the influence of residual water and oxygen.

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Biomass-derived bio-oils, particularly those obtained from fast pyrolysis, offer a renewable alternative to fossil fuels. However, their high oxygen content and low stability limit their direct use as transportation fuels. Catalytic hydrodeoxygenation (HDO) represents a promising strategy to increase the quality of bio-oil by removing oxygen from organic compounds. This study investigated the catalytic performance of monometallic and bimetallic Ni–Fe catalysts supported on Nb₂O₅ and SiO₂ for the hydrodeoxygenation (HDO) of guaiacol, a model compound representative of bio-oil derived from lignin. The catalysts were synthesized, characterized, and tested under identical conditions to evaluate the effects of metal composition, support properties, and reaction temperature on activity and selectivity. Compared with their monometallic counterparts, bimetallic Ni–Fe catalysts demonstrated superior performance, with the 5Ni1Fe/Nb₂O₅ catalyst achieving the highest guaiacol conversion (59%) and enhanced selectivity toward cyclohexanol (34%) and cyclohexane (10%) at 300 °C. The synergistic interaction between Ni and Fe facilitated hydrogenation and hydrogenolysis pathways, whereas the acidic and oxophilic properties of Nb₂O₅ promoted direct deoxygenation (DDO) by enhancing C–O bond cleavage. In contrast, SiO₂-supported catalysts exhibited greater Ni dispersion but were more selective toward partially deoxygenated intermediates, such as cyclohexanone and catechol. Among the different temperatures investigated, the HDO at 300 °C maximized fully deoxygenated products, whereas lower and higher temperatures favored partial deoxygenation and hydrogenolysis pathways, respectively.

Nickel-promoted molybdenum carbide (Mo₂C) catalysts supported on γ-Al₂O₃ were synthesized via incipient wetness impregnation and evaluated for dry methane reforming (DMR). Comprehensive physicochemical characterizations including XRD, SEM-EDS, H₂-TPR, XPS, TPSR, and TG-DSC were conducted to elucidate structure-performance relationships. Non-promoted Mo₂C exhibited poor catalytic stability due to oxidation during DMR. The incorporation of nickel significantly enhanced catalytic activity and stability by promoting the in-situ re-carburization of oxidized Mo species and facilitating methane activation. The optimized Ni/Mo molar ratio of 1:1 led to the formation of a stable Ni-Mo synergistic phase, which exhibited superior resistance to sintering and deactivation.

This study employed machine learning (ML) to predict nitrogen dioxide (NO₂) pollution in Marylebone Road, London a high-traffic urban corridor using historical data from 2015 to 2022 to forecast concentrations for the period January 2023 to January 2025. Four ML models were developed and evaluated: Linear Regression, Random Forest, LightGBM, and an Ensemble Stacking model. These models incorporated meteorological and pollutant data and were assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). The Ensemble Stacking model outperformed the others, achieving an R² of 0.9723, MAE of 3.91 µg/m³, and RMSE of 6.25 µg/m³. In comparison, the Linear Regression model showed the lowest performance (R² = 0.8307, MAE = 11.55, RMSE = 15.45), while Random Forest (R² = 0.9232) and LightGBM (R² = 0.9719) demonstrated intermediate accuracy. The best-performing ensemble model was further used to simulate NO₂ trends with and without titanium dioxide (TiO₂) catalyst intervention, assuming a 28% NO₂ reduction. Temporal analysis revealed that NO, NO₂, and NOₓ concentrations peaked during colder months (November–January) and weekdays. Correlation analysis showed a weak negative relationship between NO₂ and ozone (O₃) (R² = 0.26), moderate positive correlations with black carbon (BC) (R² = 0.597) and sulfur dioxide (SO₂) (R² = 0.654), and a very weak positive correlation with particulate matter (PM2.5) (R² = 0.143). The study concludes that ensemble stacked ML models are effective for predicting NO₂ concentrations and that TiO₂ nanocatalyst interventions hold promise for reducing NO₂, BC, and SO₂ levels in urban environments.