Active Sites in Heterogeneous Catalytic Reaction on Metal and Metal Oxide: Theory and Practice

Active sites play an essential role in heterogeneous catalysis and largely determine the reaction properties. Yet identification and study of the active sites remain challenging owing to their dynamic behaviors during catalysis process and issues with current characterization techniques. This article provides a short review of research progresses in active sites of metal and metal oxide catalysts, which covers the past achievements, current research status, and perspectives in this research field. In particular, the concepts and theories of active sites are introduced. Major experimental and computational approaches that are used in active site study are summarized, with their applications and limitations being discussed. An outlook of future research direction in both experimental and computational catalysis research is provided

Carbon Material-Based Flow-Electrode Capacitive Deionization for Continuous Water Desalination

Flow-electrode capacitive deionization (FCDI) offers an electrochemical, energy-efficient technique for water desalination. In this work, we report the study of carbon-based FCDI, which consists of one desalination chamber and one salination chamber and applies a carbon nanomaterials-based flow electrode that circulates between the cell anode and cathode, to achieve a fast, continuous desalination process. Five different carbon nanomaterials were used for preparing the flow electrode and were studied for the desalination performance, with properties including average salt removal rate (ASRR), salt removal efficiency (SRE), energy consumption (EC) and charge efficiency (CE) being quantitatively determined for comparation. Different FCDI parameters, including carbon concentration and flow rate of the flow electrode and cell voltage, were investigated to examine the influences on the desalination. Long-term operation of the carbon-based FCDI was evaluated using the optimal results found in the conditions of 1.5 M concentration, 1.5 V cell voltage, and 20 mL min−1 flow rate of electrode and water streams. The results showed an ASRR of 63.7 µg cm−2 min−1, EC of 162 kJ mol−1, and CE of 89.3%. The research findings validate a good efficiency of this new carbon-based FCDI technology in continuous water desalination and suggest its good potential for real, long-term application

The Effectiveness of Ni-Based Bimetallic Catalysts Supported by MgO-Modified Alumina in Dry Methane Reforming

Syngas is produced through the carbon dioxide reforming of methane. The traditional nickel-based catalysts are substantially destroyed by carbon deposition. The reforming reaction was conducted in a tubular microreactor at 700 °C using bimetallic Ni catalysts supported over 37% Al2O3 and 63% MgO mixtures. The impregnation process formed the catalysts, which were subsequently examined by N2-physisorption, XRD, H2-TPR, TGA, and Raman spectroscopy. The 2.5Ni+2.5Co/37%Al2O3+63%MgO bimetallic catalyst, which displayed 72% and 76% conversions of CH4 and CO2 over the course of a seven-hour procedure, was discovered to be the most active in DRM. The bimetallic catalyst with the largest weight loss in TGA, 2.5Ni+2.5Fe-MG63, had a loss of 61.3%, a difference of 26% and 21% in the activity performance of CH4 and CO2, respectively, of the tested bimetallic Ni catalysts was recorded. The long-time of 30 h on-stream CH4 and CO2 conversion reactions for 2.5Ni+2.5Co-MG63 and 2.5Ni+2.5Ce-MG63 catalysts showed the catalysts’ high stability. The TPO analysis for the 2.5Ni+2.5Cs-MG63 catalyst showed a peak at 650 °C, attributed to the oxidation of the filamentous carbon, whereas the TPO analysis for the 2.5Ni+2.5Co-MG63 catalyst depicted a peak at 540 °C, ascribed to the presence of amorphous/graphite carbon

Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: insights using a novel nickel-strontium-zirconium-aluminum catalyst

This study investigates the molecular dynamics of methane dry reforming catalyzed by a novel nickel-strontium-zirconium-aluminum (5Ni+3Sr/10Zr+Al) catalyst, leveraging both Response Surface Methodology (RSM) and Radial Basis Function Neural Network (RBFNN) for predictive optimization. Focusing on the impact of operational parameters—hourly space velocity, reaction temperature, and CO2:CH4 mole ratio—on the conversion rates and formation of reaction components, we aim to predict optimal conditions and corresponding process variables. Through a comparison of a three-layer Feed Forward Neural Network, optimized at a 3:10:1 topology, with traditional RSM approaches, our findings highlight the superior predictive capabilities of ANN models. Notably, ANN demonstrated exceptional performance with R2adj and F_Ratio values significantly surpass those of RSM, alongside lower statistical error terms. This superiority is attributed to ANN's robust handling of nonlinear relationships between inputs and outputs, asserting its potential for enhancing predictive accuracy in chemical process optimization. At optimum predicted conditions like 1 CH4/CO2,750 °C reaction temperature, 12000 cm3g−1h−1 space velocity, NiSrZrAl outperformed with &gt; 85 % CH4 and CO2 conversion with H2/CO ∼1 up to 20 h time on stream. Our research underscores the importance of integrating advanced modeling techniques for the efficient and accurate prediction of catalytic reactions, offering valuable insights for future applications in chemical engineering and catalysis.<br/