Methane dry reforming over Ni/fibrous SBA-15 catalysts: Effects of support morphology (rod-liked F-SBA-15 and dendritic DFSBA-15)

Tuning Textural Properties by Changing the Morphology of SBA-15 Mesoporous Materials

Changing the morphology is an excellent option for altering the textural parameters of SBA-15 materials. This study provides a guide on how the properties of mesoporous structures behave according to their morphology and their contribution to thermal stability. The objective of this work was to synthesize different morphologies (spherical, hexagonal prisms, rice-like grains, rods, and fibers) of SBA-15 materials and evaluate the existing textural changes. The materials were synthesized by varying the temperature of the synthesis gel from 25 °C to 55 °C, with stirring at 300 or 500 rpm. The results of X-ray diffraction, Fourier transform infrared spectroscopy, N2 adsorption and desorption, and scanning electron microscopy were evaluated. Thermal stability tests were also conducted in an inert atmosphere. The materials were successfully synthesized, and it was observed that they all exhibited different characteristics, such as their ordering, interplanar distance, mesoporous parameter, specific surface area, micropore and mesopore volumes, external mesoporous area, and wall thickness. They also presented different thermal stabilities. The rice grain morphology had the highest specific surface area (908.8 cm2/g) and the best thermal stability, while the rod morphology had the best pore diameter (7.7 nm) and microporous volume (0.078 cm3/g).

The Structure of Ordered Mesoporous Materials Synthesized from Aluminum Phyllosilicate Clay (Bentonite)

This paper reports the synthesis and structural analysis of mesoporous silica materials with the use of aluminum phyllosilicate clay (bentonite) as an alternative silica source. In the proposed synthesis, bentonite, as natural aluminosilicate, was used instead of commercially available and quite expensive tetraethyl orthosilicate (TEOS) silica source. The objective of the research study was to determine the effect of aluminum loading in the mesoporous silica body for ordering structure, porosity, and potential sorption capacity to thorium ions. The unique direction developed in this procedure is focused on preparing advanced materials from natural sources with their own desired functionality and general availability. The applied procedure based on the classic, one-step synthesis of SBA-15 silicates was modified by gradually increasing the bentonite amount with simultaneous reduction of the TEOS content. The structural and morphological characterization, as well as evaluation of the porous structure of the obtained materials, was performed using powder wide-angle X-ray diffraction (XRD), small-angle scattering (SAXS), transmission and scanning electron microscopy (TEM, SEM), low-temperature nitrogen adsorption–desorption methods and potentiometric titration. The new, cost-effective composites for the removal of Th(IV) ions are proposed. The synergistic effect of expanding the porous surface using bentonite as a silica precursor and the presence of thorium-binding groups (such as Al2O3) is indicated.

Fabrication of the Ordered Mesoporous nZVI/Zr-Ce-SBA-15 Composites Used for Crystal Violet Removal and Their Optimization Using RSM and ANN–PSO

Crystal violet (CV), a triphenylmethane dye, is widely used in the textile, printing, paper, leather, and cosmetics industries. However, due to its higher chemical stability and lower biodegradability, CV has teratogenic and carcinogenic toxic effects on animals and humans. Therefore, the objective of the present study was to investigate whether or not the as-prepared nZVI supported on an ordered mesoporous Zr-Ce-SBA-15 composite (nZVI/Zr-Ce-SBA-15) had more potential for CV removal from simulated wastewater in comparison with Zr-Ce-SBA-15. Meanwhile, the parameters of CV adsorption onto nZVI/Zr-Ce-SBA-15 composites were optimized by a response surface methodology (RSM) and an artificial neural network combined with particle swarm optimization (ANN–PSO). According to XRD, FTIR, SEM, and TEM, N2 adsorption, and thermogravimetric analyses, nZVI was supported successfully on Zr-Ce-SBA-15 composites, becoming an ordered mesoporous material. The results of RSM indicated that the order of the effects of the four parameters on CV removal was, successively, initial pH, contact time, temperature, and initial CV concentration. ANN–PSO was more suitable, in comparison to RSM, to optimize the experimental parameters for CV removal from simulated wastewater using ordered mesoporous nZVI/Zr-Ce-SBA-15 composites. The optimized removal rate of CV was 93.87% under an initial pH of 3.00, a contact time of 20.00 min, an initial CV concentration of 261.00 mg/L, and a temperature of 45. Pseudo-second-order kinetics can better describe the behavior of CV adsorption onto nZVI/Zr-Ce-SBA-15 composites. The process of CV adsorption onto Zr-Ce-SBA-15 composites was followed by the Langmuir model, and its maximum adsorption capacity was 105 mg/g in 213 K. It was indirectly confirmed that the maximum adsorption capacity of nZVI/Zr-Ce-SBA-15 exceeded this value because the removal efficiency of CV using nZVI/Zr-Ce-SBA-15 was obviously higher than that of using Zr-Ce-SBA-15. The thermodynamics results indicated that CV adsorption onto nZVI/Zr-Ce-SBA-15 was a spontaneous, endothermic, and entropy-driven process. The dissolution of Fe ions and light/dark experiments confirmed nZVI/Zr-Ce-SBA-15 was simultaneously of adsorption and catalysis in the process of CV removal. The effect of removal CV was still maintained in the first four experiments (removal rate > 78%), and our suggestion is that nZVI/Zr-Ce-SBA-15 is a potential adsorbent for CV remediation from wastewater compared to Zr-Ce-SBA-15 and other adsorbents.

Barium-Promoted Yttria-Zirconia-Supported Ni Catalyst for Hydrogen Production via the Dry Reforming of Methane: Role of Barium in the Phase Stabilization of Cubic ZrO2

Developing cost-effective nonprecious active metal-based catalysts for syngas (H₂/CO) production via the dry reforming of methane (DRM) for industrial applications has remained a challenge. Herein, we utilized a facile and scalable mechanochemical method to develop Ba-promoted (1-5 wt %) zirconia and yttria-zirconia-supported Ni-based DRM catalysts. BET surface area and porosity measurements, infrared, ultraviolet-visible, and Raman spectroscopy, transmission electron microscopy, and temperature-programmed cyclic (reduction-oxidation-reduction) experiments were performed to characterize and elucidate the catalytic performance of the synthesized materials. Among different catalysts tested, the inferior catalytic performance of 5Ni/Zr was attributed to the unstable monoclinic ZrO₂ support and weakly interacting NiO species whereas the 5Ni/YZr system performed better because of the stable cubic ZrO₂ phase and stronger metal-support interaction. It is established that the addition of Ba to the catalysts improves the oxygen-endowing capacity and stabilization of the cubic ZrO₂ and BaZrO₃ phases. Among the Ba-promoted catalysts, owing to the optimal active metal particle size and excess ionic CO₃ ^2- species, the 5Ni4Ba/YZr catalyst demonstrated a high, stable H₂ yield (i.e., 79% with a 0.94 H₂/CO ratio) for up to 7 h of time on stream. The 5Ni4Ba/YZr catalyst had the highest H₂ formation rate, 1.14 mol g^-1 h^-1 and lowest apparent activation energy, 20.07 kJ/mol, among all zirconia-supported Ni catalyst systems.

Molecular Dynamics and Machine Learning in Catalysts

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.