Enhancing preferential oxidation of CO in H2 on Au/α-Fe2O3 catalyst via combination with APTES/SBA-15 CO2-sorbent

Au/α-Fe₂O₃ was combined with a CO₂-sorbent (3-aminopropyltriethoxysilane (APTES) grafted on SBA-15 and hereafter denoted as APTES/SBA-15) to enhance preferential oxidation (PROX) of CO in H₂. The CO₂ molecules could be rapidly adsorbed on APTES/SBA-15 at low temperatures below 50 °C with a capacity of 0.68 mmol CO₂/g-sample, and desorbed at a temperature range of 50 °C–80 °C. Three different configurations of the Au/α-Fe₂O₃ catalyst and the CO₂-sorbent were tested in the PROX reaction, namely (i) the sorbent-free (catalyst//SBA-15//catalyst) configuration, (ii) the packed three-layer configuration (catalyst//CO₂-sorbent//catalyst), and (iii) the mechanically mixed catalyst and CO₂-sorbent configuration. Compared to configuration (i), configuration (ii) achieved an average 10% higher CO conversion at 50 °C and a GHSV of 65000 h⁻¹. However, the CO concentration could not be lowered to below 70 ppm from 2000 ppm using configuration (ii) at a GHSV of 10000 h⁻¹. Thus, a 5-layer configuration (catalyst//CO₂-sorbent//catalyst//CO₂-sorbent//catalyst) was used, and the CO concentration was lowered to ca. 25 ppm. The mechanism for enhancement of the PROX reaction by the continuous removal of CO₂ by the CO₂-sorbent is discussed and attributed to reduction of the surface carbonate on the Au/α-Fe₂O₃ catalyst formed during the PROX process.     

Preparation and improvement of nickel catalyst supported ordered mesoporous spherical silica for thermocatalytic decomposition of methane

The thermocatalytic alteration of CH₄ into highly pure hydrogen and filaments of carbon was investigated on a series of Ni-catalysts with various contents (25, 40, 55, and 70 wt%) supported mesoporous spherical SiO₂. The silica with ordered structure and high specific surface area (1136 m²/g) was synthesized using the Stöber technique with TEOS as a silica precursor and CTAB as the template in a simple synthesis system of aqueous-phase. This technique led to the preparation of mesoporous spherical silica. The prepared samples were characterized using BET, TPR, XRD, TPO, and SEM analyses. The prepared catalysts with different nickel loading showed the BET surface area ranging from 225.0 to 725.7 m²/g. These results indicated that an increase in nickel content decreases the surface area and leads to a subsequent collapse of a pore structure. SEM analysis confirmed a spherical nanostructure of catalysts and revealed that with the increase in loading of Ni, the particle size enlarged, because of the agglomeration of the particles. The results implied that the high methane conversion of 54% obtained over the 55 wt% Ni/SiO₂ at 575 °C and this sample had higher stability at lower reaction temperature than the other prepared catalysts, slowly deactivation was observed for this catalyst at a period of 300 min of time on stream.     

Nickel nanoparticles derived from the direct thermal reduction of Ni-containing Ca–Al layered double hydroxides for hydrogen generation via ammonia decomposition

The fabrication of nanomaterials is a crucial issue in heterogeneous catalysis. Herein, nickel nanoparticles of different particle sizes were derived from the direct thermal reduction of a Ni-containing Ca–Al layered double hydroxide (LDH). Using various characterization methods, such as XRD, TEM, SEM, and TPR-MS, it was found that nickel precursors notably influenced the final morphology of Ni metallic nanoparticles. Ni(NO₃)₂ can attend reassembling of reassemble CaAl–NO₃⁻ LDH in water and facilitate the dispersion of Ni in the lamellar structure of the LDH, resulting in the formation of uniform and small Ni metallic nanoparticles (ca.4.7 nm) on CaAlO_x via the rapid decomposition of Ni(NO₃)₂ in the reduction atmosphere. However, large Ni nanoparticles (ca.14.3 nm) were formed on the CaAlO_x catalyst when NiCl₂ was used as the precursor and Cl-remained on the final catalyst. The kinetic study and NH₃-TPSR results further indicate that smaller Ni metallic nanoparticles enclosed by CaAlO_x exhibited stronger interactions with adsorbed NH₃ and facilitated the recombinative desorption of N and H atoms from the catalyst surface. Ni/CaAlO_x (NO₃⁻)-R exhibited the highest activity among the investigated samples.     

Comparisons of Pt catalysts supported on active carbon,carbon molecular sieve,carbon nanotubes and graphite for HI decomposition at different temperature

A series of Pt catalysts supported on activated carbon (AC), carbon molecular sieve (CMS), carbon nanotubes (CNT) and graphite (GR) were prepared by the impregnation method. Their catalytic performances in HI decomposition were evaluated in a fixed bed reactor at temperatures ranging from 400 to 550 °C under atmospheric pressure. The different Pt catalysts before and after HI decomposition at different temperature were characterized by BET, XRD and TEM, respectively. The results of the activity evaluation indicated that the activity order of different Pt catalysts changed significantly with the variation of reaction temperature. At 400 °C, different supported Pt catalysts activities decreased in order of Pt/CMS > Pt/AC > Pt/CNT > Pt/GR. At 450 °C, the activities of different Pt catalysts followed the order of Pt/AC ≈ Pt/CNT > Pt/CMS > Pt/GR. At 500 and 550 °C, the Pt/CNT showed the optimum activity and stability during HI decomposition, which could be attributed to the high dispersion of Pt particles and the special microstructure of CNT. The XRD and TEM results illustrated that the Pt particle size or Pt dispersion in different supported Pt catalysts showed different sensitivity to the reaction temperature.     

Bismuth effect on the Co–Mn/Ce0.85Zr0.15O2 nanoparticulate for CO preferential oxidation in simulated syngas

The novel and efficient bismuth modified supported Co–Mn catalysts were prepared and employed to catalyze the preferential oxidation of CO (CO PROX) in simulated syngas. The effects of introducing-methods and loadings of bismuth on both catalytic performance and catalyst nature were investigated. The N₂ adsorption/desorption measurement, X-ray diffraction (XRD) and H₂-temperature programmed reduction (H₂-TPR), and O₂-TPD (O₂-temperature programmed desorption) characterization techniques were performed to reveal the relationship between the catalytic properties and the nature of the catalysts. Results demonstrate that the as-prepared Bi modified supported Co–Mn catalyst exhibits excellent catalytic performance, depending on the introducing method and loadings of Bi. The enhancement of Bi addition into supported Co–Mn catalyst in the catalytic performance for CO PROX reaction is mainly ascribed to the dramatically improved reducibility of the Bi modified sample. Moreover, the decrease in hydrogen transformation over the Bi modified samples can be observed, suggesting the introduction of Bi can compress the catalytic activity for hydrogen oxidation. This study definitely demonstrates the existence of synergistic effect between the added bismuth and Co–Mn/Ce_0.85Zr_0.15O₂ in the Bi modified supported Co–Mn catalyst for CO PROX reaction. The developed Co₃O₄–MnO_x/Ce_0.85Zr_0.15O₂–Bi₂O₃ catalyst with bismuth content of 4.2 wt.% presents the outstanding catalytic activity, selectivity, and durability for CO PROX reaction in the simulated syngas, and it can be considered as a promising candidate for highly efficient CO elimination from H₂-rich stream.     

Catalysts for Direct Methanol Fuel Cells

The activity of in house prepared carbon-supported Pt-Ru catalysts for methanol oxidation and carbon-supported RuSe for the oxygen reduction reaction in direct methanol fuel cells (DMFCs) was investigated. The composition of Pt-Ru/C was varied both in terms of weight loading (ratio of total metal content to carbon) as well as the ratio of Pt to Ru. The measurements were carried out in a half cell arrangement in sulphuric acid at various temperatures. The weight loading and ratio of Pt to Ru were varied in order to find out the optimum weight loading of precious metal and the temperature dependence of Pt to Ru ratio on methanol oxidation reaction. It has been found that there exists an optimum in the weight loading at 60 wt.% for carbon-supported Pt-Ru catalyst towards its maximum mass activity. While 1:1 Pt to Ru ratio exhibits a higher activity than 3:2 Pt:Ru above 60 °C, 3:2 ratio exhibits a higher activity at lower temperature. It has been observed that RuSe is inactive towards methanol and it is realised that RuSe is a potential candidate as methanol tolerant oxygen reduction catalyst. The activity of carbon supported RuSe for oxygen reduction reaction (ORR) was tested in sulphuric acid in the presence of methanol. Even though the mass specific activity of the RuSe catalyst is somewhat lower than that of Pt/C, the surface activity of carbon-supported RuSe is superior than that of carbon supported Pt which indicate the unfavourable size distribution of RuSe/C catalyst.     

Energy consumption mode identification and monitoring method of process industry system under unstable working conditions

Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.