Canadian Journal of Chemical Engineering最新文献

Silica nanoparticles (SiNPs) are promising drug delivery nanocarriers due to their tunable size, porous structure, and surface properties. This study compares two synthesis methods: (i) a low-temperature aqueous sol–gel process yielding SiNPs of 500–700 nm, and (ii) a water-in-oil (W/O) microemulsion using either CTAB or TX-100 surfactants. Surfactant selection significantly affected nanoparticle size, stability, and dispersity. Characterization by dynamic light scattering (DLS), scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), solid-state nuclear magnetic resonance spectroscopy (ssNMR), X-ray photoelectron spectroscopy (XPS), and photoluminescence analysis (PL) confirmed successful synthesis. The TX-100-mediated microemulsion method proved particularly effective in achieving highly stable, reproducible, and monodisperse SiNPs, with a size limit of approximately 100 nm, making them ideal candidates for drug encapsulation. Procaine (PRC) incorporation demonstrated the role of reverse micelle dynamics and surfactant-stabilized interfaces in enhancing encapsulation efficiency. This work highlights the critical role of surfactant and medium selection in SiNPs synthesis, demonstrating their impact on nanoparticle stability, dispersity, and drug loading efficiency. The TX-100-mediated microemulsion technique emerges as a superior approach for producing stable, monodisperse SiNPs, advancing the design of nanocarriers for PRC drug delivery applications.

The phenomena of bubble formation from the venturi pipe microbubble generator were investigated. The air hole sizes were varied at 1, 0.5, and 0.3 mm while the volumetric flow rates were set at 4, 5, 6, 7, and 8 LPM. Insertion of a guitar string with a diameter of 0.2 mm into the 0.5 mm air hole size was also performed to examine its effect. The simulation of the bubble shrinkage process in water (sizes of 25, 50, 75, and 100 μm) was held with and without saturated nitrogen at 0.25, 0.5, 0.75, 0.875, and 1 atm. The experiment shows that average bubble sizes for air hole sizes of 1, 0.5, and 0.3 mm are 1.37, 1.40, and 1.09 mm, respectively. The flow rate alteration significantly affects the bubble sizes for air hole sizes of 0.5 and 0.3 mm but is not meaningful for 1 mm. In addition, the guitar string insertion also produces spherical bubbles with a size of 1.33 mm for deep depth location. Moreover, simulation results prove that smaller bubble sizes lead to prolonged shrinkage time and a greater mass transfer coefficient in the liquid phase with and without saturated nitrogen.

This study applies neural ordinary differential equations (neural ODEs) to model hydrocracking kinetics, a key process for converting heavy hydrocarbons into lighter products like gasoline and diesel. Neural ODEs provide a data-driven approach, learning reaction dynamics directly from data without requiring explicit assumptions on kinetics, addressing limitations in traditional methods. Two neural ODE models were trained on synthetic hydrocracking data representing different kinetic assumptions: one based on a 2.5-order reaction scheme (Model A) and the other on a first-order scheme (Model B), across varying temperatures and feedstocks. The models demonstrated high predictive accuracy when predicting within the range of training data, with RMSE values remaining below 0.5 wt.% under most conditions. However, performance declined during high-temperature extrapolation scenarios, particularly for the higher-order model, revealing challenges in capturing nonlinear dynamics at extreme conditions. This work also enhanced the interpretability of neural ODEs by analyzing gradients within the model, which validated alignment with known kinetic principles, uncovering critical information about reaction pathways and temperature sensitivities. This analysis demonstrated the models' ability to capture temperature-dependent behaviour and rate stabilization, as illustrated through heat maps, which further emphasized the potential of neural ODEs for both predictive accuracy and interpretative insights in hydrocracking modelling. Additionally, the extracted gradients present an exciting avenue for future advancements, such as leveraging symbolic regression techniques to uncover governing equations.

Ethyl cellulose (EC) is one of the important coating materials for the short-term protection of precious metals and jewellery. In this study, graphene oxide (GO) was introduced in EC films, and electrochemical impedance spectroscopy (EIS) was utilized to probe the ion permeation through the films. EIS results demonstrate the film resistance increased with the GO content from 0 to 1 wt.%, and decreased with the GO content from 1 to 4 wt.%. It indicates that the ion penetration first becomes difficult and then easier with the increase in GO content. SEM results reveal that the pore sizes of the doped EC composite films first decrease and then increase with the increase of GO content. Additionally, water contact angle results indicate that introducing GO enhanced the surface hydrophilicity of the EC composite film. The tensile test shows that the EC/GO composite film achieves the best mechanic property at the GO content of 1 wt.%. These findings indicate that the pore structure of EC could be adjust by GO platelet, which could be an easy method to modify the ion barrier through EC film.

The fluidization behaviour was investigated using a cold flow bubbling fluidized bed setup with a column of 8 cm inner diameter. Minimum fluidization velocities (U_mf) were experimentally determined for both mono-component (rice husk or coal) and binary mixtures of rice husk and coal, using air as the fluidizing medium. For the binary mixtures, U_mf,m was measured by varying the weight fraction and particle size of coal. It was observed that fluidization performance improved significantly with an increase in the coal weight fraction. Conversely, higher proportions of rice husk led to deteriorated fluidization behaviour due to its low bulk density and irregular particle shape. To predict the U_mf,m for specific mixtures, two empirical correlations were developed for rice husk weight fractions of 20% and 40%. These correlations showed good agreement with experimental results, with relative errors within 7%.

Silica nanoparticles (SiNPs) are promising drug delivery nanocarriers due to their tunable size, porous structure, and surface properties. This study compares two synthesis methods: (i) a low-temperature aqueous sol–gel process yielding SiNPs of 500–700 nm, and (ii) a water-in-oil (W/O) microemulsion using either CTAB or TX-100 surfactants. Surfactant selection significantly affected nanoparticle size, stability, and dispersity. Characterization by dynamic light scattering (DLS), scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), solid-state nuclear magnetic resonance spectroscopy (ssNMR), X-ray photoelectron spectroscopy (XPS), and photoluminescence analysis (PL) confirmed successful synthesis. The TX-100-mediated microemulsion method proved particularly effective in achieving highly stable, reproducible, and monodisperse SiNPs, with a size limit of approximately 100 nm, making them ideal candidates for drug encapsulation. Procaine (PRC) incorporation demonstrated the role of reverse micelle dynamics and surfactant-stabilized interfaces in enhancing encapsulation efficiency. This work highlights the critical role of surfactant and medium selection in SiNPs synthesis, demonstrating their impact on nanoparticle stability, dispersity, and drug loading efficiency. The TX-100-mediated microemulsion technique emerges as a superior approach for producing stable, monodisperse SiNPs, advancing the design of nanocarriers for PRC drug delivery applications.

This study presents a hydrogen transfer reaction of 1,2-butanediol (BDO) to nitrobenzene for the simultaneous production of 1-hydroxy butanone and aniline over Cu/SiO₂ catalysts. A series of Cu supported SiO₂ catalysts with Cu loading up to 25 wt.% were prepared by the wet impregnation method. The prepared catalysts were further characterized by various characterization techniques such as X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area, Fourier transform infrared (FTIR), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and temperature-programmed reduction (TPR). The transfer hydrogenation of nitrobenzene through the dehydrogenation of BDO was effectively accomplished rather than hydrogenation of nitrobenzene via external hydrogen due to well dispersed copper nanoparticle on the surface of SiO₂. The present strategy enables the production of two industrially important chemicals in a single step with stoichiometric amount of hydrogen source. Among the series of catalysts, 20 wt.% Cu/SiO₂ catalyst exhibited excellent catalytic performance (89% conversion of BDO and 85% conversion of nitrobenzene). The catalyst also shows very good stability for 9 h during the time-on-stream.

To mitigate global warming, it is crucial to reduce CO₂ emissions from the combustion of fossil fuels. CO₂ separation plays a key role in this process. Deep eutectic solvents (DESs) have demonstrated significant advantages and are considered promising liquid absorbents. In this study, DESs were chosen as absorbents for CO₂ separation from various CO₂ streams, including flue gas, lime kiln gas, bio-syngas, and biogas by thermodynamic analysis. Based on the criteria of the amount of absorbents required and energy use, several DESs including N,N-dimethylethanolammonium chloride/urea (1:1), choline chloride/urea (2:3), choline chloride/urea (2:5) were selected for the four CO₂ streams. Meanwhile, choline chloride/lactic acid (1:5) was selected for flue gas, tetrabutylammonium bromide/lactic acid (1:3) was selected for lime kiln gas, and N,N-dimethylethanolammonium chloride/urea (1:1) was selected for bio-syngas and biogas. It is revealed that the selected DESs exhibit a lower amount of absorbents required and lower energy use than those of other DESs and their aqueous solutions. The relationship among the absorption pressure, the energy use, the physical properties, and the critical properties of DESs are established for the four CO₂ streams. It is shown that the absorption pressure of the DESs can be fitted with the physical properties, which are density and heat capacity, with average relative deviations lower than 5%, while the energy use can be fitted with the critical properties, which are critical temperature and critical pressure, with average relative deviations below 1%. It suggests that the selected DESs have potential for further applications.

Two-phase flow pattern prediction is essential in predicting liquid holdup, pressure gradient, and flow assurance risks in various applications in the chemical, nuclear, and petroleum industries. Recent studies of flow pattern model evaluation in high liquid viscosity two-phase flow in vertical upward pipe flow revealed discrepancies in all transition boundaries, specifically the intermittent (IN)/annular (AN) flow transition. Therefore, this study aims to investigate the effect of liquid viscosity on IN/AN flow pattern transition and to improve the existing models. Specifically, Taitel et al.'s IN/AN transition model is improved by incorporating the liquid viscosity effect on liquid droplet fallback and liquid film thickness. Furthermore, sensitivity analyses on Barnea's IN/AN flow pattern transition model revealed that the interfacial friction factor (f_i) and liquid entrainment (f_E) closure relationships are crucial in the film bridging and film instability mechanisms of the flow transition. Therefore, a comprehensive evaluation of the performance of the f_i and f_E closure relationships is carried out, revealing that Pan and Hanratty's f_E correlation and Ishii and Grolmes' f_i correlation is the best combination with the least prediction error over a wide range of liquid viscosity. A validation study against an extensive experimental high liquid viscosity flow pattern database with liquid viscosity ranging from 4 to 1600 mPa·s showed high prediction performance for the proposed improved Taitel et al. and Barnea IN/AN flow pattern transition models.