ACS Engineering Au最新文献

The design and use of superhydrophobic surfaces have gained special attentions due to their superior performances and advantages in many flow systems, e.g., in achieving specific goals including drag reduction and flow/droplet handling and manipulation. In this work, we conduct a brief review of shear flows over superhydrophobic surfaces, covering the classic and recent studies/trends for both Newtonian and non-Newtonian fluids. The aim is to mainly review the relevant mathematical and numerical modeling approaches developed during the past 20 years. Considering the wide ranges of applications of superhydrophobic surfaces in Newtonian fluid flows, we attempt to show how the developed studies for the Newtonian shear flows over superhydrophobic surfaces have been evolved, through highlighting the major breakthroughs. Despite the fact that, in many practical applications, flows over superhydrophobic surfaces may show complex non-Newtonian rheology, interactions between the non-Newtonian rheology and superhydrophobicity have not yet been well understood. Therefore, in this Review, we also highlight emerging recent studies addressing the shear flows of shear-thinning and yield stress fluids in superhydrophobic channels. We focus on reviewing the models developed to handle the intricate interaction between the formed liquid/air interface on superhydrophobic surfaces and the overlying flow. Such an intricate interaction will be more complex when the overlying flow shows nonlinear non-Newtonian rheology. We conclude that, although our understanding on the Newtonian shear flows over superhydrophobic surfaces has been well expanded via analyzing various aspects of such flows, the non-Newtonian counterpart is in its early stages. This could be associated with either the early applications mainly concerning Newtonian fluids or new complexities added to an already complex problem by the nonlinear non-Newtonian rheology. Finally, we discuss the possible directions for development of models that can address complex non-Newtonian shear flows over superhydrophobic surfaces.

Semiconductor-bacterial hybrid systems have been shown to be effective for photochemical conversion. The combination of two systems delineates the light absorption from the catalytic ability, wherein a semiconductor absorbs light, generating an electron–hole pair, followed by the transfer of photogenerated charges to catalytically active bacteria that assume the roles of carrying out redox reactions. The halide perovskite materials possess excellent optoelectronic properties and, if they exhibit biocompatibility with microorganisms, shall provide an opportunity to carry out environmentally important redox reactions including carbon dioxide conversion to value added products. In this work, we report the biocompatibility of panchromatic visible light absorption and stable vacancy ordered halide perovskite (VOP), Cs₂PtX₆ (X = halide) with Shewanella oneidensis MR-1 nonphotosynthetic bacterium. This microbe is shown to grow in culture media containing VOP, and the growth rate is found to be unaffected by the presence of semiconductor media. Although Shewanella oneidensis MR-1 is a well-known metal-reducing bacteria, in this work, we find that the vacancy ordered perovskite materials remain intact with this bacterium. With constraint-based metabolic modeling, we report that this biohybrid system shall potentially be used for solar energy conversion of water and carbon dioxide to hydrogen and formate, respectively.

The design of the reaction conditions is essential for controlling polymerization to synthesize polymers with desired properties. However, the experimental screening of the reaction conditions can be time-consuming and costly. Computational methods such as kinetic Monte Carlo (KMC) simulations have been developed to assist with the design of experiments. Nevertheless, when the dimensions of the reaction conditions to be explored are large, the simulation models might still not be able to meet the demand for efficient screening and design. Active learning can be used to tackle this problem by designing data acquisition strategies that can minimize the labeling required to construct a good surrogate model in the design space. In this work, we combined a cluster-maximum model change (CMMC) model with KMC simulations, which can precisely design polymerization conditions at the lowest computational cost for the desired molecular weight distributions. The case study results show that the CMMC model only uses 50 KMC simulations to construct a predictive model with a 10% relative error for a system with 4 design parameters, which greatly reduces the computational cost while maintaining accuracy.

A combined experimental and molecular modeling study identifies a family of spinel oxides that in combination with PGM (platinum group metals) provide enhanced methane oxidation activity. With a reduction in greenhouse gas (GHG) emissions urgently needed, there is renewed interest in the use of natural gas vehicles (NGVs) and engines (NGEs) for transportation, commerce, and industrial applications. NGVs and NGEs emit less CO₂ than their petroleum-derived counterparts but may emit uncombusted methane, an even more potent GHG. For stoichiometric engines, methane oxidation catalysts containing PGM and spinel oxide in layered architectures offer increased methane oxidation activity and lower light-off temperatures (T₅₀). The reducible spinel oxide has direct and indirect roles that are effectively described by the bulk oxygen vacancy formation energy (E_vac). We apply density functional theory (DFT) to identify several earth-abundant, cobalt-rich spinel oxides with favorable E_vac, shown to correlate with dynamic oxygen storage capacity (DOSC) and CO and H₂ oxidation activity. We experimentally rank-order the DFT-identified spinel oxides in combination with Pt+Pd for their methane oxidation activity measurements, under both time-invariant and modulated feed conditions. We show good agreement between the activity and the DFT-computed reducibility of the spinel oxide. The findings suggest spinel reducibility is a key factor in achieving enhanced low-temperature methane conversion, enabled through a balance of methane activation on the PGM sites and subsequent oxidation of the intermediates and byproducts on spinel oxides. In agreement with its computationally predicted E_vac, NiCo₂O₄ was confirmed to have the highest DOSC and lowest T₅₀ among the tested spinel samples.

Traditional pharmaceutical manufacturing processes for solid oral dosage forms can be inefficient and have been known to produce a large amount of undesired product. With the progressing trend of achieving carbon neutrality, there is an impetus to increase the energy efficiency of these manufacturing processes while maintaining the critical quality attributes of the product. One of the important steps in downstream pharmaceutical manufacturing is wet granulation, and within that, twin screw granulation (TSG) is a popular continuous manufacturing technique. In this study, the energy efficiency of the TSG process was maximized by combining a long-term memory (LSTM) model with an optimization algorithm. The LSTM model was trained on time-series process data obtained from the TSG experimental runs. The optimization process, with the objective of maximizing energy efficiency, was performed using a stochastic optimization algorithm, and constraints were enforced on the process parameter design space. Experimental runs at the optimal process parameters were conducted on the TSG equipment with updates occurring at predefined intervals depending on the optimization scenarios. The purpose of these experimental runs was to validate the capability of increasing the overall process energy efficiency when operating at the optimized process parameters. A maximum increase of 27% was obtained between two tested optimization scenarios while maintaining the yield of the granules at the end of the twin-screw granulation process.

The present study investigates the potential for scaling up 3D-printed porous reactors at the millimeter scale by integrating different reactor configurations in series. These reactor configurations, ranging from a single reactor (N = 1) to six reactors in series (N = 6), were evaluated for their performance in terms of axial dispersion in a gas–liquid system, with a focus on identifying potential dead zones. The scaled-up reactor systems exhibited a reduced deviation from plug flow behavior, mainly attributed to improved radial mixing maintained throughout the entire length of the porous structures. Among the various configurations tested, the scaled-up system featuring six reactors displayed the highest coefficient of variation (CoV) at approximately 24% for residence times exceeding 100 s. In all cases, the presence of stagnant zones influenced the shape of the residence time distribution (RTD) curves, although in the scaled-up system these stagnant zones did not significantly impact the overall performance or the yield of 2,5-diformylfuran (DFF). This was due to the narrow RTD and effective mass transfer between the stagnant and active flow compartments. Notably, the selectivity remained at 100%, and the highest yield of DFF (approximately 81%) was achieved for a residence time of 6.61 min in the scaled-up system. Despite introducing mass transfer limitations when operating at the millimeter scale, the scaled-up system achieved DFF productivity levels comparable to microreaction systems at significantly lower energy dissipation.

Hydrogen-based shaft furnace technology holds promise for low-carbon hydrogen metallurgy. Since hydrogen-assisted iron ore reduction is highly endothermic, inadequate heat supply relevant to the contact of gas and densely packed ores may reduce the rate and efficiency of reductions. The key to addressing this issue lies in understanding the competition among heat supply, heat transfer, and heat loss driven by the gas flow around ores and reactions within them. Modeling and simulation are crucial for revealing the underlying mechanisms and promoting process scale-up and intensification. This review summarizes previous efforts in physical modeling and model applications for improving the reduction performance. The discrete element method (DEM) and computational fluid dynamics (CFD)–DEM models have been used for particle-scale simulation to investigate inhomogeneous particle descent and relevant particle–particle interactions. For macroscale simulations, steady-state simplified models such as plug flow and REDUCTOR, as well as the Eulerian two-phase model, have been developed by considering heat and mass transfer. Based on these model applications, strategies including the optimization of operating conditions and gas-feeding methods have been proposed to improve the furnace performance. Further numerical efforts are needed to analyze the in-furnace heat evolution and reduction and reveal the competitiveness of flow, transport, and reaction by incorporating multiscale physics in shaft furnaces. Additionally, attention could be paid to the effects of particle sticking and degradation on reduction, which may be more serious when the proportion of lump ores increases. When evaluating relative optimization strategies, comprehensive comparisons are expected in terms of iron ore reduction degree, gas utilization rate, energy consumption, and economic feasibility under various reducing and cooling gas operating conditions and furnace profiles to offer practical guidelines for industrial design and intensification.

The gas-to-liquid (GTL) process is a promising technology for converting natural gas into synthetic fuels and chemicals. However, its high carbon dioxide (CO₂) emissions present significant challenges. Methane reforming contributes up to 60% of GTL’s CO₂ emissions, necessitating decarbonization. Dry reforming of methane (DRM) shows potential for CO₂ conversion. Still, it faces challenges such as high energy requirements, catalyst deactivation, and an incompatible hydrogen-to-carbon monoxide (H₂/CO) ratio for GTL processing, requiring extensive research. A previous study proposed a two-reactor system known as CARGEN that co-produces solid carbon (in the form of multiwalled carbon nanotubes [MWCNTs]) and syngas, reducing CO₂ emissions by 40% compared to the benchmark autothermal reforming (ATR) process through life cycle assessment (LCA) studies. This paper presents a comprehensive simulation of the advanced DRM process used to retrofit an existing ATR-based GTL plant─initially, a 50,000 bbl./day ATR-based GTL plant is simulated. The advanced reformer process replaces ATR through retrofitting. Comparative analysis shows a remarkable 73% reduction in net CO₂ emissions and the potential coproduction of 243 kg of MWCNTs per barrel of syncrude, equivalent to 12,150 tons/day of MWCNTs. However, the advanced reformer-based GTL plant requires 61% more natural gas feedstock while utilizing 79% less oxygen than the ATR-based plant. Furthermore, a separate techno-economic analysis examines the advanced reformer-based GTL plant based on a calculation for 13,100 tons/day of CO₂ feedstock to co-produce 3,277 tons/day of MWCNTs and 50,000 barrels/day of syncrude. This analysis, considering a 25% tax rate, 25-year plant life, and zero salvage value, demonstrates an attractive 10-year payback period at selling prices of 80 USD/bbl. for syncrude and 10 USD/kg for MWCNTs. These results provide a process system-level perspective, showcasing the advanced reformer-based GTL plant (CARGEN Process) as an effective solution for low-carbon GTL production.

Large-scale endothermic chemical processes, as currently practiced, employ tubular reactors that are heated externally through the combustion of fossil fuels, and are highly carbon-intensive. Joule-heated reactors in which electric currents flowing through the catalyst are used to provide thermal energy directly through internal heating are rapidly emerging as an alternative to these conventional, externally heated reactors. Joule-heated reactors could help significantly improve modularity and also reduce the capital, energy, and carbon footprint associated with these enthalpically demanding processes. Development of these novel types of reactors, however, is predicated on overcoming catalyst design challenges encountered uniquely when supplying heat through the use of electric currents passing through catalyst substrates. We review herein some key advancements in catalyst design that have been achieved in the recent past, and highlight considerations critical to the novel mode of reactor operation proposed. We provide an overview of the various types of electrically heated catalysts proposed, methods used in their synthesis, and reactor performance of Joule-heated catalysts for a variety of applications. Also discussed are key knowledge gaps that will likely need to be addressed in an effort to accelerate deployment of this emerging class of reactors that could play a pivotal role in the decarbonization of energy-intensive large-scale chemical processes.

The cyanidation of gold ores with copper content is frequent in gold mines. Copper affects the performance and profits of mineral processing. The current technology for gold recovery from cyanide solutions usually involves the adsorption of the gold-cyanide complex ion on activated carbon; however, the copper affects this process. The process of electrocoagulation (EC) is a promising technique for gold and silver recovery with copper, where all of the metals can be recovered. This work used the electrocoagulation process (EC) to evaluate the metal recovery from a pregnant leach solution (PLS), where EC is a promising technique. This study aimed to determine the optimal parameter to recover the gold and silver and to see the effect of copper concentration in the PLS obtained by simultaneous pressure leaching/oxidation of a gold-bearing pyritic concentrate. EC tests were run to recover gold and silver over copper from PLS using aluminum electrodes and variables like distances between electrodes, pH, potential applied, and feeding flow for continuous EC. The chemical assay of cyanide leachates shows a concentration of 7.15 mg/L of gold, 305 mg/L of silver, and 351.5 mg/L of copper with 1810 mg/L of free cyanide. The results showed that the EC process recovered 99% of gold and copper and 92% of silver at a pH of 11, 8 mm of dE, and a potential applied 3 V in 10 min. However, under this condition in continuous EC with a flow rate of 40 mL/min, the recovery is 66.3% of gold, 85.8% of silver, and 45.3% of copper; compared with the batch process, the gold and silver decrease.