Electrochemical synthesis of green oxidants O3 and H2O2 is valuable for applications, but challenges persist in enhancing the O3 and H2O2 generation activity and combined application. Herein, we modulate the surface Ni active sites and oxygen vacancy defects content in Ni-Sb-SnO2 electrocatalysts to enhance selectivity for electrochemical ozone generation (EOP) and two-electron electrochemical oxygen reduction reactions (2e⁻ ORR). The Ni active sites and oxygen vacancy defects enriched electrocatalysts resulting in an ozone faradaic efficiency of 48.1%, while non-enriched electrocatalyst obtained 90% selectivity for H2O2. Theoretical calculations revealed that Ni-Sb-SnO2 efficiently captures O2 with defective Ovac2 stabilize intermediates, facilitating O3 and H2O2 synthesis. Moreover, concerted EOP and 2e⁻ ORR enable concurrent generation of O3 and H2O2 for efficient synergistic degradation of organic pollutants, while attenuating the energy demands of the electrolyzer. This study provides an appealing strategy for the simultaneous production of O3 and H2O2 with applications in wastewater treatment.
Despite offering remarkable advantages as solvents, double salt ionic liquids (DSILs) have been scarcely studied for extractive dearomatization from hydrocarbons as well as many other applications, thus urging a theoretical guidance method. In this work, a systematic framework combining the rational screening-validation and mechanistic analysis is proposed for tailoring DSILs for the o-xylene/n-octane separation. From an initial pool of commercially available ionic liquids (ILs), key thermodynamic properties of paired DSILs are predicted by COSMO-RS while their important physical properties are estimated from those of corresponding parent ILs (retrieved from experimental database or predicted by a deep learning model). Promising DSILs are tested by liquid-liquid equilibrium experiments, wherein the ion ratio-effect is also evaluated. The mechanism underlying the tunability of DSIL thermodynamic properties is disclosed by means of quantum chemistry calculation and molecular dynamics simulation. This work can be a valuable reference for guiding the design of DSILs for diverse applications.
Various examples show that stratified columns can improve the transport performance of particle packings. However, to date, there is no universal approach to design these packings to yield optimal performance. This study proposes a novel model-based method for designing particle packings in which mass transfer occurs between a liquid phase and a stationary phase using optimal control theory. The primary objective is to provide a general design strategy that is applicable across different unit operations in chemical, pharmaceutical, and food applications. Optimal control is utilized to determine the optimal particle diameter as a function of the axial position within the column. We demonstrate the approach using two case studies and three different optimization criteria. Numerical results indicate that the proposed method is highly effective, e.g., the solvent demand is reduced by up to 32.47 %. Moreover, the optimally graded packing yields a significantly sharper breakthrough curve of an adsorption column.
The semi-resolved CFD-DEM method has emerged as a prominent tool for modelling particle-fluid interactions in granular materials with high particle size ratios. However, challenges arise from conflicting requirements regarding the CFD grid size, which must adequately resolve fluid flow in the pore space while maintaining a physically meaningful porosity field. This study addresses these challenges by introducing a two-grid mapping approach. Initially, the porosity field associated with fine particles is estimated using a coarse CFD grid, which is then mapped to a dynamically refined grid. To ensure conservation of total solid volume, a volume compensation procedure is implemented. The proposed method has been rigorously validated using benchmark cases, showing its high computational efficiency and accurate handling of complex porosity calculations near the surface of coarse particles. Moreover, the previously unreported impact of the empirical drag correlation on fluid-particle force calculations for both coarse and fine particles has been revealed.
Carbanion-based ionic liquids are proposed and utilized as the key components for the construction of five super-nucleophilic deep eutectic solvents (SNDESs) in the paper. The super-nucleophilic nature of carbanion-based ILs is found to enable the capture of CO2 with large absorption capacity. However, the absorption is very slow in the IL due to high viscosity. The synergy of carbanion siting and hydrogen bonding is found to enable high and fast absorption of CO2 in [N2222][CH(CN)2]-ethylimidazole (Eim), and a synergistic absorption mechanism is proposed and validated from spectroscopic analyses and quantum calculations. The enthalpy change of CO2 absorption in [N2222][CH(CN)2]-Eim is calculated to be -39.6 kJ/mol according to the thermodynamic model, and the moderate value implies that both absorption and desorption of CO2 in the DES are favored and well balanced. The synergism of carbanion and hydrogen bond mediated by SNDESs provides a novel insight into the efficient CO2 capture.
In this work, we demonstrate plasma-catalytic synthesis of hydrogen and acrylonitrile (AN) from CH4 and N2. The process involves two steps: 1) plasma synthesis of C2H2 and HCN in a nominally 1:1 stoichiometric ratio with high yield up to 90% and high methane conversion > 90%; and 2) downstream thermocatalytic reaction of these intermediates to make AN. The effect of process parameters on product distributions and specific energy requirements are reported. If the catalytic conversion of C2H2 and HCN in the downstream thermocatalytic step to AN were perfect, which will require further improvements in the thermocatalytic reactor, then at the maximum output of our 1 kW radiofrequency 13.56 MHz transformer, a specific energy requirement of 73 kWh kgAN-1was determined. The expectation is that scaling up the process to higher throughputs would result in decreases in specific energy requirement into the predicted economically viable range less than 10 kWh kgAN-1.
The development of the OH- sieving membranes is of extraordinary importance but challenging for treating alkaline effluents. Here, we propose the ionization engineering concept of two-dimensional (2D) laminated membranes to pursue promising OH- separation. This concept is exemplified via stacking the self-designed sulfonated graphene oxide (SGO) nanosheets to fabricate 2D membranes. The SGO membranes achieve synergetic improvements of OH- dialysis coefficients and separation factors towards the simulated NaOH/Na2WO4 alkaline wastewater in sharp contrast to the 2D GO membranes and the commonly adopted polymeric cation exchange membranes. Besides, the separation factor of the SGO membranes is far higher than most the existing alkali recovery membranes. The molecular dynamics simulation results hint the high-efficiency OH- separation originates from the 2D SGO confined channels, where the dehydration effects and intensified electrostatic repulsion jointly play critical roles. This study offers an alternative strategy to achieve fast OH- sieving by ionizing 2D confined channels.
The fragmentation/adhesion behavior of nanoparticle agglomerate collision, which is challenging to model, is a crucial factor affecting fluidization. In this study, a discrete-finite element method (FDEM) based cohesive crack model is developed to simulate normal collisions between a complex agglomerate (around 1 mm) and a wall. In the FDEM model, the complex agglomerate is built from primary agglomerates (around a few micrometers), whose adhesive force and Young’s modulus are measured by an atomic force microscope (AFM). Simulation results agree well with the collision experiments. Furthermore, the effects of adhesive force, solid holdup and Young’s modulus on fragmentation behavior are explored, and a two-parameter Weibull function is found to fit well with the fragment distribution. The current FDEM model provides a link between the collision behavior and agglomerate properties. The detailed fragmentation/adhesion information can be useful for developing a macro model for agglomerate collision in the future.
It is in urgent requirement to directly measure the droplet coalescence frequency for the application of the population balance model. In this study, a method was firstly developed to directly measure the droplet coalescence frequency in turbulent flow field by using a specially designed mixing tank and a high-speed camera. The effects of the rotating speed and holdup fraction on the droplet coalescence frequency was quantitatively investigated. The increasing of rotation speed promotes first and then inhibits the coalescence, while the holdup fraction has little influence on the coalescence frequency function. The droplet collision frequency was also counted and the coalescence efficiency was calculated. The models in literature were tested with our experimental data and were found failing to predict the coalescence frequency in the stirring tank. Empirical correlations were finally proposed and good agreement was found between the prediction results and the experimental data.
This study explores the aerobic Baeyer-Villiger oxidation of cyclohexanone into ε-caprolactone using metalloporphyrin and benzaldehyde, a greener process to replace hazardous concentrated peroxyacid. The reaction mechanism involves a series of free radical reactions, identified through in-situ EPR. In this complex three-component reaction, we developed an intrinsic kinetic model based on the proposed mechanism. Utilizing a hyperbolic equation, the model well fits experimental data, describing biomimetic catalytic behavior of the aerobic Baeyer-Villiger oxidation. The reaction orders for the three reactants corroborate the kinetic model, with the activation energy of oxygen (130.27 kJ/mol) surpassing cyclohexanone (94.85 kJ/mol) and benzaldehyde (40.73 kJ/mol), implying slow initial oxygen activation while rapid subsequent benzaldehyde oxidation, making oxygen transfer and activation key steps. This unified approach to elementary reaction, mechanism, and intrinsic kinetics provides robust forecasts and lays the groundwork for additional studies, such as side reactions control and mass transfer enhancement and reactor design.
Inaccurate models limit the performance of model-based real-time optimization (RTO) and even cause system instability. Therefore, a RTO framework that can guarantee global convergence with the presence of plant-model mismatch is desired. In this regard, the trust-region framework is simple to implement and guarantees globally convergent for unconstrained problems. However, it remains to be seen if the trust-region strategy can handle inequality constraints directly with the common model adaptation method. This paper addresses this issue and proposes a novel composite-step trust-region framework that guarantees global convergence for constrained RTO problems. The trial step is decomposed into a normal step that improves feasibility and a tangential step that reduces the cost function. In each iteration, the model optimization problem with relaxed constraints is solved. The proof of the global convergence property under structural plant-model mismatch is given.
Hydrogel-based microfluidics offer an in vivo-relevant micro-environments for construction of organs-on-chips. However, the fabrication of heterogeneous microchannels using hydrogels is challenging and fails to mimic the complex structures of organs in vivo. Here we present a new methodology called “layer-by-layer adhesion” for the construction of complex microfluidic chips. A hydrosoluble and photo-crosslinkable adhesive, chitosan methacryloyl (CS-MA), was used to stitch various hydrogels together layer-by-layer to form perfusable microchannels. Our results show that CS-MA can bond different types of hydrogels with adhesion energy ranging from 1.2-140 N/m. Using the layer-by-layer adhesion approach, we constructed heterogeneous hydrogel-based microchannels with various morphologies of snail, spiral, vascular-like, and bilayer. Based on this methodology, liver-on-a-chip was established by entrapping hepatic cells inside a biocompatible Gel-MA layer and covering it with the perfusable microchannels in tough F127-DA layer. The “layer-by-layer adhesion” provides a facile and cytocompatible approach for engineering user-defined hydrogel-based chips potentially for organs-on-chips.
Biomass-derived deep eutectic solvents (DESs) have been introduced as promising pretreatment and fractionation solvents because of their mild processing conditions, easy synthesis, and green solvent components from biomass. In recent DES studies, solvent-based third constituents like water, ethanol, and others improve the processibility of typical binary DESs. However, the impacts of these components are not well understood. Here, two solvent-based constitutions, including water and ethylene glycol, were applied to 3,4-dihydroxybenzoic acid (DHBA)-based DES system for improving the conversion efficiency of cellulose-rich fraction and the properties of lignin fraction. Compositional changes, enzymatic digestibility of carbohydrate components, and transformation of lignin were used to evaluate the impact of each constituent on biomass processing. Ternary DHBA-ChCl DESs exhibited better performances in delignification, fermentable sugar production, and preservation of β–O–4 ether linkage in lignin compared to neat ChCl-DHBA DES.
This study used a Discrete Element Method (DEM) model to investigate the Jenike shear process of flexible, cylindrical particles with different aspect ratios. The model was validated through experiments and analytical solutions. It was found that particle shape and deformation have a significant impact on friction behavior, affecting particle deformation, contact forces, orientation, and internal friction angle. Results indicate an increase in shear stress with normal load, regardless of particle stiffness or shape. Flexible particles showed higher shear stress and internal friction angles than rigid ones, especially for aspect ratios of 6. With aspect ratios of 4 and 5, flexible particles deform significantly during shear with complex reconfiguration, while aspect ratio 6 particles experience a uniform reconfiguration, indicating a solid packing structure that enhances flow resistance. These findings will aid in improving kinetic theories for granular flow of complex irregular particle flows.
To alleviate the greenhouse gas emissions by the chemical industry, electrification has been proposed as a solution where electricity from renewable sources is used to power processes. The adoption of renewable energy is complicated by its spatial and temporal variations. To address this challenge, we investigate the potential of distributed manufacturing for electrified chemical processes installed in a microgrid. We propose a multi-scale mixed-integer linear programming model for locating modular electrified plants, renewable-based generating units, and power lines in a microgrid that includes monthly transportation and hourly scheduling decisions. We propose a K-means clustering-based aggregation disaggregation matheuristic to solve the model efficiently. The model and algorithm are tested using a case study with 20 candidate locations in Western Texas. Additionally, we define a new concept, “the Value of the Multi-scale Model”, to demonstrate the additional economic benefits of our model compared with a single-scale model.
This paper considers the problem of state observation for nonlinear dynamics. While model-based observer synthesis is difficult due to the need of solving partial differential equations, this work proposes an efficient model-free, data-driven approach based on online learning. Specifically, by considering the observer dynamics as a Chen-Fliess series, the estimation of its coefficients has a least squares formulation. Since the series converges only locally, the coefficients are recursively updated, resulting in an online optimization scheme driven by instantaneous gradients. When the state trajectories are available, the online least squares guarantees an ultimate upper bound of average observation error proportional to the average variation of states. In the realistic situations where the states cannot be measured, the immersed linear dynamics based on the Kazantzis-Kravaris/Luenberger structure is assigned, followed by online kernel principal component analysis for dimensionality reduction. The proposed approach is demonstrated by a limit cycle dynamics and a chaotic system.
Two-dimensional (2D) membranes have demonstrated potential for molecular separation; however, their applicability for Li/Mg ion separation has been restricted by their negatively-charged and easily-swellable properties in water. Moreover, their practical application has been hindered by the challenge of producing significant quantities of single-layer nanosheets. To overcome these challenges, we have developed a scalable method for synthesizing micro-sized nitrate ZnAl layered double hydroxide (LDH) and subsequent exfoliating to yield monolayer nanosheets for the construction of 2D membranes. The sub-nanometer channels of the LDH membrane is positively charged, which prevents the passage of magnesium ions. These channels also impede the flow of magnesium ions that are more difficult to dehydrate. As a result, the LDH membranes exhibit robust lithium-magnesium separation ability, with a separation ratio of 6 (Li/Mg). This work provides a method for producing high-quality LDH nanosheets and validates the enormous potential of LDH membranes in the field of lithium-magnesium separation.
Accurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, this paper aims at developing convex optimization formulations for designing the optimal sensor network using information-theoretic measures in linear steady-state data reconciliation. To this end, the estimation errors are characterized by a multivariate Gaussian distribution, and thus the analytical form for entropy and Kullback-Leibler divergences (forward, reverse, and symmetric) of estimation errors can be obtained to formulate the optimal sensor network design. The proposed information theoretic-based optimal sensor selection problems are shown to be integer semidefinite programming problems where the relaxation of binary decision variables results in solving a convex optimization problem. Thus, we use a branch and bound method to obtain a globally optimal sensor network design. Demonstrative case studies are presented to illustrate the efficacy of the proposed optimal sensor selection formulations.