Accurately constructing membranes based on two-dimensional (2D) materials on commercial porous substrates remains a significant challenge for H2 purification. In this work, a series of tubular 2D MXene membranes are prepared on commercial porous stainless steel substrates via fast electrophoretic deposition. Compared with other methods, such as filtration or drop coating, etc. such preparation route shows the advantages of simple operation, high efficiency for membrane assembly (within 5 min) with attractive reproducibility, and ease for scale-up. The tubular MXene membranes present excellent gas separation performance with hydrogen permeance of 1290 GPU and H2/CO2 selectivity of 55. Furthermore, the membrane displays extremely stable performance during the long-term test for more than 1250 h, and about 93% of the membranes from one batch have exceeded the DOE target for CO2 capture. Most importantly, this work provides a valuable referential significance for other types of 2D materials-based membranes for future application development.

Classical molecular dynamics simulations were used to study the separation of carbon dioxide from methane by three formulations of the deep eutectic solvent (DES) ethaline (choline chloride: ethylene glycol at 1:2, 1:4 and 1:8 molar ratios), in the bulk and confined inside carbon and titania slit pores of two different pore widths, 2 nm and 5 nm. The highest permselectivities (~20) are observed for 1:2 ethaline in a 5 nm carbon pore, followed by the 1:4 DES in a 5 nm graphite pore, 1:2 ethaline in a 2 nm carbon pore and the 1:8 bulk DES. Our results indicate that variations in the ratio of ethylene glycol, which in turn affect the interactions of all DES species with the gas molecules and the different pore walls, plus confinement effects resulting from varying the pore sizes, can affect the gas separation performance of these systems in complex ways.

As key components of antifouling material surfaces, the design and screening of polymer molecules grafted on the substrate are critical. However, current experimental and computational models still retain an empirical flavor due to the complex structure of polymers. Here, we report a simple and general strategy that enables multi-scale design and screening of easily synthesized functional polymer molecules to address this challenge. Specifically, the required functions of the antifouling material are decomposed and assigned to different modules of the polymer molecules. By designing different modules, a novel bio-inspired polymer with three zwitterionic poly (sulfobetaine methacrylate) (PSBMA) chains, three catechol (DOPA) anchors (tri-DOPA-PSBMA), and a tris(2-aminoethyl) amine (TREN) scaffold were screened out. Moreover, it was successfully synthesized via an atom transfer radical polymerization (ATRP). The excellent performance of tri-DOPA-PSBMA with a versatile and convenient grafting strategy makes it a promising material for marine devices, biomedical devices, and industrial applications.

Chaotic flow inside porous media accelerates the transport, mixing, and reaction of molecules and particles in widespread natural and factitious processes. Current macroscopic models based on the average pore-scale variations show obvious limitations in the prediction of many chemical processes. In this paper, we reconstruct microscopic foam structures using Micro Computed Tomography to simulate fluid flow in structured ZSM-5@SiC foam catalyst. Moreover, we propose a conceptual model based on the microscopic mean square displacement theory to characterize the effective dispersion inside an open-cell foam. This model will explain the flow characteristics of confined fluid inside the porous media from fluid elements perspective. Particularly, dispersion factor and structure factor, as key parts of this model, perfectly interpret the driving characteristics of pressure drop, velocity different, and reaction in continuous foam media flow. This work also provides a unique means of predicting reaction kinetics of confined fluid in structured foam catalyst.

A robust aluminum-based metal-organic framework (Al-MOF) MIL-120Al with 1D rhombic ultra-microporous was reported. The non-polar porous walls composed of para-benzene rings with a comparable pore size to the kinetic diameter of methane allow it to exhibit a novel thermodynamic-kinetic synergistic separation of CH4/N2 mixtures. The CH4 adsorption capacity was as high as 33.7 cm3/g (298 K, 1 bar), which is the highest uptake value among the Al-MOFs reported to date. The diffusion rates of CH4 were faster than N2 in this structure as confirmed by time-dependent kinetic adsorption profiles. Breakthrough experiments confirm that this MOF can completely separate the CH4/N2 mixture and the separation performance is not affected in the presence of H2O. Theoretical calculations reveal that pore centers with more energetically-favorable binding sites for CH4 than N2. The results of pressure swing adsorption (PSA) simulations indicate that MIL-120Al is a potential candidate for selective capture coal-mine methane.

In the present work, a series of deep eutectic solvents (DESs) based on organic amine as hydrogen bond acceptors (HBAs), and ethylene glycol (EG) as hydrogen bond donor (HBD) were prepared for the H2S absorption. Thermal decomposition temperature, HBA mass ratios, alkalinity and structure effect on absorption behavior were systematically investigated. The reaction mechanism was demonstrated by FT-IR and 1H NMR spectroscopy. The reaction equilibrium constants, Henry constant, enthalpy and entropy change were calculated based on the thermodynamic model to reveal the interactions between DESs and H2S. It is found that H2S absorption capacities of the most of DESs with HBA/HBD mass ratio of 1:4 were close to 1mol /mol at 303.15K and 0.2 bar. The absorption capacity of DESs depends on the alkalinity and structure of HBAs; Additionally, a good linear correlation between the alkalinity of HBA and the absorption equilibrium constant (lnK) of DESs to H2S was found

We built a molecular-level kinetic model for hydrocarbon catalytic cracking, incorporating the catalyst acidity as the parameter to estimate the reaction rates. The n-decane and 1-hexene co-conversion catalytic cracking process was chosen as the studying case. The reaction network was automatically generated with a computer-aided algorithm. A modified linear free energy relationship was proposed to estimate the activation energy in a complex reaction system. The kinetic parameters were initially regressed from the experimental data under various reaction conditions. On this basis, the product composition was evaluated for three catalytic cracking catalysts with different Si/Al. The Bronsted acid and Lewis acid as the key catalyst properties were correlated with the kinetic parameters. The built model can calculate the product distribution, and molecular composition at different reaction conditions for different catalysts. The sensitive study shows that it will facilitate the model-based optimization of catalysts and reaction conditions according to product demands.

Mo-based catalysts are widely used for the SO2 hydrogenation process. However, the detailed reaction mechanism is still unclear and some details should be further supplemented. In this paper, the SO2 hydrogenation processes over the Mo-based catalyst were systematically studied. Several technologies including temperature-programmed experiments, isotope-tracing experiment, FTIR spectra and switching experiment were adopted to investigate the reaction steps. The results indicated that during the SO2 hydrogena

Error-in-variables model (EVM) methods are used for parameter estimation when independent variables are uncertain. During EVM parameter estimation, output measurement variances are required as weighting factors in the objective function. These variances can be estimated based on data from replicate experiments. However, conducting replicates is complicated when independent variables are uncertain. Instead, pseudo-replicate runs may be performed where the target values of inputs for repeated runs are the same, but the true input values may be different. Here, we propose a method to estimate output-measurement variances for use in multivariate EVM estimation problems, based on pseudo-replicate data. We also propose a bootstrap technique for quantifying uncertainties in resulting parameter estimates and model predictions. The methods are illustrated using a case study involving n-hexane hydroisomerization in a well-mixed reactor. Case-study results reveal that assumptions about input uncertainties can have important influences on parameter estimates, model predictions and their confidence intervals.

The suitability of phenyl–based deep eutectic solvents (DESs) as absorbents for toluene absorption was investigated by means of thermodynamic modeling and molecular dynamics (MD). The thermodynamic models PC–SAFT and COSMO–RS were used to predict the vapor–liquid equilibrium (VLE) of DES–toluene systems. PC–SAFT yielded quantitative results even without using any binary fitting parameters. Among the DESs consisting of three different HBAs and three different HBDs (phenol, levulinic acid, ethylene glycol), [TEBAC][PhOH] was considered as the most suitable absorbent. Systems with [TEBAC][PhOH] had lowest equilibrium pressures of the considered DES–toluene mixtures, the best thermodynamic characteristics (i.e., Henry’s law constant, excess enthalpy, free energy of solvation of toluene), and the highest self–diffusion coefficient of toluene. The molecular–level mechanism was explored by MD simulations, indicating that [TEBAC][PhOH] has the strongest interaction of HBA–/HBD–toluene compared to the other DESs under study. This work provides guidance to rationally design novel DESs for efficient aromatic VOCs absorption.

The experimental and simulation results indicate that the reverse Brazil nut effect (RBNE)-Brazil nut effect (BNE) segregation inversion happens faster in the circular-bottom container than that in the flat-bottom container. The starting location of the sinkage of heavier grains at the top layer is triggered with certain randomness in the latter, whereas it first occurs at either of the lateral bottom edges in the former. The occurrence of standing-wave resonant spots of higher and lower granular temperature accelerates the RBNE-BNE transition. From the elastic collision model of single grain, the bottom with a larger angle leads to more energy transfer from the vertical direction. The simulation results of a monodisperse granular bed confirm that the circular-bottom container possesses a higher granular temperature and a lower packing density at the lateral edges of the circular bottom, whereas the flat-bottom container has a uniform standing-wave distribution with a period.

Lamellar membranes, especially assembled by microporous framework nanosheets, have excited interest for fast molecular permeation. However, the underlying molecular dissolution behaviors on membrane surface, especially at pore entrances, remain unclear. Here, hierarchical metal-organic framework (MOF) lamellar membranes with 7 nm-thick surface layer and 553 nm-thick support layer are prepared. Hydrophilic (–NH2) or hydrophobic (–CH3) groups are decorated at pore entrances on surface layer to manipulate wettability, while –CH3 groups on support layer provide comparable, low-resistance paths. We demonstrate that molecular dissolution behaviors are determined by molecule-molecule and molecule-pore interactions, derived from intrinsic parameters of molecule and membrane. Importantly, two dissolution model equations are established: for hydrophobic membrane surface, dissolution activation energy (ES) obeys ES=Kmln[(γL-γC)μd2], while turns to ES=Kaln[(γL-γC)δeμd2] for hydrophilic one. Particularly, hydrophilic pore entrances exert strong interaction with polar molecules, thus compensating the energy consumed by molecule rearrangement, giving fast permeation (> 270 L m-2 h-1 bar-1).

In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.

The Global Methane Pledge declared at the 2021 United Nations climate change conference (COP26) marked the world’s commitment to eradicate methane emissions. Most of these emissions are generated by the oil-gas industry, waste landfills, and agriculture sectors, and are lean in composition. This work explores the use of an intensified reactor that implements the chemical looping principle to handle lean methane emissions. A model-based framework is used to showcase the baseline performance of the proposed reactor in converting methane emissions using nickel-based oxygen carriers. Then, sensitivity analysis of the reactor performance with respect to operating conditions is performed. The reactor is subsequently optimized to minimize the methane emitted, using a dynamic program with safety and operability constraints for the alternating redox process. With the optimal cycle strategy, we demonstrate that near-complete methane conversion can be achieved by the reactor without external heating.

The rate of KCl recovery by froth flotation using low-grade carnallite is 70–85%. Herein, a novel frother, dipropylene glycol butyl ether (DPNB), was prepared to increase the flotation efficiency of KCl recovery systems. DPNB could be applied at only half the dosage of the conventional frother methyl isobutyl carbinol (MIBC) and achieve a KCl recovery rate of 94.8–98.6% with a high KCl grade (63.2–66.5%). To date, these results are the best reported for pneumatic flotation. DPNB had a 10% higher maximum dynamic stability factor compared with of MIBC; moreover, the apparent entrainment velocity of DPNB was half that of MIBC. The molecular structure of DPNB had hydroxyl and ether groups, which promoted interactions with water, thereby contributing to its excellent froth stability. DPNB is environment friendly owing to its low volatility and, thus, a promising frother for the green and highly efficient flotation of KCl/NaCl.

Oxygen, as a terminal electron acceptor, is an essential substrate in the aerobic bio-oxidation process, affecting bacterial vitality and bio-oxidation performance. In this study, a new and smart platform biotechnology of sealed-oxygen supply bioreactor (SOS-BR) was developed by improving gas pressure to significantly intensify oxygen transfer rate and resolving the formidable barriers of aerobic catalysis. In virtue of SOS-BR, the bio-productivity was greatly improved for three representative substrates (xylose, furfural, glycerol) bio-oxidation with the whole-cell catalysis of Gluconobacter oxydans. The determination of oxygen transfer coefficient (KLα) established an upgraded theoretical dynamic model for gas pressure intersification biosystem. Additionally, viscosity measurement and combined pressure control strategy explained the inflection point phenomenon of productivity and confirmed the intensify mechanism. The new strategy of significantly intensifying oxygen transfer provided insightful ideas for overcoming the subbon obstacle of obligate aerobic catalysis, and further promoted industrial practicability of bio-oxidation.

Clustering of flexible fibers in riser flows is investigated using a hybrid approach of Discrete Element Method and Computational Fluid Dynamics. Unlike spherical particles, the flexible fibers possess elongated shape, undergo significant deformation, and dissipate kinetic energies through rapid fiber deformation. The present studies show that these distinct features have significant impacts on the cluster characteristics of the fibers. An increased fiber aspect ratio leads to an increase in number and size of agglomerates, while it causes a reduction in heterogeneity of solids distribution due to the more dilute clusters with reduced packing densities. As the fibers become more flexible, the heterogeneity increases, and denser clusters are obtained. More significant effects of the fiber flexibility on the clustering are observed for the fibers with larger aspect ratios. The increased energy dissipation through the rapid fiber deformation enhances the clustering by augmenting the number and size of the agglomerates.

A rapid and convenient strategy to monitor the productivity of biomanufacturing is essential for the research in optimizing relevant bioprocesses. In this work, we have developed a fluorescein-derived probe (FL-DT) that reacts rapidly with thiol groups via 1, 4-Micheal addition reaction of the sulfhydryl to unsaturated ketone and releases fluorescence. FL-DT specifically forms fluorescent adduct with two adjacent thiols in a protein of interest (POI), making the probe a reliable tool for protein quantification. The production of xylanase fused with a short di-Cys tag was then successfully monitored and quantified with FL-DT in E. coli system under different protein expression conditions, providing useful information for optimizing the bioprocess. Our work provides a convenient and efficient strategy for POI labeling and monitoring bioproduction.