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).
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.
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.
Oblique collisions of two spherical particles coated with a thin layer of viscous liquid are considered. Experimental measurements are performed using particle tracking velocimetry. Comprehensive experimental data for collisions with an impact angle between 0° - 60° are presented. Collisions are characterised by the Stokes’ number, the coefficient of restitution, and the rotational velocity. The experiments are compared to numerical simulations using the discrete element method (DEM). The translational velocities predicted by the simulations were in good agreement with the experiments at high Stokes’ number, where the models are dominated by the normal components. As the tangential forces become more significant (i.e. at low to medium Stokes’ number, and high collision angle), agreement between the simulations and experiments is poorer. At low Stokes’ number the translational velocities were in good agreement with the experiments, but was poorer at high Stokes’ number.
The direct Z-scheme provide a potential strategy for high efficient CO2 photoreduction, whereas the heterointerface contact resistance is significantly limited the interfacial electron transfer kinetic. Herein, we build the directional charge-transfer channels in a direct Z-scheme system over metal−organic frameworks (MOFs), that is the lattice-guided MOF-on-MOF hybrids, to facilitate CO2 photoreduction. The heteroepitaxial lattice growth along the c-axis of MIL-88B(Fe) via the high-activity (001) facet over the stable (111) facet of UiO-66-NH2. Theoretical calculations and experimental results provide the direct evidence that engineering direct Z-scheme of these MOFs hybrids can induce the electrons migration from UiO-66-NH2 to the holes of MIL-88B(Fe) by directional charge-transfer channels owing to their lattice match. This can dramatically boosts photocatalytic CO2-to-CO selectivity up to nearly 100%, with a rate of 2.26 μmol·g-1·h-1. This work demonstrates that the efficiently selective CO2 photoreduction processes can be achieved by engineering Z-scheme via lattice intergrown of MOF hybrids strategy.
Because of very high potential barrier for thermionic emission and trap-assisted charge recombination, photocatalytic reaction rate that determined by semiconductor-cocatalyst interfacial electron transfer severely deviates from linearity to the photocatalyst dosage or to the light intensity. This makes it challenging to maximize utilization of practical irradiation by referring the parameters evaluated from method used in conventional catalysis. We here develop a model and predict that photocatalytic reaction rate positively correlates to photocatalyst concentration under weak illumination while the correlation becomes negative under intense irradiation. The theoretical simulation that matches the experimental values can be used to guide maximizing photocatalytic photon utilization under various intensity of irradiation. The strong correlation can rationalize photocatalytic evaluation instead of obtaining a numerically high value by excessively lowering the denominators. To realize efficient utilization of real-time changing sunlight, we propose a reactor configuration that can optimize the amount of photocatalyst participating into the reaction.
Herein, we propose a novel method to enhance the photoreactivity of an MOF catalyst by grafting isocyanate bonds (−N=C=O) and sulfhydryl-complexed copper (−SCu) onto ZIF-8 (NIF-SCu). The grafting process intercalated interlayer bands between the conduction and valence bands of ZIF-8, thereby providing a “ladder” for facile electron transition. The extreme improvement in the photoreactivity of NIF-SCu could be attributed to the enhancement in light responses in the range of 350–450 nm by −N=C=O groups and the widening of the visible light range of the MOF by −SCu groups. The formation of staggered energy levels in NIF-SCu could also narrow the band gap, lower the resistance, and facilitate the transfer of photogenerated carriers, thereby generating electrons with strong reduction potential in the −SCu conduction band. This study provides a new strategy for improving or even endowing the photoactivity of environmental functional materials with wide bandgaps.
This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.
An economical and highly uranium extraction from seawater remains a crucial task for energy sources and environmental safety. Aiming for improving the mass transfer rate of uranium from seawater, a new synthetic strategy was adopted to synthesize 2D-open channel microporous bio-adsorbent for uranium extraction from seawater. Herein, a vapor phase modification approach was adopted to graft divinylbenzene(DVB), and polyacrylonitrile(AN) onto the surfaces of microporous frameworks via a free radical polymerization method. The post-synthetic functionalization was carried out by hydrothermal process, where amidoxime groups are structure-directing agents to trap uranium. Further, amidoxime groups not only enhanced hydrophilicity but also adjusts adsorbents pKa. AO-Fc faces minimum interference of competing ions and achieves a high uranium adsorption capacity of 8.57±0.02 and 409±1 mg/g in seawater and simulated solution. Despite its stable structure, AO-Fc exhibits a long life span and negligible weight loss revealed AO-Fc could be applied as a potential adsorbent for radionuclides
The production of hydrocarbons for the synthesis of readily available energy and multifunctional materials is of great importance in modern society. Zeolites have proven to be a boon for the targeted regulation of specific hydrocarbon as shape-selective catalyst in converting carbon resources. Yet our mechanistic understanding and quantitative description of shape-selectivity of zeolite catalysis remains rather limited, which restricts the upgrade of zeolite catalysts. Herein, we proposed quantitative principle of shape-selectivity for zeolite catalysis using methanol-to-hydrocarbons (MTH) as model. Combining with molecular simulations and infrared imaging, we unveil the competition of thermodynamic stability, preferential diffusion and favored secondary reactions between different hydrocarbons within zeolite framework are the essence of zeolite shape-selective catalysis. Notably, we provide methodology to in silico search for the optimal combination of framework topology and acidity properties of zeolites with operating conditions that potentially outperform commercial MTH catalysts to achieve high selectivity of desired hydrocarbon products.
While protein medications are promising for treatment of cancer and autoimmune diseases, challenges persist in terms of development and injection stability of high-concentration formulations. Here, the extensional flow properties of protein-excipient solutions are examined via dripping-onto-substrate (DoS) extensional rheology, using a model ovalbumin protein (OVA) and biocompatible excipients polysorbate 20 (PS20) and 80 (PS80). Despite similar PS structures, differences in extensional flow are observed based on PS identity in two regimes: at moderate total solution concentrations where surface tension differences drive changes in extensional flow behavior, and at small PS:OVA ratios, which impacts the onset of weakly elastic behavior. Undesirable elasticity is observed in ultra-concentrated formulations, independent of PS identity; higher PS contents are required to observe these effects than with analogous polymeric excipient solutions. These studies reveal novel extensional flow behaviors in protein-excipient solutions, and provide a straightforward methodology for assessing the extensional flow stability of new protein-excipient formulations.
Modern chemical processes need to be operated around different operating conditions to optimize plant economy, in response to dynamic supply chains. As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.
This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real-time machine learning modeling-based predictive controller to handle batch-to-batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network-based model predictive controller (AERNN-MPC) is developed to optimize product yield, crystal size, and energy consumption while accounting for the physical constraints on cooling jacket temperature. Deviations in the kinetic parameters are considered in the closed-loop simulations to account for the B2B parametric drift, and two error-triggered online update mechanisms are proposed to address issues pertaining to the availability of real-time crystal property measurements and are incorporated into the AERNN-MPC to improve the model prediction accuracy. Closed-loop simulation results demonstrate that the proposed AERNN-MPC with online update, irrespective of the accessibility to real-time crystal property data, achieves a desired closed-loop performance in terms of maximizing product yield and minimizing energy consumption.
To validate the experimental results of Part-1, we conducted a two-phase flow simulation of imbibition of a wetting liquid through 2D microstructures made of ellipses of varying aspect ratios. The flow simulation in the particulate microstructures, characterized by low (ellipse) aspect ratio, produced somewhat even micro-fronts, thus replicating the sharp fronts at the visual (macroscopic) scale observed in Part-1. Whereas simulations in the fibrous microstructures produced highly uneven micro-fronts, suggesting the formation of semi-sharp or diffuse visual fronts. Increasing the porosity from 50% to 70% resulted in solid-phase clustering and led to further increase in the unevenness of micro-fronts, pointing to purely diffuse visual fronts. The evolution of the saturation plots along the flow direction, obtained from area-averaging of fluid-distribution plots, pointed to diffusing of sharp fronts with time. The predictions matched our previous experimental observations, i.e., the particulate media create sharp fronts while the fibrous media create semi-sharp/diffuse fronts.
The dynamics and breakup of bubbles in swirl-venturi bubble generator (SVBG) are explored in this work. The three-dimensional movement process and breakup phenomena of bubbles are captured by one high-speed camera system with two cameras while the distribution of swirling flow field are recorded through Particle Image Velocimetry technology. It is revealed that bubbles have two motion trajectories, which are deeply related to bubble breakup. One trajectory is that mother bubble moves upward in an axial direction of the SVBG to the diverging section, and the other trajectory is that mother bubble rotates obliquely upward to another side-wall along the radial direction. Meanwhile, binary breakup, shear-off-induced breakup, static erosive breakup and dynamic erosive breakup are observed. For relatively high liquid Reynolds number, vortex flow regions are extended and the bubble size is reduced. Furthermore, it is worth noting that the number of microbubbles increases significantly for intensive swirling flow.
The strong core base in chemical engineering during the latter half of the 20th century enabled chemical engineers to contribute extensively to many areas outside of the traditional. The depth of such involvement has led researchers to confront questions much more engaging to the field of application, thus adopting and cultivating expertise more native to it than to secure chemical engineering as a discipline. The progress of knowledge in science and engineering must leave a strong trail of fundamental understanding through developed methodologies that can assist in continuing progress. If this tenet is acknowledged, this article yields considerable scope for discussion on whether chemical engineering research is continuing to provide for a growing core that has endowed chemical engineers with the ability to formulate and solve important societal problems in which material systems undergo changes in composition and energy. We discuss opportunities for hopefully serving the issues of concern.
The state estimation and sensor placement for a continuous pulp digester with delayed measurements are investigated. The underlying model of interest is heat transfer in a pulp digester modeled by two coupled hyperbolic partial differential equations and an ordinary differential equation. Output measurements are considered with delay due to the possible low sampling rate. The Cayley-Tustin transformation is utilized to realize model time discretization in a late lumping manner which does not account for any type of spatial approximation or model reduction. The discrete Kalman filter is applied to estimate the system states using the delayed measurements. The selection of sensor location is addressed along with estimator design accounting for the delayed measurements and investigated by minimizing the variance of estimation error. The performance of the state estimator is evaluated, and the sensor placement is analyzed through simulation studies, which provide guidance for sensor location selection in industrial applications.
A two-step integrated MOF and pressure/vacuum swing adsorption (P/VSA) process design has been recently established for gas separation. In the first step, selected MOF descriptors and process operating conditions are simultaneously optimized to maximize the process performance. Based on the obtained results, the second step (i.e., MOF matching) is addressed and exemplified by propene/propane separation in this work. Computational MOF synthesis and screening are carried out to find new advanced material candidates for enhancing the separation process efficiency. First, model-based property-performance relationships are developed for fast MOF screening. Then, MOF building blocks are extracted from 471 CoRE MOFs. With these building blocks, 45472 hypothetical MOFs are created. After model-based and molecular simulation-based screening, six candidates are left and sent to P/VSA process optimization. Finally, three candidates are found to meet the pre-defined separation specifications and one candidate shows a better process performance than the best out of the 471 MOFs.