The cell free system has been paid more attention due to its potential of facilitating more eﬃcient catalysis of multistep reactions. In this study, an efficient enzymatic cascade of GSH production was developed through the evolution of bifunctional glutathione synthetase (GshF), coupled with polyphosphate kinase (PPK). First, the stability and activity of GshF were enhanced by loop interchange and site-directed mutagenesis. As a result, the GshF half-value period increased 163.3-fold, and its activity raised 18 %. PPK from Jhaorihella thermophile (PPKJT) was characterized and used to regenerate ATP in the GSH synthesis, with hexametaphosphate (PolyP(6)) as the phosphate donor. After the process optimization, 99.9 mM GSH and 7.6 mM oxidized glutathione (GSSG) were produced within 2 h. The molar yield was 95.9 mol/mol based on the amino acid added, while the productivities of GSH achieved 49.95 mM/h, which was the highest yield and productivity ever reported about GSH synthesis.
Since chromatographic separation is a dynamic process, with the interactions between the drug and the chiral stationary phase mediated by the solvent, no single interacting structure, such as could be found by minimizing the energy, could possibly describe and account for the ratio of residence times in the chromatographic column for the enantiomeric pair. We describe the use of explicit-solvent fully atomistic molecular dynamics simulations, permitting all the interactions between the atoms constituting the chiral stationary phase, solvent molecules and the drug molecule. This allows us to better understand the molecular dynamic chiral recognition that provides the discrimination which results in the separation of enantiomers by high performance liquid chromatography. It also provides a means of predicting, for a given set of conditions, which enantiomer elutes first and an estimate of the expected separation factor. In this review we consider the use of molecular dynamics towards this understanding and prediction.
Vapor recompressed batch distillation (VRBD) is an energy-integrated configuration which works on the principle of a heat pump. Operation of such a column is challenging due to unsteady, nonlinear dynamics and strong interplay between separation and energy efficiency. In this paper, a two-step approach is proposed for optimal operation and control of such a column. Initially, an openloop optimal operation policy is generated for maximization of an overall performance index using offline optimization. To this end, three performance indices are proposed to capture interplay between separation and energy efficiency. Subsequently, a model-based output feedback controller is designed to track this optimal performance trajectory. The effectiveness of the proposed approach is demonstrated using a benzene-toluene separation case study wherein it is shown that the proposed approach helps to achieve optimal operation in the presence of operational disturbances.
The growth of silica nanoparticles by agglomeration and viscous flow sintering is studied from free molecular to transition regime at high temperatures by discrete element method simulations. The effect of temperature on the aggregate mobility and gyration radii, particle morphology and collision frequency function is elucidated as function of the number of primary particles. The ratio between the characteristic sintering time and characteristic collision time controls the particle size and structure, quantified by the mass fractal dimension. The effect of this ratio of characteristic times on aggregate morphology is illustrated at various temperatures. Finally, when sintering is negligible, the overall collision frequency is 90% larger than that predicted by the classic Fuchs collision kernel for monodisperse agglomerates in the near free molecular and transition regime. For comparable coagulation and sintering rates, where aggregates with sinter bonds are formed, the overall collision frequency increases an enhancement of <90% is observed.
Massive amounts of gas hydrates occur naturally in the pores of sediments or fractures in permafrost regions and beneath the oceans. For hydrate formation in confinement, the equilibrium condition can shift to harsher conditions, lowering the water activity, and subsequently depressing the hydrate freezing temperature at a given pressure. Conversely, the nucleation and rate of hydrate formation, as well as hydrate conversion can be increased in confinement. Therefore, reliable assessment of the hydrate distribution in nature requires accurate thermodynamic and kinetic models of hydrate formation; however, these models tend to be based upon the properties of bulk hydrates. Hydrate formation and growth promotion in confinement are potentially interesting for hydrate technological applications, such as gas separation, energy storage, and flow assurance. This paper reviews the thermodynamic and kinetic properties and their interrelations of gas hydrates in confined spaces.
The low concentration methyl iodides (CH3I) adsorption process on reduced silver-functionalized silica aerogel (Ag0-Aerogel) was studied. The kinetic data were acquired using a continuous flow adsorption system. Because the corresponding physical process was observed, the shrinking core model (SCM) was modified and applied. An average CH3I pore diffusivity was calculated, the CH3I-Ag0-Aerogel reaction was identified as a 1.37 order reaction instead of first order reaction, and the nth order reaction rate constant was determined. This modified SCM significantly increases the accuracy of adsorption behavior prediction at low adsorbate concentration. Modeling results indicate that the overall adsorption process is controlled by the pore diffusion. However, at low adsorbate concentration (<100 ppbv), the CH3I adsorption is limited to the surface reaction due to the low uptake rate in a predictable time period.
A group of drugs used in Intra-Arterial Chemotherapy (IAC) have intrinsic ionic properties, which can be used for filtering excessive drugs from blood in order to reduce systemic toxicity. The ion-exchange mechanism is utilized in an endovascular Chemofilter device which can be deployed during the IAC for capturing ionic drugs after they have had their effect on the tumor. In this study, the concentrated solution theory is used to account for the effect of electrochemical forces on the drug transport and adsorption by introducing an effective diffusion coefficient in the advection-diffusion-reaction equation. Consequently, a multi-physics model coupling hemodynamic and electrochemical forces is developed and applied to simulations of the transport and binding of Doxorubicin in the Chemofilter device. A comparison of drug adsorption predicted by the computations to that measured in animal studies demonstrated the benefits of using concentrated solution theory over the Nernst-Plank relations for modeling drug binding.
Having proper correlations for hydrodynamic forces is essential for successful CFD-DEM simulations of a fluidized bed. For spherical particles in a fluidized bed, efficient correlations for predicting the drag force, including the crowding effect caused by surrounding particles, are already available and well tested. However for elongated particles, next to the drag force, the lift force and hydrodynamic torque also gain importance. In this work we apply recently developed multi-particle correlations for drag, lift and torque in CFD-DEM simulations of a fluidized bed with spherocylindrical particles of aspect ratio 4 and compare them to simulations with widely used single-particle correlations for elongated particles. Simulation results are compared with previous magnetic particle tracking (MPT) experimental results. We show that multi-particle correlations improve the prediction of particle orientation and vertical velocity. We also show the importance of including hydrodynamic torque.
The controllable mass transfer and reaction rate for phase transfer hydrogenation of acetophenone across a well-defined boundary were investigated. The effect of solvent was found important and 1-butanol exhibited the best performance among the five investigated homologous alcohol solvents, consistent with its higher solubility in water and greater dielectric constant. Initial reaction rates increased with increasing electric potential, consistent with enhanced mass transfer across the aqueous/organic boundary. At longer reaction times deactivation was apparent. It correlated with increasing voltage and is ascribed to lower equilibrium concentration of reactive species at the interface. External control over reaction rate was demonstrated by switching the applied electric potential over the course of the reaction. Effects of external electric field on enantioselectivity were also explored with reversal field direction. The changes correlate with catalyst decomposition.
Aspen Plus® simulations using the Peng-Robinson (PR-EOS) and the COSMO-SAC models were performed to study absorption power cycles (APCs) using mixtures of R-134a with two ionic liquids, [C2C1im][Tf2N] or [C6C1im][Tf2N], and compared against an R-134a organic Rankine cycle (ORC) operating under similar conditions. The PR-EOS results were in agreement with calculations from a PR model fitted to the R-134a+IL experimental phase equilibrium data. The APCs have similar efficiencies and outperform the ORC by 3-46%, with the largest differences observed when operating with lower grade (lower TH) heating sources, lower quality cooling (higher TL) and lower subcooling in the pump inlet stream. The PR-EOS and the COSMO-SAC results follow similar trends, but numerical discrepancies are observed in the cycle efficiencies and stream flow rates and compositions due to differences in solubilities and enthalpy changes between both models, suggesting that improvements are needed to increase the accuracy of COSMO-SAC for these systems.
The SAFT-γ Mie group-contribution equation of state is used to represent the fluid-phase behaviour of aqueous solutions of a variety of linear, branched, and cyclic amines. New group interactions are developed in order to model the mixtures characterised by alkyl primary, secondary, and tertiary amine groups (NH2, NH, N), cyclic secondary and tertiary amine groups (cNH, cN), and cyclohexylamine groups (cCHNH, cCHN). The group-interaction parameters are estimated from appropriate experimental thermodynamic data for pure amines and selected mixtures. The fluid-phase behaviour of these mixtures can then be described over broad ranges of temperature, pressure, and composition. Liquid-liquid equilibria (LLE) bounded by lower critical solution temperatures (LCSTs) have been reported experimentally and are reproduced here with SAFT-γ Mie approach. The main feature of the approach is the ability to represent accurately the experimental data employed in the parameter estimation, a to predict the phase equilibria with the same set of parameters.
Research problems in the domains of physical, engineering, biological sciences, often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.
In this study, we developed an integrated cellular automata and thermal lattice Boltzmann model to investigate the effects of different temperatures and velocities in a microbioreactor. Compared with previous studies this model accounted for the direct effects of transient temperature on biofilm growth and indirect effects caused by changes of properties. In addition, the algorithms on variations in solid boundary conditions, detachment and extra mass transport have been improved. Results showed that temperature affected both the maximum biofilm concentration and growth speed. Roughly a 10-75% increase in biofilm concentration was observed, while in some cases the time needed to reach maximum concentration decreased from 30 days to 5 days. Despite of geometrical symmetry, changes in the upper inlet characteristics were more effective on biofilm growth. This demonstrates the capability of the present model to simulate biofilm behaviour in the microbioreactor and its potential industrial and clinical applications.
The controlling nanofiller aggregation and strengthening interfacial interaction are of great scientific significance for mixed matrix membranes (MMMs). In this study, the polymer-embedded metal-organic framework (pMOF) microspheres (MSs) are designed by one-pot synthesis and employed as microfillers for improving separation performance of MMMs. Through adding polymer during solvothermal crystallization, the polymer chains are embedded into the MOF materials, and the morphologies of the MOFs are transformed from nanopaticles to polycrystalline MSs. Since the embedding of the identical polymer promotes the compatibility of polymeric matrixes and fillers, as well as the micrometer-sized porous MSs offer additionally superior and permanent transport pathways, the resulted MMMs display simultaneously enhanced selectivity and permeability for carbon capture. The CO2/CH4 selectivity and CO2 permeability of the pMOF MMMs are achieved at 1.3 and 2.2 times as those of the pure polymeric membranes, and 1.5 and 1.2 times as those of the MOF MMMs, respectively.
The majority of clinically approved therapeutics target membrane proteins (MPs), highlighting the need for tools to study this important category of proteins. To overcome limitations with recombinant MP expression, whole cell screening techniques have been developed that present MPs in their native conformations. Whereas many such platforms utilize adherent cells, here we introduce a novel suspension cell-based platform termed “biofloating” that enables quantitative analysis of interactions between proteins displayed on yeast and MPs expressed on mammalian cells, without need for genetic fusions. We characterize and optimize biofloating and illustrate its sensitivity advantage compared to an adherent cell-based platform (biopanning). We further demonstrate the utility of suspension cell-based approaches by iterating rounds of magnetic-activated cell sorting (MACS) selections against MP-expressing mammalian cells to enrich for a specific binder within a yeast-displayed antibody library. Overall, biofloating represents a promising new technology that can be readily integrated into protein discovery and development workflows.
The isosteric heat of adsorption is an important thermodynamic property used to characterise and optimise adsorption processes. In this work, analytic expressions for isosteric heats of adsorption are derived for a collection of commonly used isotherm models and a two-dimensional molecular equation of state based on the SAFT-VR approach. The use of these expressions is presented with an example of adsorption of nitrous oxide, N₂O, on biochar, which is a waste biomass charcoal that exhibits high adsorption potential. The results show that accurate fitting of the adsorption isotherms leads to consistent results obtained with different approaches, however, the predicted isosteric heat of adsorption exhibits strong variations in the regions where experimental data is insufficient such in the region of low pressure/low coverage. Convergence on the prediction of the isosteric heat of adsorption by the different models is only observed in the region where no extrapolation of experimental data is needed.
This work addresses the design of distributed model predictive control (DMPC) systems for nonlinear processes using machine learning models to predict nonlinear dynamic behavior. Specifically, sequential and iterative distributed model predictive control systems are designed and analyzed with respect to closed-loop stability and performance properties. Extensive open- loop data within a desired operating region are used to develop Long Short-Term Memory (LSTM) recurrent neural network models with a sufficiently small modeling error from the actual nonlinear process model. Subsequently, these LSTM models are utilized in Lyapunov- based DMPC to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. Using a nonlinear chemical process network exam- ple, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed-loop performance is very close to the one of a centralized MPC system.
This study investigates the application of a recently developed construct, the Uniform Trigonometrization Method (UTM), to the singular control problems in chemical engineering. The UTM involves minimal modifications to the original problem, thereby generating near-singular control solutions that can be used for conceptual design and serve as an alternate to direct techniques like nested and simultaneous approaches. Eight classical singular control problems with known analytical solutions and three complex singular control problems from chemical engineering domain are solved in this study. The results obtained using the UTM for these problems are found to match well with the literature and are of higher resolution as compared to the results obtained using a direct pseudospectral based solver. The ability of the UTM to handle complex chemical engineering problems with both singular controls and state path constraints has also been demonstrated in this study.