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.

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 performance of advanced controllers depends on the selection of several tuning parameters that can affect the closed-loop control performance and constraint satisfaction in highly nonlinear and nonconvex ways. There has been a significant interest in auto-tuning of complex control structures using Bayesian optimization (BO). However, an open challenge is how to deal with uncertainties in the closed-loop system that cannot be attributed to a lumped, small-scale noise term. This paper develops an adversarially robust BO (ARBO) method that is suited to auto-tuning problems with significant time-invariant uncertainties in a plant simulator. ARBO uses a Gaussian process model that jointly describes the effect of the tuning parameters and uncertainties on the closed-loop performance. ARBO uses an alternating confidence-bound procedure to simultaneously select the next candidate tuning and uncertainty realizations, implying only one expensive closed-loop simulation is needed at each iteration. The advantages of ARBO are demonstrated on two case studies.

Multifluid model (MFM) simulations have been carried out on liquid-solid fluidized beds (LSFB) consisting of binary and higher-order polydisperse particle mixtures. The role of particle-particle interactions was found to be as crucial as the drag force under laminar and homogenous LSFB flow regimes. The commonly used particle-particle closure models are designed for turbulent and heterogeneous gas-solid flow regimes and thus exhibit limited to no success when implemented for LSFB operating under laminar and homogenous conditions. A need is perceived to carry out Direct Numerical Simulations of liquid-solid flows and extract data from them to develop rational closure terms to account for the physics of LSFB. Finally, a recommendation flow regime map signifying the performance of the MFM has been proposed. This map will act as a potential guideline to identify whether or not the bed expansion characteristics of a given polydisperse LSFB can be correctly simulated using MFM closures tested.

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 present work analyses the behaviour of aqueous solutions of N,N-dimethylethylenediamine as chemical solvent for carbon dioxide separation by gas-liquid absorption. The interest of this molecule is centred on the presence of different types of amino centres that confer the capability to act as typical solvent based on mixtures of amines taking into account the suitable behaviour of some mixtures. For this reason a comparison between diamine and amine blend solvents has been carried out in order to understand the differences between these solvents using absorption and NMR studies. This experimental work analyses the influence of amine type, concentration and ratio between different amines. Also, the effect of gas flow rate used in the bubble column reactor upon the absorption kinetics has been analysed.

Multiple breakdown phenomena may take place when operating dielectric elastomers. Thermal breakdown, which occurs due to Joule heating, becomes of special importance when using multilayered stacks of dielectric elastomers, due to the large volume-to-surface-area-ratio. In this article, a 2D axisymmetric finite-element model of a multilayered stack of dielectric elastomers is set up in \comsol. Both the electro-thermal and electro-mechanical couplings are considered, allowing for determination of the onset of thermal breakdown. Simulation results show that an entrapped particle in the dielectric elastomer drastically reduces the possible number of layers in the stack. Furthermore, the possible number of layers is greatly affected by the ambient temperature and the applied voltage. The performance of three hyperelastic material models for modelling the elastomer deformation are compared, and it is established that the Gent model yields the most restrictive prediction of breakdown point, while the Ogden model yields the least restrictive estimation.

The study proposed an isotopes-tagging method for investigation of reactions under the atmosphere of product gas. To illustrate this method, the calcination kinetics of calcium carbonate Ca13CO3 in CO2 atmospheres were investigated by monitoring 13CO2 produced using a micro fluidized bed reaction analyzer (MFBRA). The results demonstrated that the presence of CO2 in the reaction atmosphere increases the apparent activation energy. The increase in the apparent activation energy is, however, significantly overestimated by the TGA because of the excessive suppression by stagnated product gas inside the sample crucible. Comparatively, the results from the MFBRA are due primarily to the thermal equilibrium limitation, because the gas diffusion in the MFBRA is essentially eliminated. It is thus concluded that the MFBRA is quite capable of acquiring the real kinetics of reactions in such inhibitory atmospheres. atmosphere.

Centrifugal pumps are used in several industrial processes. It is common the operation of this equipment with gas-liquid mixtures, which is the case of the electrical submersible pumping artificial lift method used in the oil industry. The increase of free gas fraction inside the pump may lead to unstable operation and problems such as surging and gas locking phenomena to occur. In this study a drift-flux model is proposed for the gas-liquid flow subjected to centrifugal fields using the impeller as an example. The model is closed with experimental data of bubble diameter, displacements and velocities acquired via high-speed camera at several different rotational speeds and gas mass flow rates using water as the continuous medium. From the modeling and the forces balance in the bubbles, a quantitative criterion for the start point of surging and gas locking conditions was proposed.

Most cell penetrating peptides (CPPs) are unstructured and susceptible to proteolytic degradation. One alternative is to incorporate D-chirality amino acids into unstructured CPPs to allow for enhanced uptake and intracellular stability. This work investigates CPP internalization using a series of time, concentration, temperature, and energy dependent studies, resulting in a three-fold increase in uptake and 50-fold increase in stability of D-chirality peptides over L-chirality counterparts. CPP internalization occurred via a combination of direct penetration and endocytosis, with a percentage of internalized CPP expelling from cells in a time-dependent manner. Mechanistic studies identified that cells exported the intact internalized D-chirality CPPs via an exocytosis independent pathway, analogous to a direct penetration method out of the cells. These findings highlight the potential of D-chirality CPPs as bio-vectors in therapeutic and biosensing applications, but also identify a new expulsion method suggesting a relationship between uptake kinetics, intracellular stability, and export kinetics

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.

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.

At present, the development of high-performance protein imprinted materials is still a research hotspot in the field of protein imprinting. Herein, anti-protein adsorption segment (CBMA)-assisted self-driven BSA surface imprinted fibers MTCFs@SIP@CBMA with high recognition selectivity are pioneered using the strategies of combining magnetic nanomaterial surface imprinting technique with amino-Michael addition. The special structure of the carrier MTCFs endows MTCFs@SIP@CBMA with magnetic performance and self-driven adsorption performance, which simplifies the separation process while improving the adsorption capacity and accelerating the adsorption rate. The adsorption capacity for BSA reached 395.26 mg/g within 30 min. The introduction of CBMA segments on the surface after imprinting by amino-Michael addition makes its polymer chain length and position controllable. Under the strongest anti-nonspecific adsorption effect, MTCFs@SIP@CBMA exhibit excellent specific identification to BSA from mixed proteins. Additionally, MTCFs@SIP@CBMA show considerable reusability. Therefore, MTCFs@SIP@CBMA are expected to be applied in efficient separation of proteins in biological samples.

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.

Separation of higher hydrocarbons from methane is an important and energy-intensive operation in natural gas processing. We present a detailed investigation of thin and oriented MFI zeolite membranes fabricated from 2D MFI nanosheets on inexpensive α-alumina hollow fiber supports, particularly for separation of n-butane, propane, and ethane (“natural gas liquids”) from methane. The present MFI membranes display high permeances and selectivities for C2-C4 hydrocarbons over methane, driven primarily by stronger adsorption of C2-C4 hydrocarbons. We study the separation characteristics under unary, binary, ternary and quaternary mixture conditions, including the pressure dependence. The membranes are highly effective in quaternary mixture separation at elevated feed pressures, for example allowing n-butane/methane separation factors of 170–280 and n-butane permeances of 710–2700 GPU in the 1-9 bar feed pressure range. Furthermore, we parametrize and apply multicomponent Maxwell-Stefan transport equations to predict the main trends in separation behavior over a range of operating conditions.

Clarity as to the role of metal identity and oxidation state in effecting redox and acid-catalyzed turnovers is oftentimes precluded by a high degree of heterogeneity in site speciation, a limitation that can be overcome through the use of well-defined poly-metal clusters hosted by metal organic framework materials- accomplished in the present case using MIL-100(M) for the low temperature oxidation of methane with N2O. Transient kinetic data point to a) methoxy species mediating methane conversion, b) partial and deep oxidation occurring over metal sites distinct in oxidation state, c) chromium clusters amplifying the propensity toward C-C bond formation, and d) the relative velocity of propagation of water and methanol concentration fronts playing a determinative role in maximizing C2 oxygenate selectivity. The study captures the utility of using classes of materials inherently endowed with a high level of definition and uniformity in advancing the elucidation of structure-catalytic property relationships.

We present a new family of fast and robust methods for the calculation of the vapor-liquid equilibrium at isobaric-isothermal (PT-flash), isochoric-isothermal (VT-flash), isenthalpic-isobaric (HP-flash), and isoenergetic-isochoric (UV-flash) conditions. The framework is provided by formulating phase-equilibrium conditions for multi-component mixtures in an effectively reduced space based on the molar specific value of the recently introduced volume function derived from the Helmholtz free energy. The proposed algorithmic implementation can fully exploit the optimum quadratic convergence of a Newton method with the analytical Jacobian matrix. This paper provides all required exact analytic expressions for the general cubic equation of state. Computational results demonstrate the effectivity and efficiency of the new methods. Compared to conventional methods, the proposed reduced-space iteration leads to a considerable speed-up as well as to improved robustness and better convergence behavior near the spinodal and coexistence curves of multi-component mixtures, where the preconditioning by the reduction method is most effective.

In this work, based on mathematical model inspired by transition state theory, the group contribution (GC) method is used to predict the viscosity of DESs. The model is constrained by Eyring rate theory and hard sphere free volume theory. A dataset of 2229 experimental measurements of the viscosity of 183 DESs from literature is used for determining the model parameters and subsequent verification of the model. The rules introduced by this model are simple and easy to understand. The results show that the proposed model is able to predict the DESs viscosity with very high accuracy, i.e., with an average absolute relative deviation of 8.12% over the training set and 8.64% over the test set, using only temperature and composition as inputs. The maximum absolute relative deviation is 34.63%. Therefore, the as-proposed model can be considered a highly reliable tool for predicting DESs viscosity when experimental data are absent.