A recently published approach for modeling the cross flow in an extruder channel using a new solution to the biharmonic equation is utilized in a study of chaotic mixing in a free helix single screw extruder. This novel extruder was designed and constructed with the screw flight, also referred to as the helix, detached from the screw core. Each of the screw elements could be rotated independently to obtain chaotic motion in the screw channel. Using the new extruder, experimental evidence for the increased mixing of a dye, for both a Dirac and droplet input, with a chaotic flow field relative to the traditional residence time distribution is presented. These experimental results are compared using the new biharmonic equation-based model. Because of the ability to periodically rotate only the flight/helix, the chaotic mixing results are minimally confounded by the existence of Moffat eddies.
To enhance the catalytic performance of H2SO4-catalyzed alkylation, various catalytic additives have drawn considerable attention. Herein, the effects of deep eutectic solvents additives (DESs) on catalytic performance and interfacial properties of H2SO4 alkylation were systematically investigated using experimental methods and molecular dynamics (MD) simulation. Experimental results indicate that DESs additives with the optimal concentration about 1.0 wt% can efficiently improve C8 selectivity and research octane number (RON) of alkylate. However, DESs additives contribute less to the quality of alkylate at low temperature and to the lifetime of H2SO4. MD results reveal that the phenyl molecules of DESs additives play a major role in enhancing interfacial properties of H2SO4 alkylation, including enlargement of interfacial thickness, promotion of isobutane relative solubility and diffusion to butene, which is probably the main reason for the better quality of alkylate. This work gives a good guideline for the design of novel DESs for H2SO4 alkylation.
This work explores the model predictive controller design of the continuous pulp digester process consisting of the co-current zone and counter-current zone modelled by a set of nonlinear coupled hyperbolic partial differential equations (PDE). The distributed parameter system of interest is not spectral and slow-fast dynamic separation does not hold. To address this challenge, the nonlinear continuous-time model is linearized and discretized in time utilizing the Cayley-Tustin discretization framework, which ensures system theoretic properties and structure preservation without spatial discretization or model reduction. The discrete model is used in the full state model predictive controller design, which is augmented by the Luenberger observer design to achieve the output constrained regulation. Finally, a numerical example is provided to demonstrate the feasibility and applicability of the proposed controller designs.
High dimensional models typically require a large computational overhead for multiphysics applications, which hamper their use for broad-sweeping domain interrogation. Herein, we develop a modeling framework to capture the through-plane fluid dynamic response of electrodes and flow fields in a redox flow cell, generating a computationally inexpensive two-dimensional (2D) model. We leverage a depth averaging approach that also accounts for variations in out-of-plane fluid motion and departures from Darcy’s law that arise from averaging across three-dimensions (3D). Our Resulting depth-averaged 2D model successfully predict the fluid dynamic response of arbitrary in-plane flow field geometries, with discrepancies of < 5% for both maximum velocity and pressure drop. This corresponds to reduced computational expense, as compared to 3D representations (< 1% of duration and 10% of RAM usage), providing a platform to screen and optimize a diverse set of cell geometries.
Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. We propose an uncertainty quantification method for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure is based on the multiparameter delta method from statistics, which gives the standard error of the prediction. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability.
Poly(vinyl butyral) is selected as a promising ethanol-permselective membrane based the solubility parameter theory, however it exhibits anomalous water perm-selectivity in practical pervaporation process. Comprehensive analysis based on experimental and theoretical methods were carried out to explore the inherent mechanism of the anomalous performance. Firstly, sum frequency generation vibrational spectra and contact angle were developed to quantify the surface reconstruction of membrane in air and ethanol, which indicated that hydrophilic hydroxyl tended to expose on membrane surface with ethanol thus improved the membrane affinity to water. Meanwhile, swelling behaviors proved more water would accumulate in the ethanol swollen membrane. Furthermore, theoretical analysis in terms of sorption and diffusion process, based on the UNIFAC-FV model and Fujita free volume theory, confirmed the mechanism of anomalous phenomenon of poly(vinyl butyral) membrane. The comprehensive investigation was expected to provide insights into the basic separation mechanism of pervaporation process.
An integrated metal-organic framework (MOF) and pressure/vacuum swing adsorption (P/VSA) process design framework is presented for gas separation. It consists of two steps: descriptor optimization and MOF matching. In the first step, MOFs are represented as a large set of chemical and geometric descriptors from which the most influential ones are selected and treated as design variables. The valid design space is confined using a tailored classifier model and logic constraints. Based on collected adsorption isotherms of 471 different MOFs, data-driven isotherm models are developed. Combining the design space, isotherms, and process models, an integrated MOF and P/VSA process design problem is formulated. MOF descriptors and process operating conditions are optimized to maximize the process performance. The obtained optimal descriptors and isotherms can be used to guide the discovery of high-performance MOFs in a subsequent MOF matching step. This article addresses the first descriptor optimization step exemplified by propene/propane separation.
This article focuses on the catalytic hydrodenitrogenation (HDN) mechanism of indole under hydrothermal conditions. Both gaseous hydrogen and liquid hydrogen donor formic acid (FA) can improve indole conversion and total yield of denitrogenated products. Ru/C showed the highest activity among the catalysts for indole conversion in all temperature conditions with the existence of H2 and 91.17 % indole was converted at 400 °C and 60 min. Based on reaction kinetic experiments, a kinetic model was developed to describe the hydrothermal HDN reaction of indole over the home-made Ni80Ru20/γ-Al2O3 catalyst, which clearly captured all data trends and fitted the temporal variation of all major liquid products. High activation energy for formation of O-containing substance o-cresol from both mathematical fitting and density functional theory (DFT) calculation indicated a rare occurrence of reaction between pyrrole ring-opening product methyl aniline and H2O, consistent with experimental observation that only a trace of o-cresol was detected.
This study presents the chromatographic purification of immunoglobulin G (IgG) from human plasma using a two-column process integrating the peptide-based adsorbents LigaGuardTM, which captures non-Ig plasma proteins in flow-through mode, and LigaTrapTM, which isolates IgG in bind-and-elute. Buffer composition and column loading were optimized for both adsorbents. Two process configurations were evaluated. In the first design, plasma was fed to a LigaGuardTM column to capture plasma proteins, the effluent was loaded on the LigaTrapTM column, and the bound IgG was eluted with 63.8% global recovery and 99.7% purity; in comparison, Protein G agarose afforded ~67% recovery and 97.2% purity. In the alternative design, the LigaGuardTM column was utilized to polish the LigaTrapTM elution stream, affording 82.3% global recovery and 98.8% purity. Collectively, these results demonstrate the potential of a fully chromatographic process for purifying polyclonal IgG from plasma feedstocks.
Nanomaterial drug delivery systems have become one of the most important targeted therapy technologies. Although great efforts have been made to study the self-assembled mesoscopic structure of nanoparticles and understand drug loading and release mechanisms, the interaction between nanoparticles and cell membranes has not yet been clearly studied. Moreover, the research of experimental methods in this field has been greatly restricted due to its special time-space scale, so it is necessary to apply computer simulations to visualize the cell internalization of the nanoparticle. This review covers modelling methods and the current status and viewpoints of research on the influencing factors of the nanoparticle-biomembrane interaction mechanism. In particular, we discussed in detail the positive and negative effects of various nanoparticle properties. This article may assist researchers in rationally optimize the nanoparticle structure to improve therapeutic efficiency.
The advanced use of a pH-responsive biomaterial-based injectable liquid implant for effective chemotherapeutic delivery in glioblastoma multiforme brain (GBM) tumour treatment is presented. As an implant, we proposed a water-in-oil-in-water multiple emulsion with encapsulated doxorubicin. The effectiveness of the proposed therapy was evaluated by comparing the cancer cell viability achieved in classical therapy (chemotherapeutic solution). The experimental study included doxorubicin release rates and consumption for two emulsions differing in drop sizes and structures in the presence of GBM-cells (LN229, U87 MG), and a cell viability. The results showed that the multiple emulsion implant was significantly more effective than classical therapy when considering the reduction in cancer cell viability: 85% for the emulsion-implant, and only 43% for the classical therapy. A diffusion-reaction model was adapted to predict doxorubicin release kinetics and elimination by glioblastoma cells. CFD simulations confirmed that the drug release kinetics depends on multiple emulsion structures and drop sizes.
We develop a simulation toolset employing density functional theory (DFT) in conjunction with grand canonical Monte Carlo (GCMC) to study coke formation on Fe-based catalysts during propane dehydrogenation (PDH). As expected, pure Fe surfaces develop stable graphitic coke structures and rapidly deactivate. We find that coke formation is markedly less favorable on Fe3C and FeS surfaces. Fe-Al alloys display varying degrees of coke resistance, depending on their composition, suggesting that they can be optimized for coke resistance under PDH conditions. Electronic structure analyses show that both electron-withdrawing effects (on Fe3C and FeS) and electron-donating effects (on Fe-Al alloys) destabilize adsorbed carbon. On the alloy surfaces, a geometric effect also isolates Fe sites and disrupts the formation of graphitic carbon networks. This work demonstrates the utility of GCMC for studying the formation of disordered phases on catalyst surfaces and provides insights for improving the coke resistance of Fe-based PDH catalysts.
The Bayer process holds an exclusive status for alumina extraction, but a massive amount of caustic “red mud” waste is generated. In this work, three oxalate reagents: potassium hydrogen oxalate (KHC2O4), potassium tetraoxalate (KHC2O4·H2C2O4), and oxalic acid (H2C2O4) were investigated for the Al and Fe extraction process from NIST SRM 600 – Australian Darling range bauxite ore. More than 90% of Al and Fe was extracted into the aqueous phase in less than 2 h with 0.50 M C2O42- for all three reagents. The Fe and Al can be selectively precipitated by hydrolyzing the aqueous phase. By acidifying the Al and Fe free filtrate, 80% of the C2O42- can be precipitated as KHC2O4·H2C2O4. Greater than 90% of the aqueous acid can also be recycled using a cation exchange resin. The proposed closed-loop process is an energy-efficient, cost-effective, environmentally-friendly route for extracting Al and Fe from bauxite ore.
Extrusion-based 3D printing of polymeric biomaterials has emerged as a promising approach for the fabrication of complex tissue engineering constructs. However, the large pore and feature size lead to low cell seeding efficiency and limited control of spatial distribution of cells within the scaffolds. We developed hybrid scaffolds that are composed of 3D printed layers and airbrushed fibrous membranes. Airbrushing time was adjusted to fabricate low (L), medium (M), and high (H) density membranes to effectively control stem cell infiltration. When two distinct populations of stem cells were seeded from top or bottom of the scaffolds, scaffolds composed of LLL membranes showed gradual mixing of the cells with depth whereas LHL membranes led to two distinct regions of cells separated by the H membrane. Our results demonstrate that fibrous membranes incorporated within 3D printed layers enable user-defined and spatially controlled cell compositions within hybrid scaffolds.
Otitis Media (OM) is the most common reason for U.S. children to receive prescribed oral antibiotics, leading to potential to cause antibiotic resistance. To minimize oral antibiotic usage, we developed polyvinylpyrrolidone-coated silver nanoparticles (AgNPs-PVP), which completely eradicated common OM pathogens, i.e., Streptococcus pneumoniae and non-typeable Haemophilus influenzae (NTHi) at 1.04µg/mL and 2.13µg/mL. The greater antimicrobial efficacy against S. pneumoniae was a result of the H2O2-producing ability of S. pneumoniae and the known synergistic interactions between H2O2 and AgNPs. To enable the sustained local delivery of AgNPs-PVP (e.g., via injection through perforated tympanic membranes), a hydrogel formulation of 18%(w/v)P407 was developed. Reverse thermal gelation of the AgNPs-PVP-P407 hydrogel could gel rapidly upon entering the warm auditory bullae and thereby sustained release of antimicrobials. This hydrogel-based local delivery system completely eradicated OM pathogens in vitro without cytotoxicity, and thus represents a promising strategy for treating bacterial OM without relying on conventional antibiotics.
Emulsion electrospinning represents a tunable system for the fabrication of porous scaffolds for controlled, localized drug delivery in tissue engineering applications. This study aimed to elucidate the role of model drug interactions with emulsion chemistry on loading and release rates from fibers with controlled fiber diameter and fiber volume fraction. Nile Red and Rhodamine B were used as model drugs and encapsulation efficiency and release rates were determined from poly(caprolactone) (PCL) electrospun fibers spun either with no surfactant (Span 80), surfactant, or water-in-oil emulsions. Drug loading efficiency and release rates were modulated by both surfactant and aqueous internal phase in the emulsions as a function of drug molecule hydrophobicity. Overall, these results demonstrate the role of intermolecular interactions and drug phase solubility on the release from emulsion electrospun fibers and highlight the need to independently control these parameters when designing fibers for use as tunable drug delivery systems.
Hydrophobic deep eutectic solvents (DESs) emerge as candidates to extract organic substrates from aqueous solutions. The DES-aqueous liquid-liquid interface plays a vital role in the extraction ability of hydrophobic DES because the non-bulk structure of molecules at the interface could cause thermodynamic and kinetic barriers. One question is how the DES compositions affect the structural features of the DES-aqueous liquid-liquid interface. We investigate the density profile, dipole moment and hydrogen bonds of eight hydrophobic DES-aqueous liquid-liquid interfaces using molecular dynamics simulations. The eight DESs are composed of four organic compounds: decanoic acid, menthol, thymol, and lidocaine. The simulation results show the variations of dipole moment and hydrogen bond structure and dynamics at the liquid-liquid interfaces. Such variations could influence the extraction ability of DES through adjusting the partition and kinetics of organic substrates in the DES-aqueous biphasic systems.