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Chemical warfare agents that are liquids with low vapor pressure pose a contact hazard to anyone who encounters them. Personal protective equipment (PPE) is utilized to ensure safe interaction with these agents. A commonly used method to characterize the permeability of PPE towards chemical weapons is to apply droplets of the liquid agent to the surface of the material and measure for chemical breakthrough. However, this method could produce errors in the estimated values of the transport properties. In this paper, we solved numerically the three-dimensional cylindrical Fick’s second law of diffusion for a liquid permeating through a non-porous rubbery membrane to determine the time the permeating species will emerge on the other side of the polymer membrane. Simulations of different amounts of surface area coverage and the geometries of permeate on the membrane surface indicated that incomplete surface area coverage affects the estimation of the transport properties, making the experimentally determined transport properties unsuitable for predictive use. We simulated different permeation values to determine the factors that most influenced the estimation error and if the error was consistent over different permeate–membrane combinations. Finally, a method to correct the experimentally determined permeability is suggested.

In the packaging industry, the barrier property of packaging materials is of paramount importance. The enhancement of barrier properties of materials can be achieved by adding impermeable nanoparticles into thin polymeric films, known as mixed-matrix membranes (MMMs). Three-dimensional numerical simulations were performed to study the barrier property of these MMMs and to estimate the effective membrane gas permeability. Results show that horizontally-aligned thin cuboid nanoparticles offer far superior barrier properties than spherical nanoparticles for an identical solid volume fraction. Maxwell’s model predicts very well the relative permeability of spherical and cubic nanoparticles over a wide range of the solid volume fraction. However, Maxwell’s model shows an increasingly poor prediction of the relative permeability of MMM as the aspect ratio of cuboid nanoparticles tends to zero or infinity. An artificial neural network (ANN) model was developed successfully to predict the relative permeability of MMMs as a function of the relative thickness and the relative projected area of the embedded nanoparticles. However, since an ANN model does not provide an explicit form of the relation of the relative permeability with the physical characteristics of the MMM, a new model based on multivariable regression analysis is introduced to represent the relative permeability in a MMM with impermeable cuboid nanoparticles. The new model possesses a simple explicit form and can predict, very well, the relative permeability over an extensive range of the solid volume fraction and aspect ratio, compared with many existing models.

The use of mixed matrix membranes (MMMs) to facilitate the production of biofuels has attracted significant research interest in the field of renewable energy. In this study, the pervaporation separation of butanol from aqueous solutions was studied using a series of MMMs, including zeolitic imidazolate frameworks (ZIF-8)-polydimethylsiloxane (PDMS) and zinc oxide-PDMS mixed matrix membranes. Although several studies have reported that mixed matrix membranes incorporating ZIF-8 nanoparticles showed improved pervaporation performances attributed to their intrinsic microporosity and high specific surface area, an in-depth study on the role of ZIF-8 nanoparticle size in MMMs has not yet been reported. In this study, different average sizes of ZIF-8 nanoparticles (30, 65, and 80 nm) were synthesized, and the effects of particle size and particle loading content on the performance of butanol separation using MMMs were investigated. Furthermore, zinc oxide nanoparticles, as non-porous fillers with the same metalcore as ZIF-8 but with a very different geometric shape, were used to illustrate the importance of the particle geometry on the membrane performance. Results showed that small-sized ZIF-8 nanoparticles have better permeability and selectivity than medium and large-size ZIF-8 MMMs. While the permeation flux increased continuously with an increase in the loading of nanoparticles, the selectivity reached a maximum for MMM with 8 wt% smaller-size ZIF-8 nanoparticle loading. The flux and butanol selectivity increased by 350% and 6%, respectively, in comparison to those of neat PDMS membranes prepared in this study.

Recent years have seen the explosive development of mixed-matrix membranes (MMMs) for a myriad of applications. In gas separation, it is desired to concurrently enhance the permeability, selectivity and physicochemical properties of the membrane. To help achieving these objectives, experimental characterization and predictive models can be used synergistically. In this investigation, a Monte Carlo (MC) algorithm is proposed to rapidly and accurately estimate the relative permeability of ideal MMMs over a wide range of conditions. The difference in diffusivity coefficients between the polymer matrix and the filler particle is used to adjust the random progression of the migrating species inside each phase. The solubility coefficients of both phases at the polymer–filler interface are used to control the migration of molecules from one phase to the other in a way to achieve progressively phase equilibrium at the interface. Results for various MMMs were compared with the results obtained with the finite difference method under identical conditions, where the results from the finite difference method are used in this investigation as the benchmark method to test the accuracy of the Monte Carlo algorithm. Results were found to be very accurate (in general, <1% error) over a wide range of polymer and filler characteristics. The MC algorithm is simple and swift to implement and provides an accurate estimation of the relative permeability of ideal MMMs. The MC method can easily be extended to investigate more readily non-ideal MMMs with particle agglomeration, interfacial void, polymer-chain rigidification and/or pore blockage, and MMMs with any filler geometry.

Membrane-based processes are considered a promising separation method for many chemical and environmental applications such as pervaporation and gas separation. Numerous polymeric membranes have been used for these processes due to their good transport properties, ease of fabrication, and relatively low fabrication cost per unit membrane area. However, these types of membranes are suffering from the trade-off between permeability and selectivity. Mixed-matrix membranes, comprising a filler phase embedded into a polymer matrix, have emerged in an attempt to partly overcome some of the limitations of conventional polymer and inorganic membranes. Among them, membranes incorporating tubular fillers are new nanomaterials having the potential to transcend Robeson’s upper bound. Aligning nanotubes in the host polymer matrix in the permeation direction could lead to a significant improvement in membrane permeability. However, although much effort has been devoted to experimentally evaluating nanotube mixed-matrix membranes, their modelling is mostly based on early theories for mass transport in composite membranes. In this study, the effective permeability of mixed-matrix membranes with tubular fillers was estimated from the steady-state concentration profile within the membrane, calculated by solving the Fick diffusion equation numerically. Using this approach, the effects of various structural parameters, including the tubular filler volume fraction, orientation, length-to-diameter aspect ratio, and permeability ratio were assessed. Enhanced relative permeability was obtained with vertically aligned nanotubes. The relative permeability increased with the filler-polymer permeability ratio, filler volume fraction, and the length-to-diameter aspect ratio. For water-butanol separation, mixed-matrix membranes using polydimethylsiloxane with nanotubes did not lead to performance enhancement in terms of permeability and selectivity. The results were then compared with analytical prediction models such as the Maxwell, Hamilton-Crosser and Kang-Jones-Nair (KJN) models. Overall, this work presents a useful tool for understanding and designing mixed-matrix membranes with tubular fillers.

Process optimization using a physical process or its comprehensive model often requires a significant amount of time. To remedy this problem, metamodels, or surrogate models, can be used. In this investigation, a methodology for optimizing the biobutanol production process via the integrated acetone–butanol–ethanol (ABE) fermentation–membrane pervaporation process is proposed. In this investigation, artificial neural networks (ANNs) were used as metamodels in an attempt to reduce the time needed to circumscribe the Pareto domain and identify the best optimal operating conditions. Two different metamodels were derived from a small set of operating conditions obtained from a uniform experimental design. The first series of metamodels were derived to entirely replace the phenomenological model of the butanol fermentation process by representing the relationship that exists between five operating conditions and four performance criteria. The second series of metamodels were derived to estimate the initial concentrations under steady-state conditions for the eight chemical species within the fermenter in order to expedite convergence of the process simulator. The first series of metamodels led to an accurate Pareto domain and reduced the computation time to circumscribe the Pareto domain by a factor of 2500. The second series of metamodels led to only a small reduction of computation time (a factor of approximately 2) because of the inherently slow convergence of the overall fermentation process.

The pervaporation separation of organic compounds from acetone-butanol-ethanol (ABE) fermentation model solutions was studied using activated carbon (AC) nanoparticle-poly (dimethylsiloxane) (PDMS) mixed matrix membranes (MMM). The effects of the operating conditions and nanoparticle loading content on the membrane performance have been investigated. While the separation factor increased continuously, with an increase in the concentration of nanoparticles, the total flux reached a maximum in the MMM with 8 wt % nanoparticle loading in PDMS. Both the separation factor for ABE and the total permeation flux more than doubled for the MMM in comparison to those of neat PDMS membranes prepared in this study.

One of the important challenges in food industries is to achieve sufficient gas barrier properties for packaging films. Films made of polyvinyl alcohol (PVA) are commonly used for food packaging and are sometimes used with embedded nanoparticles. In this investigation, PVA nanocomposite films were prepared using solution-casting method with different concentrations of montmorillonite (MMT) and titanium oxide (TiO2) nanoparticles. A response surface methodology (RSM), based on three-level factorial design, was implemented to model and optimize the effect of the concentrations of the nanofillers on the barrier properties of thin nanocomposite films. The viscosity of the polymer-forming solution increased when nanoparticles were incorporated in the polymer matrix. SEM micrographs showed a good distribution of nanofillers at low concentration whereas some aggregation was observed at higher nanofiller loadings. Transparency of PVA-based thin films decreased with an increase of TiO2/MMT loading. A significant increase in the Young ̓s modulus occurred with an increase in the loading of nanoparticles whereas the tensile strength and elongation at the breakpoint both decreased. Results for PVA/MMT/TiO2 nanocomposite films showed a decrease in the oxygen transmission rate and water vapor permeability compared to a neat PVA membrane. The particle loading leading to optimum barrier properties for nanocomposite films was a combined loading of 1 wt% TiO2 and 4 wt% MMT.Graphic Abstract

Nanocomposite film performance parameters, including barrier and mechanical properties for packaging films, can be affected by variables such as the type and the concentration of nanoparticles. In food packaging, it is desired to develop optimal films to maintain the freshness of food for longer periods. In this investigation, polyvinyl alcohol (PVA) nanocomposite films were prepared by solution casting method with different combinations of Montmorillonite (MMT) platelets and titanium oxide (TiO2) spherical nanoparticles. A support vector machine (SVM) was implemented to study the thin nanocomposite films’ behavior to changes in the independent variables. The SVM model predicted oxygen transmission rate (OTR), water vapor permeability (WVP|), Young ̓s Modulus (YM), total color difference (ΔE), opacity, tensile strength (TS), and elongation at the breakpoint (EB) with an error of less than 6.4%. A Genetic Algorithm (GA) was applied to find the optimal nanoparticle concentration to achieve the optimum film performance. Results have clearly shown that the optimum film performance depends on the type and the concentration of nanoparticles. In this investigation, results showed that the optimum loading of nanoparticles should be between 0.5 and 1.0 wt% for TiO2 and 2.5–3.5 wt% for MMT.

A rotational reciprocating plate impeller prototype, designed to improve the mixing homogeneity of viscous non-Newtonian fermentation broth, has been tested in pullulan fermentations. With this new impeller, the operating levels of several factors were investigated to improve pullulan production with Aureobasidium pullulans ATCC 42023 in a 22-L bioreactor using experimental designs. Because both high molecular weight (MW) and high concentration of pullulan were desired; the exopolysaccharide (EPS) concentration and the broth viscosity were used as optimization objective functions to be maximized. A 6-run uniform design was used to investigate five factors. Under the best operating conditions among the six runs, 29.0 g L −1 EPS was produced at 102 h. This condition was used as the starting point for further investigation on the two statistically significant factors, the pH and the agitation speed. An 8-run 3-level custom design that investigates up to second-order effects was used in the second stage. An optimal zone of operating conditions for large quantity of high MW pullulan production was identified. A concentration of 23.3 g L −1 EPS was produced at 78 h. This is equivalent to an EPS productivity of 0.30 g L −1  h −1 . The corresponding apparent viscosity of the broth was 0.38 Pa s at the shear rate of 10 s −1 .