Modeling fixed and fluidized reactors for cassava starch saccharification with immobilized enzyme

Cassava starch saccharification in fixed-and fluidized-bed reactors using immobilized enzyme was modeled in a previous paper using a simple model in which all dextrins were grouped in a single substrate. In that case, although good fit of the model to experimental data was obtained, physical inconsistency appeared as negative kinetic constants. In this work, a multisubstrate model, developed earlier for saccharification with free enzyme, is adapted for immobilized enzyme. This latter model takes into account the formation of intermediate substrates, which are dextrins competing for the catalytic site of the enzyme, reversibility of some reactions, inhibition by substrate and product, and the formation of isomaltose. Kinetic parameters to be used with this model were obtained from initial velocity saccharification tests using the immobilized enzyme and different liquefied starch concentrations. The new model was found to be valid for modeling both fixed- and fluidized-bed reactors. It did not present inconsistencies as the earlier one had and has shown that apparent glucose inhibition is about seven times higher in the fixed-bed than in fluidized-bed reactor.

     

Modeling of Anionic Polymerization of Isoprene in an Industrial Reactor

One of the most important reasons for modeling polymerization processes is to provide a tool for estimating the risks of runaway reactions in polymer industry. This is especially important for batch processes, such as anionic polymerization of isoprene or butadiene. This work presents a theoretical and experimental research of the anionic polymerization of isoprene using cyclohexane as solvent and n‐butyllithium as initiator. In the first part, a phenomenological kinetic expression is obtained that describes the anionic polymerization of isoprene initiated by n‐butyllithium in cyclohexane. In the second, the mass and energy balance equations are solved to model the anionic polymerization of isoprene in a quasi‐adiabatic batch reactor. Adjustment of reactor parameters is made using the data obtained from a laboratory reactor. The proposed model predicts adequately the obtained temperature, pressure, and conversion profiles from this set of experiments. Finally, a mathematical model is developed to predict the behavior for the anionic polymerization of isoprene in an industrial reactor.     

The omega function for the lithium atom

The omega model is reviewed in the case of a three-electron function and the omega function determined for the lithium atom ground state by using a procedure based on Brillouin' theorem. The resulting function is analyzed in terms of natural configurations, and the correlation coefficient determined as a function of the distance to the nucleus. It is found that the omega function is essentially equivalent to two configurations built up with the occupied NO' and that the model introduces mainly correlation at large distance. These results contrast with those obtained for the beryllium atom using the same model.     

Efficient machine learning model for predicting drug-target interactions with case study for Covid-19

BackgroundDiscover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.MethodsThis paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data.ResultsThe proposed model is applied on two datasets:Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning.A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.     

Computer simulation study of water using a fluctuating charge model

Hydrogen bonding in small water clusters is studied through computer simulation methods using a sophisticated, empirical model of interaction developed by Ricket al (S W Rick, S J Stuart and B J Berne 1994J. Chem. Phys. 101 6141) and others. The model allows for the charges on the interacting sites to fluctuate as a function of time, depending on their local environment. The charge flow is driven by the difference in the electronegativity of the atoms within the water molecule, thus effectively mimicking the effects of polarization of the charge density. The potential model is thus transferable across all phases of water. Using this model, we have obtained the minimum energy structures of water clusters up to a size often. The cluster structures agree well with experimental data. In addition, we are able to distinctly identify the hydrogens that form hydrogen bonds based on their charges alone, a feature that is not possible in simulations using fixed charge models. We have also studied the structure of liquid water at ambient conditions using this fluctuating charge model.     

Estimation of Performance Characteristics of an Analytical Method Using the Data Set Of The Calibration Experiment

Abstract

A model to calculate the analytical sensitivity, limit of detection, limit of determination and precision of a method of instrumental analysis through a data set obtained by calibration experiment using the statistical analysis of linear regression, is proposed. This model has been applied to spectrophotometric, spectrofluorimetric and chromatographic methods. The values obtained are independent of the instrument used and can be applied as a criterion of comparison between different methods proposed for the same analyte. Also, these characteristics have been calculated using the IUPAC suggested model and both results have been compared.     

Mathematical modeling of gas–liquid membrane contactors using random distribution of fibers

This work presents a mathematical model for gas absorption in microporous hollow fiber membrane contactors by using a random distribution of fibers. The chemical absorption of carbon dioxide into aqueous amine solutions and sulfur dioxide into water were simulated by this model. The nonlinear mathematical expressions of the component material balance for the liquid, membrane, and gas were solved simultaneously by using a numerical method. The results from the model were compared with four sets of different experimental data in the literature. In addition, the contactors were modeled based on the assumption of regular arrangement of fibers in the shell side by using Happel's free surface as well as plug flow models. The plug flow model was employed to compare the various available equations in the literature for the shell side mass transfer coefficient. The results indicate that the channeling of gas in the shell side decreases the efficiency of contactor significantly. It was found that the random distribution of fibers is a suitable method to simulate the commercial modules. The results also indicate that, the regular Happel's free surface model and the plug flow model are more suitable for handmade modules. The influence of shell side channeling on the contactor performance were investigated in different fiber packing densities, and in various gas and liquid flow rates.     

Development of 7TM receptor-ligand complex models using ligand-biased, semi-empirical helix-bundle repacking in torsion space: application to the agonist interaction of the human dopamine D2 receptor

Prediction of 3D structures of membrane proteins, and of G-protein coupled receptors (GPCRs) in particular, is motivated by their importance in biological systems and the difficulties associated with experimental structure determination. In the present study, a novel method for the prediction of 3D structures of the membrane-embedded region of helical membrane proteins is presented. A large pool of candidate models are produced by repacking of the helices of a homology model using Monte Carlo sampling in torsion space, followed by ranking based on their geometric and ligand-binding properties. The trajectory is directed by weak initial restraints to orient helices towards the original model to improve computation efficiency, and by a ligand to guide the receptor towards a chosen conformational state. The method was validated by construction of the β₁ adrenergic receptor model in complex with (S)-cyanopindolol using bovine rhodopsin as template. In addition, models of the dopamine D₂ receptor were produced with the selective and rigid agonist (R)-N-propylapomorphine ((R)-NPA) present. A second quality assessment was implemented by evaluating the results from docking of a library of 29 ligands with known activity, which further discriminated between receptor models. Agonist binding and recognition by the dopamine D₂ receptor is interpreted using the 3D structure model resulting from the approach. This method has a potential for modeling of all types of helical transmembrane proteins for which a structural template with sequence homology sufficient for homology modeling is not available or is in an incorrect conformational state, but for which sufficient empirical information is accessible.     

Assessing the antioxidant activity of certain drugs in oxidation of emulsions

The results from developing a kinetic model for testing water-soluble antioxidants by examining the micelle formation and oxidation kinetics of water-emulsion ethyl oleate and methyl linoleate substrates are presented. The optimum composition of the system, the catalyst, and the emulsifying agent are selected. The model is tested using standard, synthetic, and natural antioxidants. Criteria for evaluating the antioxidant’s efficiency are proposed. A more complex mechanism for the action of ionol is presented, along with the possibility of using it as an external standard. The high antioxidant activity of capoten and the manifestation of antioxidant properties by α-tocopherol under selected conditions are demonstrated.     

Modeling the adsorption of U(VI) onto animal chitin using coupled mass transfer and surface complexation

This paper reports the development of a treatment system, using animal chitin as a passive biosorbent, for removing U(VI) from aqueous waste streams. An integral part of this system is a model that provides for the optimization of the treatment system through simulation of U(VI) removal efficiency based on the characteristics of the influent waste stream. The model accounts for changing solution matrix conditions through the coupling of surface complexation and mass transfer models. Complexation of U(VI) by chitin surface sites was modeled using FITEQL. Application of FITEQL in the “forward” mode provided the sorbed and aqueous phase concentrations needed for the mass transfer model. The mass transfer model was derived for both batch and continuously stirred tank reactor (CSTR) configurations using Fick's Law, reactor mass balances and rate law expressions. The coupled model was successfully validated using CSTR data at pH 6.5 and rate constants determined from batch sorption experiments. The CSTR configuration yields a steady-state, eighty percent U(VI) removal for 1 μM influent U(VI) with a solution-phase pH of 6.5 and 3.9 g l⁻¹ chitin.     

Population Balance Modeling with Coupled Agglomeration and Disintegration Processes for TiO2 Nanoparticles Formation and Experimental Validation

Particle size distribution of nanoparticles plays an important role in modelling many scientific and engineering problems. In this article, we proposed a Finite Volume Method (FVM) to model TiO₂ nanoparticles formation using population balance equations (PBEs) by incorporating the simultaneous agglomeration and disintegration processes. The superposition of the PBEs for agglomeration and disintegration with different kernels leads to a system of partial-integro differential equations, which are numerically solved by using FVM. The precipitation of TiO₂ nanoparticles in the batch reactor is studied experimentally as well as by numerical simulations based on Austin and Diemer disintegration kernels and Shear agglomeration kernel. Finally, the capability of the precipitation model is evaluated and the experimental results on particle sizes are compared with the numerical results.

     

ESCA applied to polymers. XXVI. Investigation of a series of aliphatic,aromatic, and fluorine-containing polycarbonates

The absolute and relative binding energies for the C_1s, O_1s, and F_1s core levels have been determined using x-ray photoelectron spectroscopy (ESCA) for a series of aliphatic, aromatic, and fluorine-containing polycarbonates. Comparisons of these experimentally determined, core-level binding energies with theoretical calculations using the ground-state potential model in the CNDO/2 SCF MO formalism as well as model compounds have been made on the C_1s and O_1s core levels. The degree of polymerization for low-molecular-weight fluorine-containing polycarbonates, as determined from ESCA measurements, is compared to measurements by vapor-phase osmometry and ¹⁹F-NMR.     

Simultaneous mixture analysis by using non-linear spectrophotometric data and linear iterative target transformation factor analysis

Non-linear absorption spectral data obtained from ternary mixtures of analytes are analyzed by using a linear model, iterative target transformation factor analysis (ITTFA). The use of transformed original variables is used to correct non-linearities in the original data. Absorbance below a certain limit (k) is described as linear and above this limit as non-linear. The extension of the regressor variables is the squared absorbances above the linear range. The variation of the prediction error as a function of the number of the factors and the k-values were considered and the minimum prediction error was evaluated for reaching to optimum. Except the natural non-negativity constraint the correlation constraint also is used on concentration vector in each iteration of ITTFA algorithm. The reliability of the method is evaluated using model data for ternary mixtures by spectral overlapping and different degrees of non-linearity. Simultaneous spectrophotometric determination of Eu³⁺, UO₂²⁺ and Th⁴⁺ with arsenazo III as chromogenic reagent is used as experimental model systems with non-linearity behavior of Eu³⁺and UO₂²⁺ components. The application to both synthetic and real data sets with different degrees of non-linearity demonstrate the ability of the proposed methodology to obtain better results than original data and ITTFA. The relative standard errors of prediction for proposed method in comparison with using the PLS calibration on original and extended data are nearly smaller.     

ClogPalk: a method for predicting alkane/water partition coefficient

Alkane/water partition coefficients (P_alk) are less familiar to the molecular design community than their 1-octanol/water equivalents and access to both data and prediction tools is much more limited. A method for predicting alkane/water partition coefficient from molecular structure is introduced. The basis for the ClogP_alk model is the strong (R² = 0.987) relationship between alkane/water partition coefficient and molecular surface area (MSA) that was observed for saturated hydrocarbons. The model treats a molecule as a perturbation of a saturated hydrocarbon molecule with the same MSA and uses increments defined for functional groups to quantify the extent to which logP_alk is perturbed by the introduction each functional group. Interactions between functional groups, such as intramolecular hydrogen bonds are also parameterized within a perturbation framework. The functional groups and interactions between them are specified substructurally in a transparent and reproducible manner using SMARTS notation. The ClogP_alk model was parameterized using data measured for structurally prototypical compounds that dominate the literature on alkane/water partition coefficients and then validated using an external test set of 100 alkane/water logP measurements, the majority of which were for drugs.     

Deep Oxidative Desulfurization of Thiophenic Model Oil/Natural Gas Condensate Over Tungsten/Molybdenum Oxides Using H2O2 as Oxidant

Supported Na₂WO₄/ZSM5 as catalyst was used for deep oxidative desulfurization (ODS) of mixed thiophenic compounds model oil and natural gas condensate under mild conditions by using hydrogen peroxide as oxidant. A one factor at a time optimization strategy was applied for optimizing the parameters such as temperature, loading of catalysts, reaction time, type of extractant and oxidant to S‐compounds molar ratio. The corresponding products can be easily removed from the model by using MeCN as best extractant. Results showed highly catalytic activity of Na₂WO₄/ZSM5 for the oxidative removal of dibenzothiophene and mixed thiophenic model oil under atmospheric pressure at 75 °C in a biphasic system. By applying the ODS to mixed model/MeCN and gas condensate/MeCN, the conversion reached to 94 and 81 %, respectively, using 40 % loading Na₂WO₄/ZSM5 as catalysis under the optimal conditions. To investigate the oxidation and adsorption effects of gas condensate composition on ODS, effects of cyclohexene, 1,7‐octadiene, and o‐xylene were studied with different concentrations.     

Parameterization of Models for Vapor Solubility in Semicrystalline Polyethylene

This paper extends the previous article by the authors on the solubility of hydrocarbon vapors in semicrystalline polyethylenes produced in the gas phase process. That work demonstrates a computational model for solubility based on an activity coefficient modification of the Sako–Wu–Prausnitz equation of state. In that work, by fitting a key parameter, one related to the constraint of tie chains on polymer fluid behavior, to a single isopentane solubility isotherm, accurate predictions of hydrocarbon solubility in polymer granules over a range of temperature, pressure, and composition are reported. In the present work, additional experimental solubility data are reported, an error in the authors' previous article is corrected, and a useful parameterization method that improves the predictive capabilities of the model is demonstrated. By using the model to predict much of the authors' own experimental data, as well as that published by others in the field, it is demonstrated how the proposed parameterization method allows for accurate predictions using a limited amount of experimental measurements.     

Theory on the molecular characteristic contour (I)

Based on the classical turning point of electron movement in a molecule, a model for defining the molecular characteristic boundary contour is advanced. By using an accurateab initio MELD program and an auxiliary program, some electron parameters in a molecule, such as the potential felt by an electron, have been evaluated. According to our model and definition, the molecular characteristic contour of the equilibrium geometry configuration is drawn and a vivid intuitive picture for describing the forming or breaking of a chemical bond is displayed.     

Evaluation of the Linear-Elution-Strength Model for the Prediction of Retention and Selectivity in Isothermal Gas Chromatography

Linear-elution strength theory and temperature-programmed gas chromatography is evaluated as a rapid method for predicting isothermal retention factors and column selectivity. Retention times for a wide range of compounds are determined at the program rates of 3 and 12 °C/min for the temperature range 60 to 160 °C on three open-tubular columns (DB-1701, DB-210 and EC-Wax) and used to predict isothermal retention factors for each column over the temperature range 60 to 140 °C. The temperature-program predicted isothermal retention factors are compared with experimental values using linear regression and the solvation parameter model. It is shown that isothermal retention factors predicted by the linear-elution-strength model only approximately represents the experimental data. The model fails to predict the slight curvature that exists in most plots of the experimental retention factor (log k) as a function of temperature. In addition, regression of the temperature-program predicted isothermal retention factors against the experimental values indicates that the slopes and intercepts deviate significantly from their target values of one and zero, respectively, in a manner which is temperature dependent. The temperature-program predicted isothermal retention factors result in system constants for the solvation parameter model that are different to those obtained from the experimental retention factors. These results are interpreted as indicating that linear-elution-strength theory predicts retention factors that fail to accurately model stationary phase interactions over a wide temperature range. It is concluded that temperature-program methods using linear-elution-strength theory are unsuitable for constructing system maps for isothermal separations.