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Membrane rejection of nitrogen compounds

Rejection characteristics of nitrogen compounds were examined for reverse osmosis, nanofiltration, and low-pressure reverse osmosis membranes. The rejection of nitrogen compounds is explained by integrating experimental results with calculations using the extended Nernst-Planck model coupled with a steric hindrance model. The molecular weight and chemical structure of nitrogen compounds appear to be less important in determining rejection than electrostatic properties. The rejection is greatest when the Donnan potential exceeds 0.05 V or when the ratio of the solute radius to the pore radius is greater than 0.8. The transport of solute in the pore is dominated by diffusion, although convective transport is significant for organic nitrogen compounds. Electromigration contributes negligibly to the overall solute transport in the membrane. Urea, a small organic compound, has lower rejection than ionic compounds such as ammonium, nitrate, and nitrite, indicating the critical role of electrostatic interaction in rejection. This suggests that better treatment efficiency for organic nitrogen compounds can be obtained after ammonification of urea.

NASA Discipline Life Support Systems↗

A universal steady state I-V relationship for membrane current

A purely electrical mechanism for the gating of membrane ionic channel gives rise to a simple I-V relationship for membrane current. Our approach is based on the known presence of gating charge, which is an established property of the membrane channel gating. The gating charge is systematically treated as a polarization of the channel protein which varies with the external electric field and modifies the effective potential through which the ions migrate in the channel. Two polarization effects have been considered: 1) the up or down shift of the whole potential function, and 2) the change in the effective electric field inside the channel which is due to familiar effect of the effective reduction of the electric field inside a dielectric body because of the presence of surface charges on its surface. Both effects are linear in the channel polarization. The ionic current is described by a steady state solution of the Nernst-Planck equation with the potential directly controlled by the gating charge system. The solution describes reasonably well the steady state and peak-current I-V relationships for different channels, and when applied adiabatically, explains the time lag between the gating charge current and the rise of the ionic current. The approach developed can be useful as an effective way to model the ionic currents in axons, cardiac cells and other excitable tissues.

NASA Discipline Regulatory Physiology↗

Removal of organic contaminants by RO and NF membranes

Rejection characteristics of organic and inorganic compounds were examined for six reverse osmosis (RO) membranes and two nanofiltration (NF) membranes that are commercially available. A batch stirred-cell was employed to determine the membrane flux and the solute rejection for solutions at various concentrations and different pH conditions. The results show that for ionic solutes the degree of separation is influenced mainly by electrostatic exclusion, while for organic solutes the removal depends mainly upon the solute radius and molecular structure. In order to provide a better understanding of rejection mechanisms for the RO and NF membranes, the ratio of solute radius (r(i,s)) to effective membrane pore radius (r(p)) was employed to compare rejections. An empirical relation for the dependence of the rejection of organic compounds on the ratio r(i,s)/r(p) is presented. The rejection for organic compounds is over 75% when r(i,s)/r(p) is greater than 0.8. In addition, the rejection of organic compounds is examined using the extended Nernst-Planck equation coupled with a steric hindrance model. The transport of organic solutes is controlled mainly by diffusion for the compounds that have a high r(i,s)/r(p) ratio, while convection is dominant for compounds that have a small r(i,s)/r(p) ratio. c2005 Elsevier B.V. All rights reserved.

NASA Discipline Life Sciences Technologies↗

Molecular Dynamics Simulation of the Antiamoebin Ion Channel: Linking Structure and Conductance

Molecular dynamics simulations were carried out in order to ascertain which of the potential multimeric forms of the transmembrane peptaibol channel, antiamoebin, is consistant with its measured conductance. Estimates of the conductance obtained through counting ions that cross the channel and by solving the Nernst-Planck equation yield consistent results, indicating that the motion of ions inside the channel can be satisfactorily described as diffusive.The calculated conductance of octameric channels is markedly higher than the conductance measured in single channel recordings, whereas the tetramer appears to be non-conducting. The conductance of the hexamer was estimated to be 115+/-34 pS and 74+/-20 pS, at 150 mV and 75 mV, respectively, in satisfactory agreement with the value of 90 pS measured at 75 mV. On this basis we propose that the antiamoebin channel consists of six monomers. Its pore is large enough to accommodate K(+) and Cl(-) with their first solvation shells intact. The free energy barrier encountered by K(+) is only 2.2 kcal/mol whereas Cl(-) encounters a substantially higher barrier of nearly 5 kcal/mol. This difference makes the channel selective for cations. Ion crossing events are shown to be uncorrelated and follow Poisson statistics. keywords: ion channels, peptaibols, channel conductance, molecular dynamics

Wilson, Michael A.↗

Conductance of Ion Channels - Theory vs. Experiment

Transmembrane ion channels mediate a number of essential physiological processes in a cell ranging from regulating osmotic pressure to transmission of neural signals. Kinetics and selectivity of ion transport is of critical importance to a cell and, not surprisingly, it is a subject of numerous experimental and theoretical studies. In this presentation we will analyze in detail computer simulations of two simple channels from fungi - antiamoebin and trichotoxin. Each of these channels is made of an alpha-helical bundle of small, nongenomically synthesized peptides containing a number of rare amino acids and exhibits strong antimicrobial activity. We will focus on calculating ionic conductance defined as the ratio of ionic current through the channel to applied voltage. From molecular dynamics simulations, conductance can be calculated in at least two ways, each involving different approximations. Specifically, the current, given as the number of charges transferred through the channel per unit of time, can be obtained from the number of events in which ions cross the channel during the simulation. This method works well for large currents (high conductance values and/or applied voltages). If the number of crossing events is small, reliable estimates of current are difficult to achieve. Alternatively, conductance can be estimated assuming that ion transport can be well approximated as diffusion in the external potential given by the free energy profile. Then, the current can be calculated by solving the one-dimensional diffusion equation in this external potential and applied voltage (the generalized Nernst-Planck equation). To do so three ingredients are needed: the free energy profile, the position-dependent diffusion coefficient and the diffusive flux of ions into the channel. All these quantities can be obtained from molecular dynamics simulations. An important advantage of this method is that it can be used equally well to estimating large and small currents. In addition, once the free energy profile becomes available the full current-voltage dependence can be readily obtained. For both channels we carried out calculations using both approaches. We also tested the main assumptions underlying the diffusive model, such as uncorrelated nature of individual crossing events and Fickian diffusion. The accuracy and consistency of different methods will be discussed. Finally we will discuss how comparisons between calculated and measured ionic conductance and selectivity of transport can be used for determining structural models of the channels.

Pohorille, Andrew↗

Multiphysics Modeling of Electrode Dissolution under Hydrodynamic Conditions in Low Conductivity Water

Efficient plug-flow electrochemical reactors are characterized by their ability to effectively convert species with significant ionic strength and/or with the assistance of a well-behaved electrolyte. Because of the classic electroconductivity requirement, electrochemical reactors are not particularly attractive options for applications with low-conductivity conditions, especially for low-pressure feeds. However, these reactors can still find application in dedicated processes such as ultra-clean water conditioning and biological-coupled separation systems. Consequently, a multi-physics model was developed using COMSOL Multiphysics to characterize different reactor-design conditions based on the Tertiary Current Distribution which accounts for the effects of expected variations in electrolyte composition and ionic strength on the electrochemical process, as well as solution resistance and electrode kinetics. The modelling framework presented here employs single-phase laminar fluid flow, the Nernst-Planck equation, the water-based electroneutrality condition, and concentration-dependent overpotentials. This investigation particularly explores the electrolysis process of “anodic dissolution,” in which elemental species dissociate from the solid matrix of the electrode and enter the liquid phase of the electrolyte as soluble ions. The reactor configuration of interest has a rectangular parallel-electrode design and is operated galvanostatically. A series of parametric studies are carried out to inspect the response of the system when hydrodynamic, kinetic, and geometric conditions are changed. The parametric sweep resulted in variable effluent concentrations and system voltage, which reveal underlaying optimization patterns for electrode dissolution in low conductivity water.

Multiphysics↗