2015
DOI: 10.1002/elan.201400584
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Generalized Diffusion Parameter: Main Factors of Influence and Application for Estimation of Selectivity Coefficients for Highly Selective Electrodes

Abstract: The influence of membrane composition, ion nature, measuring time and stirring mode on the generalized diffusion parameter q was investigated. It was found that the main factors of influence are PVC to plasticizer ratio and measuring time, which obey rigid regularities and can be estimated. It makes possible to evaluate the role of the diffusion factor for other systems and measuring conditions using some averaged q value determined for a limited set of systems. It was shown that averaged q value allowed relia… Show more

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Cited by 8 publications
(3 citation statements)
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“…For inward ion fluxes, a modification of the protocol by extrapolating to logarithmic selectivity coefficients was proposed for best results. Egorov’s group then evaluated the influence of the membrane composition on the diffusion parameter q (called by other authors the permeability ratio), which has direct bearing on the determination of selectivity coefficients by extrapolation . Here, the parameter was obtained by a formula that compares the unbiased selectivity coefficient with the experimentally observed one, along with the membrane composition.…”
Section: Potentiometrymentioning
confidence: 99%
See 1 more Smart Citation
“…For inward ion fluxes, a modification of the protocol by extrapolating to logarithmic selectivity coefficients was proposed for best results. Egorov’s group then evaluated the influence of the membrane composition on the diffusion parameter q (called by other authors the permeability ratio), which has direct bearing on the determination of selectivity coefficients by extrapolation . Here, the parameter was obtained by a formula that compares the unbiased selectivity coefficient with the experimentally observed one, along with the membrane composition.…”
Section: Potentiometrymentioning
confidence: 99%
“…Egorov's group then evaluated the influence of the membrane composition on the diffusion parameter q (called by other authors the permeability ratio), which has direct bearing on the determination of selectivity coefficients by extrapolation. 51 Here, the parameter was obtained by a formula that compares the unbiased selectivity coefficient with the experimentally observed one, along with the membrane composition. As the diffusion layer in the membrane expands with time, so does the diffusion parameter.…”
mentioning
confidence: 99%
“…It is well-known that the diffusion processes, controlling the rate of ion delivery from the bulk of the membrane and solution to the interface and vice versa, have a significant impact on the response of conventional ion-selective electrodes (ISEs), especially highly selective ones. The consideration of the diffusion processes’ influence on the ISEs response, at a qualitative level, suggests various techniques for controlling the intensity of ion fluxes and direction by means of optimizing the design of ISEs, membrane composition, and inner filling solution as well as using special algorithms of membrane conditioning (including impulse galvanostatic polarization ) and measurements protocols. , As a result, the analytical characteristics of ISEs, including lower detection limit and selectivity, were drastically improved, and the application field of ISEs was significantly expanded. Furthermore, it was shown that some electrodes provide an analytically significant response only under the condition of the diffusion control of the potential; in particular, the functioning of polyion sensors for determination of heparin and protamine is based on this discovery. It was found that diffusion processes at the membrane–sample solution interface can lead to strong potential drift for highly selective electrodes in the solutions of foreign ions. On the other hand, progress in computer science and in methods of numerical simulations have promoted the development of electrode potential models, , which are characterized by a different level of complexity and demonstrate a certain predictive ability. In some cases, these models can be used to quantitatively estimate the influence of the change in design of ISEs, measurements conditions, and protocols for the main analytical parameters and response characteristics of ISEs in a way which is faster and cheaper than by performing of the experiments.…”
mentioning
confidence: 99%