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Resolving Anomalies in Predicting Electrokinetic Energy Conversion Efficiencies of Nanofluidic Devices.

Majumder S, Dhar J, Chakraborty S - Sci Rep (2015)

Bottom Line: We devise a new approach for capturing complex interfacial interactions over reduced length scales, towards predicting electrokinetic energy conversion efficiencies of nanofluidic devices.By embedding several aspects of intermolecular interactions in continuum based formalism, we show that our simple theory becomes capable of representing complex interconnections between electro-mechanics and hydrodynamics over reduced length scales.The present model, thus, may be employed to rationalize the discrepancies between low energy conversion efficiencies of nanofluidic channels that have been realized from experiments, and the impractically high energy conversion efficiencies that have been routinely predicted by the existing theories.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Indian Institute of Technology Kharagpur Kharagpur 721302, INDIA.

ABSTRACT
We devise a new approach for capturing complex interfacial interactions over reduced length scales, towards predicting electrokinetic energy conversion efficiencies of nanofluidic devices. By embedding several aspects of intermolecular interactions in continuum based formalism, we show that our simple theory becomes capable of representing complex interconnections between electro-mechanics and hydrodynamics over reduced length scales. The predictions from our model are supported by reported experimental data, and are in excellent quantitative agreement with molecular dynamics simulations. The present model, thus, may be employed to rationalize the discrepancies between low energy conversion efficiencies of nanofluidic channels that have been realized from experiments, and the impractically high energy conversion efficiencies that have been routinely predicted by the existing theories.

No MeSH data available.


Electrokinetic Conversion efficiency against , estimated from different modeling considerations, for (a) hydrophilic surface and (b) hydrophobic surface.<Inset> Corresponding dimensionless streaming potentials predicted from the present model. The solid lines represent the predictions from the present model, with all the addressed effects taken into consideration. The dashed lines represent model predictions where only the viscosity jump is replaced by Navier slip based hydrodynamic boundary condition and keeping rest of the conditions same (see main text below for details). The dash-dot lines represent the predictions from the classical PB formalism. The markers represent MD simulation data. (a) The diamond markers corresponds to data reported in ref. 3, whereas the square marker corresponds to data reported in ref. 64 Other values chosen are α = −1.5, v = 10−4, ydds = 0.10 nm, ys = 0.30 nm,  and . (b) The MD results (square markers) were obtained from a single simulation (ref. 7). Other values chosen are α = 1, v = 10−4, ydds = 0.12 nm, ys = 0.15 nm,  and . Credited authors: S. Majumder, J. Dhar and S. Chakraborty.
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f5: Electrokinetic Conversion efficiency against , estimated from different modeling considerations, for (a) hydrophilic surface and (b) hydrophobic surface.<Inset> Corresponding dimensionless streaming potentials predicted from the present model. The solid lines represent the predictions from the present model, with all the addressed effects taken into consideration. The dashed lines represent model predictions where only the viscosity jump is replaced by Navier slip based hydrodynamic boundary condition and keeping rest of the conditions same (see main text below for details). The dash-dot lines represent the predictions from the classical PB formalism. The markers represent MD simulation data. (a) The diamond markers corresponds to data reported in ref. 3, whereas the square marker corresponds to data reported in ref. 64 Other values chosen are α = −1.5, v = 10−4, ydds = 0.10 nm, ys = 0.30 nm, and . (b) The MD results (square markers) were obtained from a single simulation (ref. 7). Other values chosen are α = 1, v = 10−4, ydds = 0.12 nm, ys = 0.15 nm, and . Credited authors: S. Majumder, J. Dhar and S. Chakraborty.

Mentions: Fig. 5 describes the values of the energy conversion efficiencies considering different modeling approaches, as a function of the variation of the surface charge. It gives an overall comparison of various methods of estimation of the electrokinetic conversion efficiency, in perspective of reported MD simulation data. For the dashed line, plots of the electrokinetic conversion efficiencies are obtained by the usual combination of all the above effects except employing the near wall viscosity jump where instead a slip boundary condition based approach is used. We have derived the streaming field by incorporating a slip length (b) (typically obtainable from MD data) at the wall through the velocity profile. For the specific results reported here, we have taken for the hydrophilic surface, while 35 for the hydrophobic surface. The corresponding non-dimensional streaming potential field then reads , where the non-dimensional pressure driven and electrical components of the velocity field are given by and respectively, B = b/H being the dimensionless slip length. For the MD simulation results, we have used the Grahame equation , to obtain the equivalent dimensionless zeta potential from the surface charge density, σ. The remaining dash-dot line represents the prediction using the classical PB based approach. From Fig. 5, it is evident that considering viscous and permittivity sublayers of the order of nanometers using the present model, a close prediction of the MD simulation results may be achieved for a wide range of surface potentials, using the present continuum based theory. In Fig. 5a, we see that results from the present model (solid line) show quite accurate agreement with the molecular simulation data (markers), besides exhibiting an efficiency peak which is commonly encountered. The slip-based model (instead of considering near-wall viscosity variations) along with the dielectric profile (dashed line), however, cannot predict the MD data especially at higher wall potentials (see Fig. 5a), corresponding to hydrophilic surfaces. If one neglects the presence of the added effects altogether, the model (dot-dash line) completely fails to reproduce the MD simulation data. In Fig. 5b, we again notice that the estimation from our present model (solid line) gives very close predictions compared to the MD simulation data (square markers) obtained from7, corresponding to hydrophobic substrates. The present modeling considerations, in conjunction with the slip based paradigm (instead of considering near-wall viscosity variations), also captures the MD trends quite effectively for this case, consistent with the practicalities of slipping hydrodynamics over hydrophobic surfaces. However, simulations with the classical PB formalism appear to fail severely in capturing the MD predictions for this case as well.


Resolving Anomalies in Predicting Electrokinetic Energy Conversion Efficiencies of Nanofluidic Devices.

Majumder S, Dhar J, Chakraborty S - Sci Rep (2015)

Electrokinetic Conversion efficiency against , estimated from different modeling considerations, for (a) hydrophilic surface and (b) hydrophobic surface.<Inset> Corresponding dimensionless streaming potentials predicted from the present model. The solid lines represent the predictions from the present model, with all the addressed effects taken into consideration. The dashed lines represent model predictions where only the viscosity jump is replaced by Navier slip based hydrodynamic boundary condition and keeping rest of the conditions same (see main text below for details). The dash-dot lines represent the predictions from the classical PB formalism. The markers represent MD simulation data. (a) The diamond markers corresponds to data reported in ref. 3, whereas the square marker corresponds to data reported in ref. 64 Other values chosen are α = −1.5, v = 10−4, ydds = 0.10 nm, ys = 0.30 nm,  and . (b) The MD results (square markers) were obtained from a single simulation (ref. 7). Other values chosen are α = 1, v = 10−4, ydds = 0.12 nm, ys = 0.15 nm,  and . Credited authors: S. Majumder, J. Dhar and S. Chakraborty.
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getmorefigures.php?uid=PMC4593964&req=5

f5: Electrokinetic Conversion efficiency against , estimated from different modeling considerations, for (a) hydrophilic surface and (b) hydrophobic surface.<Inset> Corresponding dimensionless streaming potentials predicted from the present model. The solid lines represent the predictions from the present model, with all the addressed effects taken into consideration. The dashed lines represent model predictions where only the viscosity jump is replaced by Navier slip based hydrodynamic boundary condition and keeping rest of the conditions same (see main text below for details). The dash-dot lines represent the predictions from the classical PB formalism. The markers represent MD simulation data. (a) The diamond markers corresponds to data reported in ref. 3, whereas the square marker corresponds to data reported in ref. 64 Other values chosen are α = −1.5, v = 10−4, ydds = 0.10 nm, ys = 0.30 nm, and . (b) The MD results (square markers) were obtained from a single simulation (ref. 7). Other values chosen are α = 1, v = 10−4, ydds = 0.12 nm, ys = 0.15 nm, and . Credited authors: S. Majumder, J. Dhar and S. Chakraborty.
Mentions: Fig. 5 describes the values of the energy conversion efficiencies considering different modeling approaches, as a function of the variation of the surface charge. It gives an overall comparison of various methods of estimation of the electrokinetic conversion efficiency, in perspective of reported MD simulation data. For the dashed line, plots of the electrokinetic conversion efficiencies are obtained by the usual combination of all the above effects except employing the near wall viscosity jump where instead a slip boundary condition based approach is used. We have derived the streaming field by incorporating a slip length (b) (typically obtainable from MD data) at the wall through the velocity profile. For the specific results reported here, we have taken for the hydrophilic surface, while 35 for the hydrophobic surface. The corresponding non-dimensional streaming potential field then reads , where the non-dimensional pressure driven and electrical components of the velocity field are given by and respectively, B = b/H being the dimensionless slip length. For the MD simulation results, we have used the Grahame equation , to obtain the equivalent dimensionless zeta potential from the surface charge density, σ. The remaining dash-dot line represents the prediction using the classical PB based approach. From Fig. 5, it is evident that considering viscous and permittivity sublayers of the order of nanometers using the present model, a close prediction of the MD simulation results may be achieved for a wide range of surface potentials, using the present continuum based theory. In Fig. 5a, we see that results from the present model (solid line) show quite accurate agreement with the molecular simulation data (markers), besides exhibiting an efficiency peak which is commonly encountered. The slip-based model (instead of considering near-wall viscosity variations) along with the dielectric profile (dashed line), however, cannot predict the MD data especially at higher wall potentials (see Fig. 5a), corresponding to hydrophilic surfaces. If one neglects the presence of the added effects altogether, the model (dot-dash line) completely fails to reproduce the MD simulation data. In Fig. 5b, we again notice that the estimation from our present model (solid line) gives very close predictions compared to the MD simulation data (square markers) obtained from7, corresponding to hydrophobic substrates. The present modeling considerations, in conjunction with the slip based paradigm (instead of considering near-wall viscosity variations), also captures the MD trends quite effectively for this case, consistent with the practicalities of slipping hydrodynamics over hydrophobic surfaces. However, simulations with the classical PB formalism appear to fail severely in capturing the MD predictions for this case as well.

Bottom Line: We devise a new approach for capturing complex interfacial interactions over reduced length scales, towards predicting electrokinetic energy conversion efficiencies of nanofluidic devices.By embedding several aspects of intermolecular interactions in continuum based formalism, we show that our simple theory becomes capable of representing complex interconnections between electro-mechanics and hydrodynamics over reduced length scales.The present model, thus, may be employed to rationalize the discrepancies between low energy conversion efficiencies of nanofluidic channels that have been realized from experiments, and the impractically high energy conversion efficiencies that have been routinely predicted by the existing theories.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Indian Institute of Technology Kharagpur Kharagpur 721302, INDIA.

ABSTRACT
We devise a new approach for capturing complex interfacial interactions over reduced length scales, towards predicting electrokinetic energy conversion efficiencies of nanofluidic devices. By embedding several aspects of intermolecular interactions in continuum based formalism, we show that our simple theory becomes capable of representing complex interconnections between electro-mechanics and hydrodynamics over reduced length scales. The predictions from our model are supported by reported experimental data, and are in excellent quantitative agreement with molecular dynamics simulations. The present model, thus, may be employed to rationalize the discrepancies between low energy conversion efficiencies of nanofluidic channels that have been realized from experiments, and the impractically high energy conversion efficiencies that have been routinely predicted by the existing theories.

No MeSH data available.