Micro and Nanofluidics
In recent years, the characteristics of fluid flow at micro and nanoscale have triggered the scientific and industrial interest. The use of micro and nanofluidic devices is continuously increasing with applications spanning from material and environmental sciences to bioengineering and medicine. Obtaining a better insight into the physical processes associated with these devices is required for optimising their performance and broadening their applicability. The phenomena observed in micro and nanofluidic systems are dominated by interfacial interactions due to their high surface-to-volume ratio. The static and dynamic properties of the interface can greatly influence the flow characteristics, particularly the slip across the solid fluid interface.We have developed continuum fluid dynamics, molecular dynamics, as well as multiscale models to investigate various phenomena and processes in micro/nanofluidic devices. Examples include:
- Quantify the slip, the slip length (Ls), which is defined as the extrapolated distance from the wall to the point where the tangential velocity component is equal to zero. Recent experimental observations and computational modelling indicated a number of factors such as surface energy,wettability and rate dependency that influence the slip length. Despite the scientific interest to the slip phenomena, the implications of the aforementioned factors to the slip’s existence and magnitude are not yet fully understood.
- Multi-scale models for simulating macromolecules in fluid flows. Macromolecule transport at low number densities is frequently encountered in biomedical devices, such as separators, detection and analysis systems. Accurate modelling of this process is challenging due to the wide range of physical scales involved. The continuum approach is not valid for low solute concentrations, but the large timescales of the fluid flow make purely molecular simulations prohibitively expensive. A promising multi-scale modelling strategy is provided by the meta-modelling approach. Meta-models are based on the coupled solution of fluid flow equations and equations of motion for a simplified mechanical model of macromolecules. The approach enables simulation of individual macromolecules at macroscopic time scales.
- Development of variable density, incompressible models for microfluidic channels. The figures below show simulations of Xylene-Water mixing in microfluidics cells and comparison of the CFD results with experiments. The results have been obtained using the methods of .
- N. Asproulis and D. Drikakis, An Artificial Neural Network based Multiscale Method for Hybrid Atomistic-Continuum Simulations, Microfluidics and Nanofluidics, 15(4), 559-574, 2013.
- N. Asproulis, D. Drikakis, Surface Roughness Effects in Micro and Nanofluidic Devices, Journal of Computational and Theoretical Nanoscience,7(9), 1825-1830, 2010
- E. Shapiro, D. Drikakis, J. Gargiuli, P. Vadgama, Interface capturing in dual-flow microfluidics, Journal of Computational and Theoretical Nanoscience, 4, 802-806, 2007.
- G. Nair, J. Garguili, N. R. Shiju, Z. Rhong, E. Shapiro, D. Drikakis, P. Vadgama, In Situ fabrication of Crosslinked Protein Membranes using Microfluidics, ChemBioChem, 7(11), 1683-1689, 2006.
- J. Gargiuli, E. Shapiro, H. Gulhane, G. Nair, D. Drikakis, P. Vadgama “Microfluidic systems for in situ formation of nylon 6,6 membranes'' Journal of Membrane Science, 282, 257-265, 2006.
- M. Benke, E. Shapiro, D. Drikakis, On mesoscale modelling of dsDNA molecules in fluid flow, Journal of Computational and Theoretical Nanoscience, 10(3), 697-704, 2013.
- M. Benke, E. Shapiro, D. Drikakis, Mechanical behaviour of DNA molecules-elasticity and migration, Medical Engineering and Physics, 33, 883-886, 2011.
- M. Benke, E. Shapiro, D. Drikakis, An efficient multi-scale modelling approach for ssDNA motion in fluid flow, Journal of Bionic Engineering, Vol. 5, 4, 299-307, 2008.
- E. Shapiro, D. Drikakis, Artificial compressibility, characteristics-based schemes for variable density, incompressible, multi-species flows. Part II. Multigrid implementation and numerical tests, Journal of Computational Physics, 210, 608-631, 2005.