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Abstract
Flow linear dichroism is a biophysical spectroscopic technique that exploits the shear-induced alignment of elongated particles in suspension. Motivated by the broad aim of optimizing the sensitivity of this technique, and more specifically by a hand-held synthetic biotechnology prototype for waterborne-pathogen detection, a model of steady and oscillating pressure-driven channel flow and orientation dynamics of a suspension of slender microscopic fibres is developed. The model couples the Fokker-Planck equation for Brownian suspensions with the narrow channel flow equations, the latter modified to incorporate mechanical anisotropy induced by the particles. The linear dichroism signal is estimated through integrating the perpendicular components of the distribution function via an appropriate formula which takes the biaxial nature of the orientation into account. For the specific application of pathogen detection via binding of M13 bacteriophage, it is found that increases in the channel depth are more significant in improving the linear dichroism signal than increases in the channel width. Increasing the channel depth to 2 mm and pressure gradient to 5 × 10 4 Pa m -1 essentially maximizes the alignment. Oscillating flow can produce nearly equal alignment to steady flow at appropriate frequencies, which has significant potential practical value in the analysis of small sample volumes.
Original language | English |
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Article number | 20190184 |
Number of pages | 21 |
Journal | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences |
Volume | 475 |
Issue number | 2232 |
DOIs | |
Publication status | Published - 18 Dec 2019 |
Keywords
- Brownian suspensions
- Fokker-Planck equation
- flow-induced alignment
- linear dichroism spectroscopy
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Dive into the research topics of 'Oriented suspension mechanics with application to improving flow linear dichroism spectroscopy'. Together they form a unique fingerprint.Projects
- 1 Finished
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Rapid sperm capture: integrating live imaging and machine learning to optimise fertility treatment
Engineering & Physical Science Research Council
1/07/16 → 30/06/22
Project: Research Councils