Deterministic lateral displacement (DLD), a hydrodynamic, microfluidic technology, was first reported by Huang et al. in 2004 to separate particles on the basis of size in continuous flow with a resolution of down to 10 nm. For 10 years, DLD has been extensively studied, employed and modified by researchers in terms of theory, design, microfabrication and application to develop newer, faster and more efficient tools for separation of millimetre, micrometre and even sub-micrometre sized particles. To extend the range of potential applications, the specific arrangement of geometric features in DLD has also been adapted and/or coupled with external forces (e.g. acoustic, electric, gravitational) to separate particles on the basis of other properties than size such as the shape, deformability and dielectric properties of particles. Furthermore, investigations into DLD performance where inertial and non-Newtonian effects are present have been conducted. However, the evolvement and application of DLD has not yet been reviewed. In this paper, we collate many interesting publications to provide a comprehensive review of the development and diversity of this technology but also provide scope for future direction and detail the fundamentals for those wishing to design such devices for the first time.

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At present, there are few technologies which enable the detection, identification and viability analysis of protozoan pathogens including Cryptosporidium and/or Giardia at the single (oo)cyst level. We report the use of Microfluidic Impedance Cytometry (MIC) to characterise the AC electrical (impedance) properties of single parasites and demonstrate rapid discrimination based on viability and species. Specifically, MIC was used to identify live and inactive C. parvum oocysts with over 90% certainty, whilst also detecting damaged and/or excysted oocysts. Furthermore, discrimination of Cryptosporidium parvum, Cryptosporidium muris and Giardia lamblia, with over 92% certainty was achieved. Enumeration and identification of (oo)cysts can be achieved in a few minutes, which offers a reduction in identification time and labour demands when compared to existing detection methods.

Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and crosssectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting.

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