Effects of Flow‐Induced Microfluidic Chip Wall Deformation on Imaging Flow Cytometry

Squeezed state in the hydrodynamic focusing regime for Escherichia coli bacteria detection

Flow cytometry is an essential technique in single particle analysis and cell sorting for further downstream diagnosis, exhibiting high-throughput and multiplexing capabilities for many biological and biomedical applications. Although many hydrodynamic focusing-based microfluidic cytometers have been demonstrated with reduced size and cost to adapt to point-of-care settings, the operating conditions are not characterized systematically. This study presents the flow transition process in the hydrodynamic focusing mechanism when the flow rate or the Reynolds number increases. The characteristics of flow fields and mass transport were studied under various operating conditions, including flow rates and microchannel heights. A transition from the squeezed focusing state to the over-squeezed anti-focusing state in the hydrodynamic focusing regime was observed when the Reynolds number increased above 30. Parametric studies illustrated that the focusing width increased with the Reynolds number but decreased with the microchannel height in the over-squeezed state. The microfluidic cytometric analyses using microbeads and E. coli show that the recovery rate was maintained by limiting the Reynolds number to 30. The detailed analysis of the flow transition will provide new insight into microfluidic cytometric analyses with a broad range of applications in food safety, water monitoring and healthcare sectors.

A review on intelligent impedance cytometry systems: Development, applications and advances

Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.

An optimized PDMS microfluidic device for ultra-fast and high-throughput imaging flow cytometry

Imaging flow cytometry (IFC) is a powerful tool for cell detection and analysis due to its high throughput and compatibility in image acquisition. Optical time-stretch (OTS) imaging is considered as one of the most promising imaging techniques for IFC because it can realize cell imaging at a flow speed of around 60 m s-1. However, existing PDMS-based microchannels cannot function at flow velocities higher than 10 m s-1; thus the capability of OTS-based IFC is significantly limited. To overcome the velocity barrier for PDMS-based microchannels, we proposed an optimized design of PDMS-based microchannels with reduced hydraulic resistance and 3D hydrodynamic focusing capability, which can drive fluids at an ultra-high flow velocity (of up to 40 m s-1) by using common syringe pumps. To verify the feasibility of our design, we fabricated and installed the microchannel in an OTS IFC system. The experimental results first proved that the proposed microchannel can support a stable flow velocity of up to 40 m s-1 without any leakage or damage. Then, we demonstrated that the OTS IFC is capable of imaging cells at a velocity of up to 40 m s-1 with good quality. To the best of our knowledge, it is the first time that IFC has achieved such a high flow velocity just by using a PDMS-glass chip. Moreover, high velocity can enhance the focusing of cells on the optical focal plane, increasing the number of detected cells and the throughput. This work provides a promising solution for IFC to fully release its capability of advanced imaging techniques by operating at an extremely high screening throughput.

Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems

A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems. The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of "on demand" treatments. In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

Fabrication of Perforated PDMS Microchannel by Successive Laser Pyrolysis

Poly(dimethylsiloxane) has attracted much attention in soft lithography and has also been preferred as a platform for a photochemical reaction, thanks to its outstanding characteristics including ease of use, nontoxicity, and high optical transmittance. However, the low stiffness of PDMS, an obvious advantage for soft lithography, is often treated as an obstacle in conducting precise handling or maintaining its structural integrity. For these reasons, a Glass-PDMS-Glass structure has emerged as a straightforward alternative. Nevertheless, several challenges are remaining in fabricating Glass-PDMS-Glass structure through the conventional PDMS patterning techniques such as photolithography and etching processes for master mold. The complicated techniques are not suitable for frequent design modifications in research-oriented fields, and fabrication of perforated PDMS is hard to achieve using mold replication. Herein, we utilize the successive laser pyrolysis technique to pattern thin-film PDMS for microfluidic applications. The direct use of thin film at the glass surface prevents the difficulties of thin-film handling. Through the precise control of photothermal pyrolysis phenomena, we provide a facile fabrication process for perforated PDMS microchannels. In the final demonstration, the laminar flow has been successfully created owing to the smooth surface profile. We envision further applications using rapid prototyping of the perforated PDMS microchannel.

Soft hydraulics: from Newtonian to complex fluid flows through compliant conduits

Microfluidic devices manufactured from soft polymeric materials have emerged as a paradigm for cheap, disposable and easy-to-prototype fluidic platforms for integrating chemical and biological assays and analyses. The interplay between the flow forces and the inherently compliant conduits of such microfluidic devices requires careful consideration. While mechanical compliance was initially a side-effect of the manufacturing process and materials used, compliance has now become a paradigm, enabling new approaches to microrheological measurements, new modalities of micromixing, and improved sieving of micro- and nano-particles, to name a few applications. This topical review provides an introduction to the physics of these systems. Specifically, the goal of this review is to summarize the recent progress towards a mechanistic understanding of the interaction between non-Newtonian (complex) fluid flows and their deformable confining boundaries. In this context, key experimental results and relevant applications are also explored, hand-in-hand with the fundamental principles for their physics-based modeling. The key topics covered include shear-dependent viscosity of non-Newtonian fluids, hydrodynamic pressure gradients during flow, the elastic response (deformation and bulging) of soft conduits due to flow within, the effect of cross-sectional conduit geometry on the resulting fluid-structure interaction, and key dimensionless groups describing the coupled physics. Open problems and future directions in this nascent field of soft hydraulics, at the intersection of non-Newtonian fluid mechanics, soft matter physics, and microfluidics, are noted.

A simple and reversible glass-glass bonding method to construct a microfluidic device and its application for cell recovery

Compared with polymer microfluidic devices, glass microfluidic devices have advantages for diverse lab-on-a-chip applications due to their rigidity, optical transparency, thermal stability, and chemical/biological inertness. However, the bonding process to construct glass microfluidic devices usually involves treatment(s) like high temperature over 400 °C, oxygen plasma or piranha solution. Such processes require special skill, apparatus or harsh chemicals, and destroy molecules or cells in microchannels. Here, we present a simple method for glass-glass bonding to easily form microchannels. This method consists of two steps: placing water droplets on a glass substrate cleaned by neutral detergent, followed by fixing a cover glass plate on the glass substrate by binding clips for a few hours at room temperature. Surface analyses showed that the glass surface cleaned by neutral detergent had a higher ratio of SiOH over SiO than glass surfaces prepared by other cleaning steps. Thus, the suggested method could achieve stronger glass-glass bonding via dehydration condensation due to the higher density of SiOH. The pressure endurance reached over 600 kPa within 6 h of bonding, which is sufficient for practical microfluidic applications. Moreover, by exploiting the reversibility of this bonding method, cell recoveries after cultivating cells in a microchannel were demonstrated. This new bonding method can significantly improve both the productivity and the usability of glass microfluidic devices and extend the possibility of glass microfluidic applications in future.