Photonic Microfluidic Technologies for Phytoplankton Research

Phytoplankton is a crucial component for the correct functioning of different ecosystems, climate regulation and carbon reduction. Being at least a quarter of the biomass of the world's vegetation, they produce approximately 50% of atmospheric O2 and remove nearly a third of the anthropogenic carbon released into the atmosphere through photosynthesis. In addition, they support directly or indirectly all the animals of the ocean and freshwater ecosystems, being the base of the food web. The importance of their measurement and identification has increased in the last years, becoming an essential consideration for marine management. The gold standard process used to identify and quantify phytoplankton is manual sample collection and microscopy-based identification, which is a tedious and time-consuming task and requires highly trained professionals. Microfluidic Lab-on-a-Chip technology represents a potential technical solution for environmental monitoring, for example, in situ quantifying toxic phytoplankton. Its main advantages are miniaturisation, portability, reduced reagent/sample consumption and cost reduction. In particular, photonic microfluidic chips that rely on optical sensing have emerged as powerful tools that can be used to identify and analyse phytoplankton with high specificity, sensitivity and throughput. In this review, we focus on recent advances in photonic microfluidic technologies for phytoplankton research. Different optical properties of phytoplankton, fabrication and sensing technologies will be reviewed. To conclude, current challenges and possible future directions will be discussed.

Microfluidic Microalgae System: A Review

Microalgae that have recently captivated interest worldwide are a great source of renewable, sustainable and economical biofuels. The extensive potential application in the renewable energy, biopharmaceutical and nutraceutical industries have made them necessary resources for green energy. Microalgae can substitute liquid fossil fuels based on cost, renewability and environmental concern. Microfluidic-based systems outperform their competitors by executing many functions, such as sorting and analysing small volumes of samples (nanolitre to picolitre) with better sensitivities. In this review, we consider the developing uses of microfluidic technology on microalgal processes such as cell sorting, cultivation, harvesting and applications in biofuels and biosensing.

The Fusion of Microfluidics and Optics for On-Chip Detection and Characterization of Microalgae

It has been demonstrated that microalgae play an important role in the food, agriculture and medicine industries. Additionally, the identification and counting of the microalgae are also a critical step in evaluating water quality, and some lipid-rich microalgae species even have the potential to be an alternative to fossil fuels. However, current technologies for the detection and analysis of microalgae are costly, labor-intensive, time-consuming and throughput limited. In the past few years, microfluidic chips integrating optical components have emerged as powerful tools that can be used for the analysis of microalgae with high specificity, sensitivity and throughput. In this paper, we review recent optofluidic lab-on-chip systems and techniques used for microalgal detection and characterization. We introduce three optofluidic technologies that are based on fluorescence, Raman spectroscopy and imaging-based flow cytometry, each of which can achieve the determination of cell viability, lipid content, metabolic heterogeneity and counting. We analyze and summarize the merits and drawbacks of these micro-systems and conclude the direction of the future development of the optofluidic platforms applied in microalgal research.

How does the Internet of Things (IoT) help in microalgae biorefinery?

Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high-throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.

Resistive pulse sensing device with embedded nanochannel (nanochannel-RPS) for label-free biomolecule and bionanoparticle analysis

This paper reports an IC-compatible method for fabricating a PDMS-based resistive pulse sensing (RPS) device with embedded nanochannel (nanochannel-RPS) for label-free analysis of biomolecules and bionanoparticles, such as plasmid DNAs and exosomes. Here, a multilayer lithography process was proposed to fabricate the PDMS mold for the microfluidic device, comprising a bridging nanochannel, as the sensing gate. RPS was performed by placing the sensing and excitation electrodes symmetrically upstream and downstream of the sensing gate. In order to reduce the noise level, a reference electrode was designed and placed beside the excitation electrode. To demonstrate the feasibility of the proposed nanochannel-RPS device and sensing system, polystyrene micro- and nanoparticles with diameters of 1μm and 300 nm were tested by the proposed device with signal-to-noise ratios (SNR) ranging from 9.1-30.5 and 2.2-5.9, respectively. Furthermore, a nanochannel with height of 300 nm was applied for 4 kb plasmid DNA detection, implying the potential of the proposed method for label-free quantification of nanoscale biomolecules. Moreover, HeLa cell exosomes, known as a well-studied subtype of extracellular vesicles, were measured and analyzed by their size distribution. The result of the resistive pulse amplitude corresponded well to that of nanoparticle tracking analysis (NTA). The proposed nanochannel-RPS device and the sensing strategy are not only capable of label-free analysis for nanoscale biomolecules and bionanoparticles, but are also cost-effective for large-scale manufacturing.

Particle detection on microfluidic chips by differential resistive pulse sensing (RPS) method

The resistive pulse sensing (RPS) method has been widely used for characterization of particles, cells, and biomolecules due to its merits of high sensitivity and resolution. This paper investigates working parameters involved in detecting submicron and micron-sized particles by the differential RPS method on microfluidic chips. Effects of particle-to-sensor size ratio, ionic concentration and pH of the electrolyte solution, and applied electric field are studied systematically by using polystyrene particles with a size range from 140 nm to 5 µm. The results show that both the amplitude and the signal-to-noise ratio (SNR) of the RPS signals increase with the particle-to-sensor size ratio as well as the ionic concentration of the electrolyte media. The amplitude of the RPS signals also increases with increasing applied voltage, while the SNR experiences an upslope at low voltages and a decline under the condition of high voltages. pH has little effect on the background noise of the differential RPS signals but reduces the amplitude of the RPS signals at high pH. Grouping of RPS signals is considered to be caused by interactions between the sensor walls and the particles. Nanoparticle detection by the differential RPS method can be enhanced by optimizing these working parameters.

Detection and sizing of nanoparticles and DNA on PDMS nanofluidic chips based on differential resistive pulse sensing

The RPS (Resistive Pulse Sensing) technique is a popular tool for the label-free detection of particles. This paper describes a simple, cost-effective PDMS nanofluidic chip for the detection and characterization of nanoparticles based on the differential RPS technique with high resolution and sensitivity. The chip is composed of two layers of PDMS slabs. Microchannel systems fabricated by the photolithography method on the top layer are used for sample loading and differential signal acquisition, and a straight nanochannel on the bottom layer fabricated by an unconventional approach bridging the gap between the microchannels works as an RPS sensing gate. A single-stage differential amplifier is used to amplify the RPS signals when particles or DNA pass through the sensing gate. It was demonstrated that this nanofluidic RPS chip can detect nanoparticles as small as 23 nm with a high SNR (Signal-to-Noise Ratio). The experimental results also show that the device is able to distinguish nanoparticles of smaller size differences such as 60 nm and 83 nm with high resolution, showing superior performance in comparison with the results obtained from DLS (Dynamic Light Scattering). This differential nano-RPS chip was also applied to detect the translocation of dsDNA molecules.

Deformability-Based Electrokinetic Particle Separation

Deformability is an effective property that can be used in the separation of colloidal particles and cells. In this study, a microfluidic device is proposed and tested numerically for the sorting of deformable particles of various degrees. The separation process is numerically investigated by a direct numerical simulation of the fluid–particle–electric field interactions with an arbitrary Lagrangian–Eulerian finite-element method. The separation performance is investigated with the shear modulus of particles, the strength of the applied electric field, and the design of the contracted microfluidic devices as the main parameters. The results show that the particles with different shear moduli take different shapes and trajectories when passing through a microchannel contraction, enabling the separation of particles based on their difference in deformability.

Hydrodynamic particle focusing design using fluid-particle interaction

For passive sheathless particles focusing in microfluidics, the equilibrium positions of particles are typically controlled by micro channels with a V-shaped obstacle array (VOA). The design of the obstacles is mainly based on the distribution of flow streamlines without considering the existence of particles. We report an experimentally verified particle trajectory simulation using the arbitrary Lagrangian-Eulerian (ALE) fluid-particle interaction method. The particle trajectory which is strongly influenced by the interaction between the particle and channel wall is systematically analyzed. The numerical experiments show that the streamline is a good approximation of particle trajectory only when the particle locates on the center of the channel in depth. As the advantage of fluid-particle interaction method is achieved at a high computational cost and the streamline analysis is complex, a heuristic dimensionless design objective based on the Faxen's law is proposed to optimize the VOA devices. The optimized performance of particle focusing is verified via the experiments and ALE method.