Microfluidic High-Throughput Platforms for Discovery of Novel Materials

A Highly Sensitive Dual-Drive Microfluidic Device for Multiplexed Detection of Respiratory Virus Antigens

Conventional microfluidic systems that rely on capillary force have a fixed structure and limited sensitivity, which cannot meet the demands of clinical applications. Herein, we propose a dual-drive microfluidic device for sensitive and flexible detection of multiple pathogenic microorganisms antigens/antibodies. The device comprises a portable microfluidic analyzer and a dual-drive microfluidic chip. Along with capillary force, a second active driving force is provided by a removable self-driving valve in the waste chamber. The interval between these two driving forces can be adjusted to control the reaction time in the microchannel, optimizing the formation of antigen-antibody complexes and enhancing sensitivity. Moreover, the material used in the self-driving valve can be changed to adjust the active force strength needed for different tests. The device offers quantitative analysis for respiratory syncytial virus antigen and SARS-CoV-2 antigen using a 35 μL sample, delivering results within 5 min. The detection limits of the system were 1.121 ng/mL and 0.447 ng/mL for respiratory syncytial virus recombinant fusion protein and SARS-CoV-2 recombinant nucleoprotein, respectively. Although the dual-drive microfluidic device has been used for immunoassay for respiratory syncytial virus and SARS-CoV-2 in this study, it can be easily adapted to other immunoassay applications by changing the critical reagents.

Multiplex digital microfluidics using serial controls and its applications in glucose sensing

Digital microfluidics (DMF) has found great applications in vitro diagnostics (IVD). Compared to the microfabrication-based DMF, printed circuit board (PCB)-based DMF is more economical and compatible with existing IVD instruments. Despite that, current PCB-based DMF is oftentimes limited by the available droplets that can be controlled simultaneously, compromising their throughput and applications as point-of-care tools. In this work, a platform that simultaneously controls multiple PCB-based DMF plates was constructed. The software and hardware were first developed, followed by the reliability tests. Colorimetric analysis of glucose was applied to the PCB-based DMF, demonstrating the capability of this platform. With the high throughput enabled by simultaneous operations of multiple plates, this PCB-based DMF can potentially allow point-of-care testing with low cost for resource-limited settings.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Analyte Enrichment via Ion Concentration Polarization with Hydrogel Plugs Polymerized in PDMS Microchannels by a Facile and Comprehensive Method for Improved Polymerization

Hydrogels are incorporated into microfluidic devices to provide enhanced functionality by enabling processes such as enzyme immobilization, sample enrichment, and ionic current rectification. However, in the microfluidic devices with the commonly used material poly(dimethylsiloxane) (PDMS), hydrogels are very difficult to polymerize in situ in an ambient atmosphere because of the high oxygen concentration in PDMS. Even with very high (1.8%) photoinitiator concentrations, the polymerized hydrogel does not completely fill the microchannel. Here, we report a facile and broadly applicable protocol that utilizes microchannel pretreatment with 20% benzophenone in acetone to provide a hydrogel plug that completely fills the microchannel cross section by consuming the oxygen in the PDMS substrate near the microchannel wall. Both negatively charged and neutral hydrogels are polymerized from monomer solutions that utilize the photoinitiator/solvent combinations of VA-086 in water and benzophenone or IRG in DMSO. The photoinitiators were tested at different concentrations and in devices with different levels of oxidation. The hydrogel morphology is characterized using phase contrast microscopy and is related to the hydrogel's performance for concentration enrichment and ionic current rectification. A novel method is employed to confine the precursor solution in desired locations so that a photomask is not required for the spatial control of the plug location. Among six hydrogel formulations, at 100 V, the best current rectification factor obtained is ∼600 and the best analyte enrichment achieved is ∼120-fold in 5 min. This method provides a rapid and simple approach to increase the capabilities of PDMS microfluidic devices through improved polymerization of nanoporous hydrogels.

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters' experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter's behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

Enabling Technology for Supramolecular Chemistry

Supramolecular materials-materials that exploit non-covalent interactions-are increasing in structural complexity, selectivity, function, stability, and scalability, but their use in applications has been comparatively limited. In this Minireview, we summarize the opportunities presented by enabling technology-flow chemistry, high-throughput screening, and automation-to wield greater control over the processes in supramolecular chemistry and accelerate the discovery and use of self-assembled systems. Finally, we give an outlook for how these tools could transform the future of the field.