A high-throughput system combining microfluidic hydrogel droplets with deep learning for screening the antisolvent-crystallization conditions of active pharmaceutical ingredients

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.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images

Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited crystallizability of proteins often restricts a broad application of crystallization as a purification method, advances in molecular biology, as well as computational methods are pushing the applicability towards integration in biotechnological downstream processes. However, in industrial and academic settings, monitoring protein crystallization processes non-invasively by microscopic photography and automated image evaluation remains a challenging problem. Recently, the identification of single crystal objects using deep learning has been the subject of increased attention for various model systems. However, the advancement of crystal detection using deep learning for biotechnological applications is limited: robust models obtained through supervised machine learning tasks require large-scale and high-quality data sets usually obtained in large projects through extensive manual labeling, an approach that is highly error-prone for dense systems of transparent crystals. For the first time, recent trends involving the use of synthetic data sets for supervised learning are transferred, thus generating photorealistic images of virtual protein crystals in suspension (PCS) through the use of ray tracing algorithms, accompanied by specialized data augmentations modelling experimental noise. Further, it is demonstrated that state-of-the-art models trained with the large-scale synthetic PCS data set outperform similar fine-tuned models based on the average precision metric on a validation data set, followed by experimental validation using high-resolution photomicrographs from stirred tank protein crystallization processes.

Antisolvent Precipitation for Metal Recovery from Citric Acid Solution in Recycling of NMC Cathode Materials

Lithium-ion batteries (LIBs) are widely used everywhere today, and their recycling is very important. This paper addresses the recovery of metals from NMC111 (LiNi1/3Mn1/3Co1/3O2) cathodic materials by leaching followed by antisolvent precipitation. Ultrasound-assisted leaching of the cathodic material was performed in 1.5 mol L−1 citric acid at 50 °C and at a solid-to-liquid ratio of 20 g/L. Nickel(II), manganese(II) and cobalt(II) were precipitated from the leach liquor as citrates at 25 °C by adding an antisolvent (acetone or ethanol). No lithium(I) precipitation occurred under the experimental conditions, allowing for lithium separation. The precipitation efficiencies of manganese(II), cobalt(II) and nickel(II) decreased according to the order Mn > Co > Ni. The precipitation efficiency increased when a greater volume of antisolvent to the leachate was used. A smaller volume of acetone than ethanol was needed to reach the same precipitation efficiency in accordance with the difference in the dielectric constants of ethanol and acetone and their associated solubility constants. After adding two volumes of acetone into one volume of the leach liquor, 99.7% manganese, 97.0% cobalt and 86.9% nickel were recovered after 120 h, leaving lithium in the liquid phase. The metal citrates were converted into metal oxides by calcination at 900 °C.

Nanomaterials Synthesis through Microfluidic Methods: An Updated Overview

Microfluidic devices emerged due to an interdisciplinary "collision" between chemistry, physics, biology, fluid dynamics, microelectronics, and material science. Such devices can act as reaction vessels for many chemical and biological processes, reducing the occupied space, equipment costs, and reaction times while enhancing the quality of the synthesized products. Due to this series of advantages compared to classical synthesis methods, microfluidic technology managed to gather considerable scientific interest towards nanomaterials production. Thus, a new era of possibilities regarding the design and development of numerous applications within the pharmaceutical and medical fields has emerged. In this context, the present review provides a thorough comparison between conventional methods and microfluidic approaches for nanomaterials synthesis, presenting the most recent research advancements within the field.

Fabrication and Applications of Microfluidic Devices: A Review

Microfluidics is a relatively newly emerged field based on the combined principles of physics, chemistry, biology, fluid dynamics, microelectronics, and material science. Various materials can be processed into miniaturized chips containing channels and chambers in the microscale range. A diverse repertoire of methods can be chosen to manufacture such platforms of desired size, shape, and geometry. Whether they are used alone or in combination with other devices, microfluidic chips can be employed in nanoparticle preparation, drug encapsulation, delivery, and targeting, cell analysis, diagnosis, and cell culture. This paper presents microfluidic technology in terms of the available platform materials and fabrication techniques, also focusing on the biomedical applications of these remarkable devices.

Improved efficiency of urine cell image segmentation using droplet microfluidics technology

Recent advances in the recognition of biological samples using machine vision have made this technology increasingly important in research and detection. Image segmentation is an important step in this process. This study focuses on how to reduce the interference factors such as the overlap between different types (or within the same type) of urine cells according to microfluidics and improve the machine vision segmentation accuracy for cell images. In this study, we demonstrate that the platform can realize this hypothesis using urine cell image segmentation as an example application. We first discuss the reported urine cell droplet microfluidic chip system, which can realize the test conditions in which urine cells are encapsulated in the droplet and isolated from salt crystallization and/or bacteria and other urine-formed elements. Then, based on the analysis conditions set in the aforementioned experiment, the proportions of red blood cells, white blood cells, and squamous epithelial cells covered by various formed elements in the total urine cells in the same urine sample are measured. We simultaneously analyze the percentage of urine cells covered by salt crystallization and the incidence of overlapping between urine cells. Finally, the Otsu algorithm is used to segment the urine cell images encapsulated by the droplet and the urine cell images not encapsulated by the droplet, and the Dice, Jaccard, precision, and recall values are calculated. The results suggest that the method of encapsulating single cells based on droplets can improve the image segmentation effect without optimizing the algorithm.

Microfluidic High-Throughput Platforms for Discovery of Novel Materials

High-throughput screening is a potent technique to accelerate the discovery and development of new materials. By performing massive synthesis and characterization processes in parallel, it can rapidly discover materials with desired components, structures and functions. Among the various approaches for high-throughput screening, microfluidic platforms have attracted increasing attention. Compared with many current strategies that are generally based on robotic dispensers and automatic microplates, microfluidic platforms can significantly increase the throughput and reduce the consumption of reagents by several orders of magnitude. In this review, we first introduce current advances of the two types of microfluidic high-throughput platforms based on microarrays and microdroplets, respectively. Then the utilization of these platforms for screening different types of materials, including inorganic metals, metal alloys and organic polymers are described in detail. Finally, the challenges and opportunities in this promising field are critically discussed.