Deep learning-based optimization of a microfluidic membraneless fuel cell for maximum power density via data-driven three-dimensional multiphysics simulation

Cell chip device for real-time monitoring of drug release from drug-laden microparticles

A cell chip is a microfluidic cell culture device fabricated using microchip manufacturing methods for culturing living cells in a micrometer-sized chamber to model the physiological functions of tissues and organs. It has been extensively investigated in the domain of drug transport and toxicity research. Herein, we developed a cell chip for real-time monitoring of drug release from drug carriers. The proposed system integrates three core functions: cell culture, real-time analysis, and drug delivery tests. This device was designed to be loaded with microparticles for drug release and to enable real-time drug measurement. The efficacy of the developed system was evaluated by measuring the concentration of drugs released from the microparticles prepared with poly(lactic-co-glycolic acid) (PLGA). Doxorubicin, an anticancer drug, was used as a model drug and A549 cells, a type of lung cancer cell, were simultaneously cultured to compare the drug release concentrations in the presence of cells. Furthermore, variations in cell viability with respect to the presence of drug-loaded microparticles were observed and analyzed. Notably, as the proposed system requires an extremely small number of microparticles, it affords simple implementation in a single device, thereby eliminating the need for complex accessories and instruments for analysis. Thus, the analysis process becomes more convenient and cost-efficient. Thus, the proposed method offers an easy analysis of the release behavior of various cells and drugs. The simplicity and low cost of this innovative system without sacrificing analytical precision demonstrate its potential for applications across various fields.

Numerical evaluation and experimental validation of fluid flow behavior within an organ-on-a-chip model

BACKGROUND AND OBJECTIVE

By combining biomaterials, cell culture, and microfluidic technology, organ-on-a-chip (OoC) platforms have the ability to reproduce the physiological microenvironment of human organs. For this reason, these advanced microfluidic devices have been used to resemble various diseases and investigate novel treatments. In addition to the experimental assessment, numerical studies of biodevices have been performed aiming at their improvement and optimization. Despite considerable progress in numerical modeling of biodevices, the validation of these computational models through comparison with experimental assays remains a significant gap in the current literature. This step is critical to ensure the accuracy and reliability of numerical models, and consequently enhance confidence in their predictive results. The aim of the present work is to develop a numerical model capable of reproducing the fluid flow behavior within an OoC, for future investigations, encompassing the geometry optimization.

METHODS

In this study, the validation of a numerical model for an OoC microfluidic device was undertaken. This comprised both quantitative and qualitative assessments of trace microparticles flowing through a physical OoC model. High-speed microscopy images of the flow, using a blood analog fluid, were analyzed and compared with the numerical simulations run using the Ansys Fluent software. For a qualitative analysis, the particles' paths through the inlet and bifurcations were observed whereas, for a quantitative analysis, the particle velocities were measured. Furthermore, oxygen transport was simulated and evaluated for different Reynolds numbers.

RESULTS

In both qualitative and quantitative analyses, the results predicted by the numerical model and the ones outputted by the experimental model were in good agreement. These findings underscore the capability and potential of the developed numerical model. The examination of oxygen transport at various vertical positions within the organoid has revealed that for lower positions, oxygen transport predominantly occurs through diffusion, leading to a symmetric distribution of oxygen. Contrastingly, the convection phenomenon becomes more evident in the upper region of the organoid.

CONCLUSIONS

The successful validation of the numerical model against experimental data shows its accuracy and reliability in simulating the fluid flow within the OoC, which consequently can expedite the OoC design process by reducing the need for prototypes' fabrication and costly laboratory experiments.

Multi-criteria assessment and triple-objective optimization of a bio-anode microfluidic microbial fuel cell

Microfluidic microbial fuel cell has lower costs and greater potential than typical microbial fuel cell due to the elimination of proton exchange membrane. However, the development has mostly relied on experiments, and there has been little research on numerical simulations. Based on experimental validation, a reliable and universal model for microfluidic microbial fuel cell without quantifying the biomass concentration is proposed. Subsequently, the primary work is to study the output performance and energy efficiency of the microfluidic microbial fuel cell under different operating conditions and to comprehensively optimize the cell performance by employing the multi-objective particle swarm algorithm. Compared the optimal case with the base case, the increase ratios of maximum current density, power density, fuel utilization and exergy efficiency are 40.96%, 20.87%, 61.58% and 32.19%, respectively. On the basis of improving energy efficiency, the maximum power density and current density can reach 1.193 W/m² and 3.51 A/m².

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.