Integration of reinforcement learning to realize functional variability of microfluidic systems

Abstract

In this article, we present a proof-of-concept for microfluidic systems with high functional variability using reinforcement learning. By mathematically defining the objective of tasks, we demonstrate that the system can autonomously learn to behave according to its objectives. We applied Q-learning to a peristaltic micropump and showed that two different tasks can be performed on the same platform: adjusting the flow rate of the pump and manipulating the position of the particles. First, we performed typical micropumping with flow rate control. In this task, the system is rewarded according to the deviation between the average flow rate generated by the micropump and the target value. Therefore, the objective of the system is to maintain the target flow rate via an operation of the pump. Next, we demonstrate the micromanipulation of a small object (microbead) on the same platform. The objective was to manipulate the microbead position to the target area, and the system was rewarded for the success of the task. These results confirmed that the system learned to control the flow rate and manipulate the microbead to any randomly chosen target position. In particular, the manipulation technique is a new technology that does not require the use of structures such as wells or weirs. Therefore, this concept not only adds flexibility to the system but also contributes to the development of novel control methods to realize highly versatile microfluidic systems.

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

“…271,272 These systems can dynamically regulate flow rates based on real-time data, adapting seamlessly to changes in fluid viscosity or external environmental conditions. 273 This heightened adaptability contributes to the overall efficiency and performance of microfluidic wearables, offering a level of precision that is particularly crucial in processes such as drug delivery or diagnostic procedures. 270 The dynamic adjustment of flow rates ensures that the microfluidic processes remain finely tuned, providing a more accurate and personalized experience within the wearable device.…”

A Review of Single‐Cell Pose Adjustment and Puncture

“…However, in a high-noise, complex, dynamic, and unpredictable working environment, it is a challenge to establish an accurate recognition and control model, due to the fact that it requires a lot of priori knowledge. In recent years, with the development of artificial intelligence technology, especially convolutional neural network (CNN) [202] and deep reinforcement learning (DRL), [203] researchers have shown great interest in combining CNN and DRL algorithms with the recognition and control of single-cell manipulation. [204] The scenario of single-cell manipulation is not only in vitro, but also in vivo.…”