Optimization techniques in digital microfluidic biochips: a survey of sample preparation algorithmic solutions and challenges

Abstract

Digital Micro Fluidic Biochips (DMFBs) are a revolutionary way to automate biochemical processes which are accurate, handy, and multifunctional. However, limitations in droplet manipulation, resource allocation, and assay execution continue to serve as considerable obstacles to effective sample preparation. Using electrical actuation techniques, these biochips accurately automate fluid sample analysis, simplifying essential laboratory tasks including cleaning, mixing, separating, and merging. Solutions with a predetermined target volume can be generated due to this technique. This process consists of combining various solutions of chemicals in a specified volume ratio by carrying out a different procedure. By using these methods, DMFBs can perform tests with little use of sample or reagent, opening up possibilities for use in drug research, gene sequencing, DNA analysis, medical diagnostics, and other fields. An extensive overview of optimization methods used for sample preparation in DMFBs is given in this paper, with an emphasis on algorithmic solutions that improve scheduling, dilution, and mixing. We categorize and evaluate current methods according to their computational methodologies and trade-offs between performance and adaptation to various biochip layouts. We also look at important issues, including real-time reconfiguration and waste droplet management. Lastly, we explore future research prospects in developing digital microfluidic biochip technologies and emphasize the suggested sample preparation scheduling method. The purpose of this survey is to assist researchers in creating DMFB sample preparation techniques that are more dependable and effective.