Sub-second synthesis of silver nanoparticles in 3D printed monolithic multilayered microfluidic chip: Enhanced chemiluminescence sensing predictions via machine learning algorithms

Abstract

Futuristic microfluidics will require alternative ways to extend its potential in vast areas by integrating various facets such as automation of different subsystems, multiplexing, incorporation of cyber-physical capabilities, and rapid prototyping. On the rapid prototyping aspect, for the last decade, additive manufacturing (AM) or 3D printing (3DP) has advanced to become an alternative fabrication process for microfluidic devices, enabling industry-level abilities towards mass production. In this context, for the first time, this work demonstrates the fabrication of monolithic multilayer microfluidic devices (MMMD) from planar orientation (1 layer) to nonplanar (4 layers) monolithic microchannels. The developed MMM device was impeccable for synthesizing highly potentialized silver nanoparticles (AgNPs) in <1 s. Moreover, the transport of chemical species with laminar flow simulations was performed on the process along with the thorough characterizations of produced AgNPs, finding the mean AgNPs particle size of around 35 nm without any post-processing requirements. The well-known catalytic activity of AgNPs was leveraged to enhance weak chemiluminescence (CL) sensing signals by >1300 %, increasing CL sensitivity. Further, machine learning (ML) predictive models encouraged to obtain the experimental parameters without human intervention iterations for target-specific applications. The proposed methodology finds the potential to save resources, time, and enables automation with rapid prototyping, providing possibilities for mass fabrications.