Plasma-aided direct printing of silver nanoparticle conductive structures on polydimethylsiloxane (PDMS) surfaces.

We report a controlled deposition process using atmospheric plasma to fabricate silver nanoparticle (AgNP) structures on polydimethylsiloxane (PDMS) substrates, essential for stretchable electronic circuits in wearable devices. This technique ensures precise printing of conductive structures using nanoparticles as precursors, while the relationship between crystallinity and plasma treatment is established through X-ray diffraction (XRD) analysis. The XRD studies provide insights into the effects of plasma parameters on the structural integrity and adhesion of AgNP patterns, enhancing our understanding of substrate stretchability and bendability.

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.

An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force.

Deep-Learning Based Estimation of Dielectrophoretic Force.

The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced.