Direct Single-Step Printing of Conductive Grids on Curved Surfaces Using Template-Guided Foaming

Programmable and Pixelated Solute Concentration Fields Controlled by Three-Dimensionally Networked Microfluidic Source/Sink Arrays

Membrane-integrated microfluidic platforms have played a pivotal role in understanding natural phenomena coupled with solute concentration gradients at the micro- and nanoscale, enabling on-chip microscopy in well-defined planar concentration fields. However, the standardized two-dimensional fabrication schemes in microfluidics have impeded the realization of more complex and diverse chemical environmental conditions due to the limited possible arrangements of source/sink conditions in a fluidic domain. In this study, we present a microfluidic platform with a three-dimensional microchannel network design, where discretized membranes can be integrated and individually controlled in a two-dimensional array format at any location within the entire quasi-two-dimensional solute concentration field. We elucidate the principles of the device to implement operations of the pixel-like sources/sinks and dynamically programmable control of various long-lasting solute concentration fields. Furthermore, we demonstrate the application of the generated solute concentration fields in manipulating the transport of micrometer or submicrometer particles with a high degree of freedom, surpassing conventionally available solute concentration fields. This work provides an experimental tool for investigating complex systems under high-order chemical environmental conditions, thereby facilitating the extensive development of higher-performance micro- and nanotechnologies.

Fabricating and Laminating Films with Through-Holes and Engraved/Protruding Structures for 3D Micro/Nanofluidic Platforms

Micro/nanofluidic devices have become popular for delicately processing biological, material, and chemical samples. However, their reliance on 2D fabrication schemes has hindered further innovation. Here, a 3D manufacturing method is proposed through the innovation of laminated object manufacturing (LOM), which involves the selection of building materials as well as the development of molding and lamination techniques. Fabrication of interlayer films is demonstrated with both multi-layered micro-/nanostructures and through-holes, using an injection molding approach and establishing strategic principles of film design. Utilization of the multi-layered through-hole films in LOM allows reducing the number of alignments and laminations by at least two times compared to conventional LOM. Using a dual-curing resin for film fabrication, a surface-treatment-free and collapse-free lamination technique is shown for constructing 3D multiscale micro/nanofluidic devices with ultralow aspect ratio nanochannels. The 3D manufacturing method enables the development of a nanochannel-based attoliter droplet generator capable of 3D parallelization for mass production, which implies the remarkable potential to extend numerous existing 2D micro/nanofluidic platforms into a 3D framework.

Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner

A liquid–gas foam, here called bubble array, is a ubiquitous phenomenon widely observed in daily lives, food, pharmaceutical and cosmetic products, and even bio- and nano-technologies. This intriguing phenomenon has been often studied in a well-controlled environment in laboratories, computations, or analytical models. Still, real-world bubble undergoes complex nonlinear transitions from wet to dry conditions, which are hard to describe by unified rules as a whole. Here, we show that a few early-phase snapshots of bubble array can be learned by a glass-box physics rule learner (GPRL) leading to prediction rules of future bubble array. Unlike the black-box machine learning approach, the glass-box approach seeks to unravel expressive rules of the phenomenon that can evolve. Without known principles, GPRL identifies plausible rules of bubble prediction with an elongated bubble array data that transitions from wet to dry states. Then, the best-so-far GPRL-identified rule is applied to an independent circular bubble array, demonstrating the potential generality of the rule. We explain how GPRL uses the spatio-temporal convolved information of early bubbles to mimic the scientist’s perception of bubble sides, shapes, and inter-bubble influences. This research will help combine foam physics and machine learning to better understand and control bubbles.

Performance characterization of transparent and conductive grids one-step-printed on curved substrates using template-guided foaming

Next-generation electronic devices require electrically conductive, mechanically flexible, and optically transparent conducting electrodes (CEs) that can endure large deformations.