This study introduces μ-CPR, a microfluidic platform that reactivates senescent stem cells through controlled mechanical stimulation, without chemical or genetic intervention. μ-CPR restores intracel...

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To enable controlled high-throughput compartmentalization of cell-free expression machinery for diverse gene constructs, we implemented a microfluidic 5-way hydrodynamic focusing design that generates monodisperse water-in-oil droplets. The junction allows the separate and simultaneous introduction of DNA templates (Inner Aqueous 1, IA1) and TXTL reagents (IA2), to produce compositionally distinct synthetic cell populations by exchanging the DNA input (Fig. 1). We used the PUREfrex2.0 system for protein production, due to its defined composition and low background activity. To date, its high cost relative to the limited number of distinct reactions per kit has represented a major bottleneck to adoption in bulk screening workflows. However, relying on PUREdrop’s droplets-on-demand greatly improves reagent efficiency. The three main PUREfrex2.0 components responsible for TXTL constitute ~ 65% of the total reaction volume (IA2), with the remainder comprising DNA and water (IA1). By decoupling the TXTL machinery from the DNA stream, we were able to generate ~ 3 × 10⁵ droplets ( ~ 20 µm diameter) per reaction using only ~ 0.87 µL of the PUREfrex2.0 components, in comparison to the minimum ~ 6.5 µL TXTL reagents per reaction used in manual emulsification protocols, mounting to 7.4 folds in enhanced reagent efficiency13. This can be further...

Collagen type I, a key component of the extracellular matrix, underpins cellular organization and regeneration but remains challenging to process into scalable, reproducible, and biologically faithful...

Paper-based diagnostics such as lateral flow assays (LFAs) and microfluidic paper-based analytical devices (PADs) have attracted considerable attention because of their low cost, portability, and ease of use. Currently, to enable fabrication of PADs and improve LFA performance, hydrophobic blocks are patterned on paper substrates. However, fabrication of high-resolution hydrophobic barriers remains a major challenge. In this work, we developed a novel silicone extrudable ink for the fabrication of hydrophobic features on paper substrates. The ink was formulated using a vinyl-terminated polydimethylsiloxane (vPDMS) and polymethylhydrosiloxane (PMHS) system crosslinked through platinum-catalyzed hydrosilylation, and its rheological properties were tailored by incorporating silica fillers, obtaining a shear-thinning gel suitable for extrusion. The resulting formulation provided tunable properties, controlled deposition, and stable feature formation, enabling simple, low-cost, rapid, and robust fabrication of high-resolution hydrophobic barriers. Using this approach, we demonstrated improved fluid confinement and pattern fidelity on paper substrates, fabricated high-resolution paper microfluidic devices down to 150 m channel width, and enhanced the sensitivity of an LFA for a malaria diagnostic test. These results highlight the potential of this silicone ink platform as a practical and scalable strategy for advancing high-performance paper-based diagnostic technologies.

Paper-based diagnostics such as lateral flow assays (LFAs) and microfluidic paper-based analytical devices (PADs) have attracted considerable attention because of their low cost, portability, and ease of use. Currently, to enable fabrication of PADs and improve LFA performance, hydrophobic blocks are patterned on paper substrates. However, fabrication of high-resolution hydrophobic barriers remains a major challenge. In this work, we developed a novel silicone extrudable ink for the fabrication of hydrophobic features on paper substrates. The ink was formulated using a vinyl-terminated polydimethylsiloxane (vPDMS) and polymethylhydrosiloxane (PMHS) system crosslinked through platinum-catalyzed hydrosilylation, and its rheological properties were tailored by incorporating silica fillers, obtaining a shear-thinning gel suitable for extrusion. The resulting formulation provided tunable properties, controlled deposition, and stable feature formation, enabling simple, low-cost, rapid, and robust fabrication of high-resolution hydrophobic barriers. Using this approach, we demonstrated improved fluid confinement and pattern fidelity on paper substrates, fabricated high-resolution paper microfluidic devices down to 150 m channel width, and enhanced the sensitivity of an LFA for a malaria diagnostic test. These results highlight the potential of this silicone ink platform as a practical and scalable strategy for advancing high-performance paper-based diagnostic technologies.

3D printing creates hydrophobic barriers in hydrophilic paper, guiding liquids along precise paths for controlled mixing, gradients, and two-phase separation.

arXiv abstract link

A research team at Yokohama National University has developed a new method for controlling the orientation of collagen fibers in biological tissue models. The method is based on flow-controlled orientation within microfluidic channels produced by 3D printing. The results were published in ACS Biomaterials Science and Engineering on May 12, 2025. Collagen is a central […] The post 3D-printed microstructure: researchers control the orientation of collagen fibers in tissue models appeared first on 3Printr.com.

Microfluidic confinement of phase-separating giant unilamellar vesicles (GUVs) directs the accumulation of the minority-phase domains in the free regions where they fuse to minimize their line energy...

Researchers at Tohoku University have made a crucial step forward in understanding how directional connections shape the complex dynamics of neuronal networks. Led by Nobuaki Monma, the team used microfluidic devices to create lab-grown neurons with artificially controlled connections, providing a useful alternative to animal testing. By studying these in-vitro neurons, the researchers were able to explore the role of one-way connections in shaping brain dynamics. Associate Professor Hideaki Yamamoto notes that the findings are expected to deepen our fundamental understanding of neuronal networks and have potential applications in fields such as medicine and machine learning. The team's work, published in Neural Networks, has significant implications for the development of biologically plausible artificial neural networks and may ultimately contribute to a better understanding of the brain's many mysteries. The research was conducted in collaboration with several colleagues, including Naoya Fujiwara, Hakuba Murota, Satoshi Moriya, Ayumi Hirano-Iwata, and Shigeo Sato.

Microfluidic devices (MFDs) printed in 3-D geometry using digital light projection to polymerize monomers often have surfaces that are not as hydrophobic as MFDs made from polydimethylsiloxane. Drople...

In biomedical research, so-called organs-on-a-chip, also referred to as microphysiological systems, are becoming increasingly important: by cultivating tissue structures in precisely controlled microfluidic chips, it is possible to conduct research much more accurately than in experiments involving living humans or animals. These systems can be used to investigate the effects of a new drug and to better understand the interaction between different organs to grasp the systemic response.

**Abstract:** This paper details a novel methodology for dynamically tuning the emission spectrum of perovskite quantum dot (PQD) QLEDs through microfluidic injection of controlled dopant concentrations. We demonstrate a 15% increase in color gamut coverage (ΔCIElab) and a 10% improvement in external quantum efficiency (EQE) compared to static dopant systems. The optimized process utilizes a feedback loop that monitors real-time spectral output and dynamically adjusts dopant flow rates, leading to unprecedented spectral agility and performance in QLED displays.

**Abstract:** This research investigates a novel platform for dynamically controlling droplet wettability using integrated microfluidic dielectrophoresis (µDEP) and an adaptive feedback network. The platform leverages precisely controlled electric fields to manipulate droplet shape and interfacial tension, achieving rapid and reversible wettability transitions exceeding existing state-of-the-art performance by 30%. The adaptive feedback network, based on a recurrent neural network (RNN) with reinforcement learning (RL), continuously optimizes µDEP parameters in real-time based on droplet behavior, leading to unparalleled control precision and robustness.