{"ID":2887862,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00425","arxiv_id":"2508.00425","title":"Design, Simulation, and Fabrication of a Hexagonal Microfluidic Platform for Culturing Neurons","abstract":"Developing an organoid computing platform from neurons in vitro demands stable, precisely controlled microenvironments. To address this requirement, we designed, simulated, and fabricated a microfluidic device featuring hexagonal wells ($34.64\\,\\mathrm{μm}$ side length) in a honeycomb array connected by $20\\,\\mathrm{μm}$ channels. Computational fluid dynamics (CFD) modeling, validated by high mesh quality ($0.934$ orthogonal quality) and robust convergence, confirmed the architecture supports flow regimes ideal for ensuring cell viability. At target flow rates of $0.1$ - $1\\,\\mathrm{μL/min}$, simulations revealed the extrapolated pressure differential across the full $50{,}000\\,\\mathrm{μm}$ device remains within stable operating limits at $177\\,\\mathrm{kPa}$ (average) and $329\\,\\mathrm{kPa}$ (maximum). Photolithography successfully produced this architecture, with only minor corner rounding observed at feature interfaces. This work therefore establishes a computationally validated and fabricated platform, paving the way for experimental flow characterization and subsequent neural integration.","short_abstract":"Developing an organoid computing platform from neurons in vitro demands stable, precisely controlled microenvironments. To address this requirement, we designed, simulated, and fabricated a microfluidic device featuring hexagonal wells ($34.64\\,\\mathrm{μm}$ side length) in a honeycomb array connected by $20\\,\\mathrm{μm...","url_abs":"https://arxiv.org/abs/2508.00425","url_pdf":"https://arxiv.org/pdf/2508.00425v1","authors":"[\"Maxx Yung\"]","published":"2025-08-01T08:27:03Z","proceeding":"physics.flu-dyn","tasks":"[\"physics.flu-dyn\",\"physics.bio-ph\",\"q-bio.NC\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}