Maxx Yung | Searcharxiv

arXiv2025

Design, Simulation, and Fabrication of a Hexagonal Microfluidic Platform for Culturing Neurons

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 ($34.64\,\mathrm{\mu m}$ side length) in a honeycomb array connected by $20\,\mathrm{\mu m}$ 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 $0.1$ - $1\,\mathrm{\mu L/min}$, simulations revealed the extrapolated pressure differential across the full $50{,}000\,\mathrm{\mu m}$ 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.

physics.flu-dyn↗

arXiv2024

AI-Driven Physics-Informed Bio-Silicon Intelligence System: Integrating Hybrid Systems, Biocomputing, Neural Networks, and Machine Learning, for Advanced Neurotechnology

We present the Bio-Silicon Intelligence System (BSIS), an innovative hybrid platform that integrates biological neural networks with silicon-based computing. The BSIS, a Physics-Informed Hybrid Hierarchical Reinforcement Learning State Machine, employs carbon nanotube-coated electrodes to interface rat brains with computational systems, enabling high-fidelity neural interfacing and bidirectional communication through self-organizing systems in both biological and silicon forms. Our system leverages both analogue and digital AI theory, incorporating concepts from computational theory, chaos theory, dynamical systems theory, physics, and quantum mechanics. Additionally, the BSIS replicates the neuronal dynamics typical of intelligent brain tissue, employing nonlinear operations underlying learning and information storage. Neural signals are read through the FreeEEG32 board and BrainFlow software, then features are extracted and mapped to game actions by tracking feature changes in continuous data. Metadata is encoded into both analogue and digital brain stimulation signals at the microvolt level using our proprietary software and hardware. The system employs a dual signaling approach for training the rat brain, incorporating a reward solution and sound as well as human-inaudible distress sounds. This paper details the design, theory, functionality, and technical specifications of the BSIS, highlighting its interdisciplinary approach and advanced technological integration.

q-bio.NC↗