ArticleBiomicrofluidics2023
Machine learning-augmented fluid dynamics simulations for micromixer educational module.
Article in Biomicrofluidics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- ML-automated microfluidic circuit design.Science advances · 2026Article
- Performance analysis of three-dimensional passive micromixers using k-means priority clustering with AHP-based sustainable design optimization.Scientific reports · 2025Article
- Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks.Micromachines · 2024Article
- Computational Fluid-Structure Interaction in Microfluidics.Micromachines · 2024Review
- Acoustohydrodynamic micromixers: Basic mixing principles, programmable mixing prospectives, and biomedical applications.Biomicrofluidics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Micromixers play an imperative role in chemical and biomedical systems. Designing compact micromixers for laminar flows owning a low Reynolds number is more challenging than flows with higher turbulence. Machine learning models can enable the optimization of the designs and capabilities of microfluidic systems by receiving input from a training library and producing algorithms that can predict the outcomes prior to the fabrication process to minimize development cost and time. Here, an educational interactive microfluidic module is developed to enable the design of compact and efficient micromixers at low Reynolds regimes for Newtonian and non-Newtonian fluids. The optimization of Newtonian fluids designs was based on a machine learning model, which was trained by simulating and calculating the mixing index of 1890 different micromixer designs. This approach utilized a combination of six design parameters and the results as an input data set to a two-layer deep neural network with 100 nodes in each hidden layer. A trained model was achieved with R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.