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RESEARCH PAPER · Analytical Chemistry · 2022

Microfluidic Devices Controlled by Machine Learning with Failure Experiments

Kenta Fukada, Michiko Seyama

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18 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
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42 direct citing works

Microfluidic Devices Controlled by Machine Learning with Failure ExperimentsOriginalInertial microfluidics: current status, challenges, and future opportunities111Electrochemical fingerprinting combined with machine learning algorithm for closely related medicinal plant identification33Functions and applications of artificial intelligence in droplet microfluidics49Advances in Microfluidics: Technical Innovations and Applications in Diagnostics and Therapeutics75Machine learning for nanoplasmonics149Viscoelastic Particle Encapsulation Using a Hyaluronic Acid Solution in a T-Junction Microfluidic Device12Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems54Closed-loop control systems for pumps used in portable analytical systems13The mechanisms and properties of inertial microfluidics: from fundamental models to biomedical applications15Low-cost inertial microfluidic device for microparticle separation: A laser-Ablated PMMA lab-on-a-chip approach without a cleanroom27Integrating machine learning and biosensors in microfluidic devices: A review73Design automation for deterministic lateral displacement by leveraging deep Q-network1Synergizing microfluidics and machine learning for next generation intelligent Lab-on-a-Chip devices8Enhancing Microparticle Separation Efficiency in Acoustofluidic Chips via Machine Learning and Numerical Modeling7Computational methods for inertial microfluidics: recent advances and future perspectives8Machine Learning-Driven Optimization of Viscoelastic Microfluidic Particle Separation0From microscale transport to data-driven intelligence: The convergence of microfluidics and machine learning0Deep Learning-enabled fast prediction of transient concentration gradients and mass transport in microfluidic systems with embedded cavities0

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  1. Inertial microfluidics: current status, challenges, and future opportunities

    Lab on a Chip · 2022-01-01

    Nan Xiang, Zhonghua Ni

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  2. Electrochemical fingerprinting combined with machine learning algorithm for closely related medicinal plant identification

    Sensors and Actuators B Chemical · 2022-10-31

    Qi Xiao, Zhenzeng Zhou, Zijie Shen, Jiandan Chen, Chunchuan Gu, Lihua Li, Fengnong Chen, Hongying Liu

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  3. Functions and applications of artificial intelligence in droplet microfluidics

    Lab on a Chip · 2023-01-01

    Huan Liu, Lang Nan, Feng Chen, Yue Zhao, Yue Zhao, Yongxi Zhao, Yongxi Zhao

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  4. Advances in Microfluidics: Technical Innovations and Applications in Diagnostics and Therapeutics

    Analytical Chemistry · 2023-01-10

    Guillaume Aubry, Hyun Jee Lee, Hang Lu

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  5. Machine learning for nanoplasmonics

    Nature Nanotechnology · 2023-01-26

    Jean‐François Masson, John Simeon Biggins, Emilie Ringe

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  6. Viscoelastic Particle Encapsulation Using a Hyaluronic Acid Solution in a T-Junction Microfluidic Device

    Micromachines · 2023-02-27

    Anoshanth Jeyasountharan, Francesco Del Giudice

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  7. Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems

    International Journal of Pharmaceutics · 2023-03-11

    Aikaterini Dedeloudi, Edward Weaver, Dimitrios A. Lamprou

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  8. Closed-loop control systems for pumps used in portable analytical systems

    Journal of Chromatography A · 2023-03-15

    Suleman A. Naz, Van Thanh Huynh, Egan H. Doeven, Scott D. Adams, Abbas Z. Kouzani, Rosanne Marieke Guijt

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  9. The mechanisms and properties of inertial microfluidics: from fundamental models to biomedical applications

    Microfluidics and Nanofluidics · 2023-10-25

    Shlok Mishra, Joydeb Mukherjee, Deepa Chaturvedi, Ratnesh D. Jain, Prajakta P. Dandekar

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  10. Low-cost inertial microfluidic device for microparticle separation: A laser-Ablated PMMA lab-on-a-chip approach without a cleanroom

    HardwareX · 2023-11-11

    Cristian F. Rodríguez, Paula Guzmán-Sastoque, Mónica Gantiva-Díaz, Saúl C. Gómez, Valentina Quezada, Carolina Muñoz-Camargo, Johann F. Osma, Luis H. Reyes, Juan C. Cruz

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  11. Integrating machine learning and biosensors in microfluidic devices: A review

    Biosensors and Bioelectronics · 2024-08-03

    Gianni Antonelli, Joanna Filippi, Michele D’Orazio, Giorgia Curci, Paola Casti, Arianna Mencattini, Eugenio Martinelli

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  12. Design automation for deterministic lateral displacement by leveraging deep Q-network

    Biomicrofluidics · 2025-03-01

    Yu‐Wei Chen, Yidan Zhang, Junchao Wang

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  13. Synergizing microfluidics and machine learning for next generation intelligent Lab-on-a-Chip devices

    Microchemical Journal · 2025-08-27

    Shobhit Das

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  14. Enhancing Microparticle Separation Efficiency in Acoustofluidic Chips via Machine Learning and Numerical Modeling

    Sensors · 2025-10-17

    Tamara Klymkovych, Natalia Bokla, Wojciech Zabierowski, Dmytro Klymkovych

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  15. Computational methods for inertial microfluidics: recent advances and future perspectives

    Microsystems & Nanoengineering · 2025-10-29

    Giuseppe Lauricella, Mohammad Moein Naderi, Benjamin Owen, Nima Mostafazadeh, Jian Zhou, Zhangli Peng, Ian Papautsky

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  16. Machine Learning-Driven Optimization of Viscoelastic Microfluidic Particle Separation

    Analytical Chemistry · 2026-04-21

    Qing Lu, Zhuoran Zhao, Zhinan Zhang, Xianting Ding

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  17. From microscale transport to data-driven intelligence: The convergence of microfluidics and machine learning

    Physics of Fluids · 2026-06-01

    Tanishq Khurana, Sourav Ganguly, Kiran Raj M

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  18. Deep Learning-enabled fast prediction of transient concentration gradients and mass transport in microfluidic systems with embedded cavities

    International Communications in Heat and Mass Transfer · 2026-09-22

    Jianhong Dong, Wen Liu, Yu Wang, Qing Yu, Junsheng Wang

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