Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
  1. Home
  3. Machine Learning-driven Capillary Microfluidic Design Automation For Programmable Gradient Generation And Antimicrobial Testing.
  1. Home
  3. Machine Learning-driven Capillary Microfluidic Design Automation For Programmable Gradient Generation And Antimicrobial Testing.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A review of magnesium-based stents: manufacturing strategies and future trends.

Progress in biomedical engineering (Bristol, England)·2026
Same author

Cap-Sweat: a capillary microfluidic platform for digitized sweat sampling and time-resolved biomarker analysis.

Lab on a chip·2026
Same author

CapSense-Flex: A Self-Powered Capillary Lab-on-Chip for Universal Electrochemical Biosensing.

ACS sensors·2026
Same author

Associations between perceived social support, self-efficacy, and health-promoting behaviors in hospitalized heart failure patients: a cross-sectional study.

BMC cardiovascular disorders·2026
Same author

Electrical Conductivity of Nanofluids Containing Small Extracellular Vesicles.

ACS omega·2026
Same author

Nursing Managers' Coronary Care Unit Management Experience.

Iranian journal of nursing and midwifery research·2025
Same journal

Vapor-Phase Deposition of CsPbBr<sub>3</sub> Shells on Iodide-Rich Perovskite Cores in SiO<sub>2</sub> for Efficient and Robust Red Emission.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

High-Efficiency Generation of Vectorial Terahertz Beams via Surface-Wave-Excited Dielectric Metasurfaces.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Stabilizing Lattice Oxygen Redox Through Bicarbonate Pyrolysis-Driven Multifunctional Interface Engineering in Li-Rich Layered Oxides.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Achieving Excellent Nonlinear Optics in α‑Ba<sub>2</sub>Ge<sub>4</sub>S<sub>10</sub> via Flux‑Induced Symmetry Breaking.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Anisotropic Swelling of a Single-Crystalline Hydrogen-Bonded Organic Framework Induced by Iodine Vapor Uptake.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Engineering Side-Chain Steric Effects to Build Selective COF Channels for Polysulfide Suppression in Li-S Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2026
See all related articles

Related Experiment Video

A Gradient-generating Microfluidic Device for Cell Biology
11:05

A Gradient-generating Microfluidic Device for Cell Biology

Published on: August 30, 2007

Machine Learning-Driven Capillary Microfluidic Design Automation for Programmable Gradient Generation and

Mahmood Khalghollah1,2,3, Azam Zare2,3, Sorosh Abdollahi2,3

  • 1Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada.

Small (Weinheim an Der Bergstrasse, Germany)
|May 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

TCG-CMDA automates microfluidic chip design for rapid antimicrobial susceptibility testing. This machine learning platform enables portable, point-of-care minimum inhibitory concentration (MIC) determination without pumps or valves.

Keywords:
capillary microfluidicdesign automationmachine learningminimum inhibitory concentration testingneural networktree‐shaped concentration gradient generator

More Related Videos

A Microfluidic Device for Quantifying Bacterial Chemotaxis in Stable Concentration Gradients
09:28

A Microfluidic Device for Quantifying Bacterial Chemotaxis in Stable Concentration Gradients

Published on: April 19, 2010

Related Experiment Videos

A Gradient-generating Microfluidic Device for Cell Biology
11:05

A Gradient-generating Microfluidic Device for Cell Biology

Published on: August 30, 2007

A Microfluidic Device for Quantifying Bacterial Chemotaxis in Stable Concentration Gradients
09:28

A Microfluidic Device for Quantifying Bacterial Chemotaxis in Stable Concentration Gradients

Published on: April 19, 2010

Area of Science:

  • Biomedical Engineering
  • Microfluidics
  • Machine Learning

Background:

  • Accurate antimicrobial susceptibility testing (AST) is vital for combating antimicrobial resistance, especially in decentralized healthcare settings.
  • Current AST methods can be slow and require specialized equipment, limiting their use in point-of-care applications.
  • Automated, portable solutions are needed for rapid minimum inhibitory concentration (MIC) determination.

Purpose of the Study:

  • To develop a machine learning-guided platform (TCG-CMDA) for automated design of capillary microfluidic chips.
  • To enable passive, pump-free generation of programmable concentration gradients for AST.
  • To create a streamlined workflow for decentralized, point-of-care antimicrobial susceptibility testing.

Main Methods:

  • TCG-CMDA integrates computational fluid dynamics (CFD) simulations, a neural network surrogate model, and quasi-Newton optimization.
  • The platform designs tree-shaped microfluidic geometries for synchronized, capillary-driven flow of two agents.
  • Validation involved dye flow visualization and minimum inhibitory concentration (MIC) testing of Escherichia coli against gentamicin.

Main Results:

  • TCG-CMDA successfully designed microfluidic chips that passively generate precise concentration gradients.
  • The system demonstrated synchronized, capillary-driven flow without external pumps or valves.
  • MIC testing showed high concordance with conventional antibiotic susceptibility testing, with a streamlined workflow of approximately 2 hours.

Conclusions:

  • TCG-CMDA offers a novel, simulation-guided design paradigm for capillary microfluidic systems.
  • The platform facilitates rapid, automated, and portable antimicrobial susceptibility testing for decentralized settings.
  • The TCG-CMDA framework is generalizable to other gradient-dependent microfluidic applications beyond AST.