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

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

Clinical translation and landscape of stimuli-responsive nanomedicines and microscale therapeutics.

Chemical Society reviews·2026
Same author

Validation of an automated AI-based micro-CT organ segmentation workflow against expert annotations and its impact on fluorescence quantification.

European radiology experimental·2026
Same author

(Hybrid) SPECT and PET Technologies.

Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer·2026
Same author

Molecular Ultrasound Imaging.

Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer·2026
Same author

Uncertainty-aware gamma interaction localization and reconstruction in PET.

Medical physics·2026
Same author

A preclinical CT and MRI Liver Imaging Dataset with Anatomical, Functional and Segmentation Data.

Scientific data·2026

Related Experiment Video

Updated: Jun 5, 2025

Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension
09:33

Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension

Published on: September 11, 2020

6.1K

A Generative Adversarial Network Approach to Predict Nanoparticle Size in Microfluidics.

Sara Mihandoost1, Sima Rezvantalab2, Roger M Pallares3

  • 1Electrical Engineering Department, Urmia University of Technology, Urmia 57166-419, Iran.

ACS Biomaterials Science & Engineering
|December 12, 2024
PubMed
Summary

Controlling nanoparticle size in microfluidics is key for drug delivery systems. Machine learning accurately predicts poly(lactic-co-glycolic acid) nanoparticle size, identifying synthesis method, PVA concentration, and LA/GA ratio as critical factors.

Keywords:
PLGAdata miningdecision treelinear regressionmachine learningrandom forest

More Related Videos

Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles
11:13

Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles

Published on: March 13, 2016

10.6K
Generation of Size-controlled Poly ethylene Glycol Diacrylate Droplets via Semi-3-Dimensional Flow Focusing Microfluidic Devices
11:08

Generation of Size-controlled Poly ethylene Glycol Diacrylate Droplets via Semi-3-Dimensional Flow Focusing Microfluidic Devices

Published on: July 3, 2018

7.7K

Related Experiment Videos

Last Updated: Jun 5, 2025

Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension
09:33

Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension

Published on: September 11, 2020

6.1K
Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles
11:13

Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles

Published on: March 13, 2016

10.6K
Generation of Size-controlled Poly ethylene Glycol Diacrylate Droplets via Semi-3-Dimensional Flow Focusing Microfluidic Devices
11:08

Generation of Size-controlled Poly ethylene Glycol Diacrylate Droplets via Semi-3-Dimensional Flow Focusing Microfluidic Devices

Published on: July 3, 2018

7.7K

Area of Science:

  • Materials Science and Engineering
  • Biomedical Engineering
  • Chemical Engineering

Background:

  • Nanoparticles (NPs) are crucial for advanced drug delivery systems (DDS).
  • Precise control over NP size in microfluidic synthesis is essential for optimizing DDS performance.
  • Poly(lactic-co-glycolic acid) (PLGA) NPs are widely investigated for drug delivery applications.

Purpose of the Study:

  • To develop accurate predictive models for poly(lactic-co-glycolic acid) (PLGA) nanoparticle size synthesized via microfluidics.
  • To identify key parameters influencing NP size in microfluidic systems.
  • To enhance the reliability and accuracy of NP size prediction using machine learning.

Main Methods:

  • A comprehensive database of over 1100 data points was curated from extensive literature review.
  • Tabular Generative Adversarial Network (TGAN) was utilized for data augmentation to improve dataset reliability.
  • Multiple machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Deep Neural Networks (DNN), Linear Regression (LR), Support Vector Regression (SVR), and Gradient Boosting (GB), were employed for NP size prediction.

Main Results:

  • The Decision Tree (DT) algorithm demonstrated the highest accuracy in predicting NP size, with an average prediction error of 8%.
  • Simulations highlighted the significant impact of synthesis method, poly(vinyl alcohol) (PVA) concentration, and the lactide-to-glycolide (LA/GA) ratio of PLGA copolymers on NP size.
  • Data enhancement using TGAN improved the overall prediction accuracy and reliability of the machine learning models.

Conclusions:

  • Machine learning, particularly Decision Tree, offers a robust approach for predicting PLGA NP size in microfluidic synthesis.
  • Key formulation parameters such as PVA concentration and LA/GA ratio are critical determinants of NP size, enabling better control over DDS characteristics.
  • This study provides valuable insights for optimizing microfluidic synthesis of PLGA NPs for enhanced drug delivery applications.