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

Precipitate Formation and Particle Size Control01:16

Precipitate Formation and Particle Size Control

In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...

You might also read

Related Articles

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

Sort by
Same author

Real-Time Observation of Thermal Reshaping Mechanisms in Gold Nanostars.

Nano letters·2026
Same author

Clinical diagnosis of diabetes using machine learning and surface-enhanced Raman spectroscopy liquid biopsy: an exploratory study.

Nanoscale advances·2025
Same author

Unraveling the role of MAG, PTEN, and NOTCH1 in axonal regeneration: a network analysis and molecular dynamics study of siRNA/drugs/nanocarriers interactions.

Journal of translational medicine·2025
Same author

The binding mechanism of Covalent Organic Frameworks (COFs) to Calcitonin Gene-Related Peptide Receptors (CGRPRs).

International journal of biological macromolecules·2025
Same author

Sequential EXtreme Gradient Boosting-Based Descriptor Reduction for Size Prediction of Zwitterionic Polymer-Based Nanoparticles.

ACS omega·2025
Same author

Corrigendum to "3D printing of complicated GelMA-coated alginate/tri‑calcium silicate scaffold for accelerated bone regeneration" [Int. J. Biol. Macromol. 229 (2023) 636-653].

International journal of biological macromolecules·2025

Related Experiment Video

Updated: Jul 16, 2026

Hydrogel Nanoparticle Harvesting of Plasma or Urine for Detecting Low Abundance Proteins
10:05

Hydrogel Nanoparticle Harvesting of Plasma or Urine for Detecting Low Abundance Proteins

Published on: August 7, 2014

14.3K

Intelligence prediction of microfluidically prepared nanoparticles.

Nima Hanari1, Sara Mihandoost2, Sima Rezvantalab3

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

Scientific Reports
|October 28, 2025
PubMed
Summary

Machine learning models can predict drug loading and encapsulation efficiency for poly(lactic-co-glycolic) acid (PLGA) nanoparticles. This accelerates the design of advanced drug delivery systems with desired properties.

Keywords:
Data miningDrug loadingEncapsulation efficiencyMachine learning

More Related Videos

Flash NanoPrecipitation for the Encapsulation of Hydrophobic and Hydrophilic Compounds in Polymeric Nanoparticles
10:12

Flash NanoPrecipitation for the Encapsulation of Hydrophobic and Hydrophilic Compounds in Polymeric Nanoparticles

Published on: January 7, 2019

23.4K
Computer Numerical Control Micromilling of a Microfluidic Acrylic Device with a Staggered Restriction for Magnetic Nanoparticle-Based Immunoassays
09:58

Computer Numerical Control Micromilling of a Microfluidic Acrylic Device with a Staggered Restriction for Magnetic Nanoparticle-Based Immunoassays

Published on: June 23, 2022

2.6K

Related Experiment Videos

Last Updated: Jul 16, 2026

Hydrogel Nanoparticle Harvesting of Plasma or Urine for Detecting Low Abundance Proteins
10:05

Hydrogel Nanoparticle Harvesting of Plasma or Urine for Detecting Low Abundance Proteins

Published on: August 7, 2014

14.3K
Flash NanoPrecipitation for the Encapsulation of Hydrophobic and Hydrophilic Compounds in Polymeric Nanoparticles
10:12

Flash NanoPrecipitation for the Encapsulation of Hydrophobic and Hydrophilic Compounds in Polymeric Nanoparticles

Published on: January 7, 2019

23.4K
Computer Numerical Control Micromilling of a Microfluidic Acrylic Device with a Staggered Restriction for Magnetic Nanoparticle-Based Immunoassays
09:58

Computer Numerical Control Micromilling of a Microfluidic Acrylic Device with a Staggered Restriction for Magnetic Nanoparticle-Based Immunoassays

Published on: June 23, 2022

2.6K

Area of Science:

  • Biomaterials Science
  • Nanotechnology
  • Pharmaceutical Sciences

Background:

  • Developing poly(lactic-co-glycolic) acid (PLGA) nanoparticles is vital for drug delivery.
  • Controlling nanoparticle physicochemical properties for optimal drug encapsulation and loading remains a significant challenge.

Purpose of the Study:

  • To compile a comprehensive dataset of PLGA nanoparticle formulations.
  • To apply machine learning algorithms for predicting drug loading (DL) and encapsulation efficiency (EE).

Main Methods:

  • Compiled a dataset of over 300 PLGA nanoparticle formulations from literature.
  • Included 25 key features related to microfluidic preparation.
  • Utilized various machine learning algorithms, including random forest, to predict DL and EE.

Main Results:

  • The random forest model demonstrated high predictive performance.
  • Achieved R² values of 0.93 for DL and 0.96 for EE predictions.
  • Found minimal impact between EE and DL predictions, indicating distinct formulation insights.

Conclusions:

  • Machine learning effectively predicts key properties of PLGA nanoparticles.
  • This approach can guide the rational design of drug delivery systems.
  • Accelerates the development of nanoparticles with tailored drug loading and encapsulation efficiency.