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

Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...
Olefin Metathesis Polymerization: Overview01:13

Olefin Metathesis Polymerization: Overview

Recently, the development of olefin metathesis polymerization advanced the field of polymer synthesis. Simply put, the reorganization of substituents on their double bonds between two olefins in the presence of a catalyst is known as the olefin metathesis reaction. The use of metathesis reaction for polymer synthesis is called olefin metathesis polymerization.
Ruthenium-based Grubbs catalyst is the most commonly used catalyst for olefin metathesis polymerization. Grubbs catalyst consists of a...
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...

You might also read

Related Articles

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

Sort by
Same author

White-Light-Excitable Deep-Red/NIR Organic Afterglow Nanoparticles for High-Contrast In Vivo Imaging.

Angewandte Chemie (International ed. in English)·2026
Same author

A Robotic High-Throughput Grid-Search Platform for Mapping Phase Behavior in Triblock Copolymer-Homopolymer Blends.

ACS nano·2026
Same author

Plasma signals of lung tumor promotion for molecular cancer prevention.

Cell·2026
Same author

Dynamic permeability in metastable droplet interfacial bilayers.

Soft matter·2026
Same author

Approaching-unity PLQY and high stretchability in polymer emitters via molecular spacers.

Nature communications·2026
Same author

Associations of 6600 SomaScan proteins with demographic, lifestyle, environmental and health characteristics in Chinese adults.

Scientific reports·2026
Same journal

Catalyst-Controlled Regiodivergent C-H Olefination of Furanyl Carbamates through a Rational Approach.

JACS Au·2026
Same journal

Charting the Biosynthetic Landscape of Hybrid Polyketide-Nonribosomal Peptide-Specialized Lipids.

JACS Au·2026
Same journal

Valence-State-Dependent Surface Lattice Oxygen in CeO<sub>2</sub>‑Modified VPO Catalysts: Elucidating the Mechanism of <i>n</i>‑Butane Selective Oxidation to Maleic Anhydride.

JACS Au·2026
Same journal

Quantitative Insights into Pressure-Dependent Mass Transport and Reaction Kinetics in Electrochemical CO<sub>2</sub> Reduction.

JACS Au·2026
Same journal

3‑Methylthiopropionic Acid Kills Carbapenem-Resistant <i>Klebsiella pneumoniae</i> by Disrupting Membrane Integrity and Bioenergetics.

JACS Au·2026
Same journal

Tunable Natural and Magnetic Circularly Polarized Luminescence in the UVB Region from a Molecular Gd(III) Complex.

JACS Au·2026
See all related articles
  1. Home
  3. Automated And High-throughput Phase Separation Control For Supramolecular Polymer Blends Enabled By Machine Learning.
  1. Home
  3. Automated And High-throughput Phase Separation Control For Supramolecular Polymer Blends Enabled By Machine Learning.

Related Experiment Video

MALDI-ToF MS Method for the Characterization of Synthetic Polymers with Varying Dispersity and End Groups
06:16

MALDI-ToF MS Method for the Characterization of Synthetic Polymers with Varying Dispersity and End Groups

Published on: October 3, 2025

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning.

Yunfei Wang1,2, Daniel Struble1, Saroj Upreti1

  • 1School of Polymer Science and Engineering, Center for Optoelectronic Materials and Devices, the University of Southern Mississippi, Hattiesburg, Mississippi 39406, United States.

JACS Au
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed a high-throughput workflow using machine learning to accelerate the discovery of supramolecular polymer blends (SPBs). This data-driven approach enables predictive models for designing SPBs with desired morphologies and properties.

Keywords:
ML-guided polymer designautomation and high-throughput materials discoveryhigh-throughput characterizationsupramolecular polymer blends

More Related Videos

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles
14:37

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles

Published on: July 6, 2012

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries
10:58

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries

Published on: September 6, 2012

Related Experiment Videos

MALDI-ToF MS Method for the Characterization of Synthetic Polymers with Varying Dispersity and End Groups
06:16

MALDI-ToF MS Method for the Characterization of Synthetic Polymers with Varying Dispersity and End Groups

Published on: October 3, 2025

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles
14:37

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles

Published on: July 6, 2012

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries
10:58

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries

Published on: September 6, 2012

Area of Science:

  • Materials Science
  • Polymer Science
  • Computational Chemistry

Background:

  • Supramolecular polymer blends (SPBs) possess tunable morphologies crucial for macroscopic properties.
  • Rational design of SPBs is hindered by a lack of predictive structure-morphology models.

Purpose of the Study:

  • To establish a data-driven, high-throughput workflow for accelerated discovery of supramolecular polymer blends.
  • To develop predictive models for correlating SPB structure with morphology.

Main Methods:

  • Modular synthesis of 33 hydrogen-bonding end-functional homopolymers.
  • Robotic formulation to create 260 SPBs.
  • Automated atomic force microscopy (AFM) for morphology characterization.
  • Machine learning (ML), specifically Support Vector Regression (SVR), for model training.

Main Results:

  • Generated 260 SPBs and 2340 AFM morphology datasets rapidly.
  • Developed an SVR model that accurately predicted phase-separation sizes.
  • Experimental validation confirmed the model's predictive accuracy for target sizes (50, 100, 150 nm).

Conclusions:

  • Coupling high-throughput experimentation with ML significantly accelerates morphology discovery in SPBs.
  • This work provides a large-scale dataset for supramolecular polymer systems.
  • The workflow enables more rational design of SPBs with targeted properties.