Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Dec 9, 2025

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

Pendant drop tensiometry: A machine learning approach.

Felix S Kratz¹, Jan Kierfeld¹

¹Department of Physics, TU Dortmund University, Dortmund, Germany.

The Journal of Chemical Physics

|September 6, 2020

Summary

This summary is machine-generated.

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

Adsorption of finite semiflexible polymers and their loop and tail distributions.

The Journal of chemical physics·2017

Same journal

The influence of chirality on the macroscopic behavior of multiferroic smectic phases.

The Journal of chemical physics·2026

Same journal

Polaron transformed canonically consistent quantum master equation.

The Journal of chemical physics·2026

Same journal

The x-ray absorption spectrum of the propargyl radical C3H3●.

The Journal of chemical physics·2026

Same journal

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. I. Conformer- and isomer-resolved infrared spectra.

The Journal of chemical physics·2026

Same journal

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. II. Isomer-resolved unimolecular dynamics.

The Journal of chemical physics·2026

Same journal

Quantum state-to-state dynamics studies of the C(3P) + OH(X2Π) → CO(a3Π) + H(2S) reaction based on a new HCO(12A″) potential energy surface.

The Journal of chemical physics·2026

See all related articles

We developed a fast and precise machine learning method using deep neural networks to measure surface tension from pendant drop shapes. This AI approach significantly improves upon traditional numerical solutions for analyzing droplet images.

Area of Science:

Physics
Materials Science
Computational Science

Background:

Pendant drop tensiometry traditionally uses numerical solutions of the Young-Laplace equation to determine surface tension.
Current methods require repeated solving of the equation to fit droplet images, which is computationally intensive.

Purpose of the Study:

To introduce a more computationally efficient machine learning approach for determining surface tension from pendant drop shapes.
To demonstrate the superiority of deep learning over conventional shape-fitting techniques.

Main Methods:

Training a deep neural network on a large dataset of numerically generated pendant droplet shapes.
Utilizing droplet shape data sensitive to surface tension for optimized training.
Analyzing the role of the Worthington number in both conventional and machine learning fitting.

More Related Videos

Author Spotlight: Integrating Mechanical and Biological Analysis in Tendinopathy Research

Author Spotlight: Integrating Mechanical and Biological Analysis in Tendinopathy Research

Published on: March 1, 2024

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Dec 9, 2025

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

Author Spotlight: Integrating Mechanical and Biological Analysis in Tendinopathy Research

Author Spotlight: Integrating Mechanical and Biological Analysis in Tendinopathy Research

Published on: March 1, 2024

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Main Results:

The deep learning approach significantly outperforms state-of-the-art shape fitting in terms of speed and precision.
The method accurately determines surface tension from single images of pendant drops.
The study clarifies the Worthington number's importance for training set optimization.

Conclusions:

Deep neural networks offer a computationally efficient and highly precise alternative for surface tension measurement.
This machine learning methodology is applicable to material parameter determination in rheological experiments.
The findings highlight the potential of AI in advanced material characterization.