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

Insights of Photocatalytic Properties of Fe/TiO<sub>2</sub> Bio-Based Particles: Experimental and Modeling Design Toward Methyl Orange Photodegradation.

Entropy (Basel, Switzerland)·2026
Same author

Correlation Between Manufacturing Conditions, Microstructure, and Electrical-Mechanical Properties of Cu Matrix Composites.

Materials (Basel, Switzerland)·2026
Same author

High Entropy Alloys Database generated with Large Language Model.

Scientific data·2026
Same author

Thermal and Mechanical Properties of Silica-Reinforced SBR/NR/NBR Rubber Composites for Boot Tread Production.

Polymers·2026
Same author

Thermally Activated Composite Y<sub>2</sub>O<sub>3</sub>-bTiO<sub>2</sub> as an Efficient Photocatalyst for Degradation of Azo Dye Reactive Black 5.

Molecules (Basel, Switzerland)·2026
Same author

Effect of an Innovative Online Ayurveda Program for Detox and Lifestyle on Mental and Physical Health in Home-Based Adults: A Pilot Study.

Journal of integrative and complementary medicine·2025

Related Experiment Video

Updated: Jul 13, 2025

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications
09:56

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Published on: December 8, 2015

10.8K

Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning.

Gordana Marković1, Vaso Manojlović2, Jovana Ružić3

  • 1Institute for Technology of Nuclear and Other Mineral Raw Materials, 11000 Belgrade, Serbia.

Materials (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning identified specific heat as key to lowering titanium alloy Young's modulus. This research predicts new biocompatible titanium alloys with a low Young's modulus for medical applications.

Keywords:
Extra Tree RegressionMonte Carlo methodYoung’s modulusmachine learningtitanium alloys

More Related Videos

Plasma Polishing as a New Polishing Option to Reduce the Surface Roughness of Porous Titanium Alloy for 3D Printing
06:12

Plasma Polishing as a New Polishing Option to Reduce the Surface Roughness of Porous Titanium Alloy for 3D Printing

Published on: April 28, 2023

1.8K
Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo
12:19

Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo

Published on: July 1, 2013

10.9K

Related Experiment Videos

Last Updated: Jul 13, 2025

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications
09:56

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Published on: December 8, 2015

10.8K
Plasma Polishing as a New Polishing Option to Reduce the Surface Roughness of Porous Titanium Alloy for 3D Printing
06:12

Plasma Polishing as a New Polishing Option to Reduce the Surface Roughness of Porous Titanium Alloy for 3D Printing

Published on: April 28, 2023

1.8K
Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo
12:19

Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo

Published on: July 1, 2013

10.9K

Area of Science:

  • Materials Science
  • Biocompatible Materials
  • Computational Materials Science

Background:

  • Titanium alloys are widely used in orthopedic and dental implants.
  • There is a growing need for titanium alloys with a low Young's modulus and without cytotoxic elements.

Purpose of the Study:

  • To analyze biocompatible titanium alloys using machine learning.
  • To predict the composition of new titanium alloys with a low Young's modulus.

Main Methods:

  • A database of 246 biocompatible titanium alloys was compiled, including composition and properties.
  • Extra Tree Regression model was developed to predict Young's modulus.
  • Monte Carlo simulations were performed to predict future alloy compositions.

Main Results:

  • Specific heat was identified as the most influential parameter for lowering the Young's modulus.
  • Machine learning models successfully predicted Young's modulus for titanium alloys.
  • Simulations indicated the possibility of creating multicomponent alloys with a Young's modulus below 70 GPa.

Conclusions:

  • Machine learning effectively analyzes and predicts properties of biocompatible titanium alloys.
  • New titanium alloy compositions, primarily containing titanium, zirconium, tin, manganese, and niobium, can achieve desired low Young's modulus values.
  • This research paves the way for developing advanced biocompatible materials for medical applications.