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

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

11.1K
A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning...
11.1K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

13.1K
This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners’ engagement during a task.
13.1K
Aqueous Synthesis of Plasmonic Gold-Tin Alloy Nanoparticles03:54

Aqueous Synthesis of Plasmonic Gold-Tin Alloy Nanoparticles

1.4K
Here, the synthesis of gold (Au) seeds is described using the Turkevich method. These seeds are then used to synthesize gold-tin alloy (Au-Sn) nanoparticles with tunable plasmonic...
1.4K
A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation11:38

A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation

1.1K
Available pluripotent stem cell (PSC)-to-functional cell differentiation systems are currently impeded by problems of severe line-to-line and batch-to-batch variability. Here, using cardiac differentiation as the main example, we present a protocol to intelligently monitor and modulate the process of PSC differentiation based on image-based machine learning.
1.1K
Nanocrystalline Alloys and Nano-grain Size Stability06:52

Nanocrystalline Alloys and Nano-grain Size Stability

5.6K
Source: Sina Shahbazmohamadi and Peiman Shahbeigi-Roodposhti-Roodposhti, School of Engineering, University of Connecticut, Storrs, CT
Alloys with grain size less than 100 nm are known as nanocrystaline alloys. Due to their enhanced physical and mechanical properties, there is an ever-increasing demand to employ them in various industries such as semiconductor, biosensors and aerospace. 
To improve the processing and application of nanocrystalline alloys, it is necessary to develop close to...
5.6K
Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory07:59

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

8.4K
The present experiment combined three experimental procedures — a retrieval-practice learning manipulation, a list-discrimination task, and a stress-induction technique — to examine the influences of different learning strategies and acute stress on multiple measures of episodic...
8.4K

You might also read

Related Articles

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

Sort by
Same author

Multi-Objective Catalyst Discovery in High-Entropy Alloy Composition Space: The Role of Noble Metals on the Pareto Front for Oxygen Reduction Reaction.

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

Direct synthesis of amorphous metal-organic frameworks from nanoclusters.

Nature communications·2026
Same author

The Computational Cation Electrode: A Case Study on CO2RR.

Chemphyschem : a European journal of chemical physics and physical chemistry·2026
Same author

Trapping a Metastable Node in an Amorphous Metal-Organic Framework.

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

Mapping the Coordination Number and Coordination Geometry of Lanthanide Ions in Aqueous and Nonaqueous Solution Phases.

Journal of the American Chemical Society·2026
Same author

Continuous Ammonia Electrosynthesis from Nitrogen and Water in a Monolithic Pd Membrane-Based Flow Cell.

ACS energy letters·2026

Related Experiment Video

Updated: Jan 20, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.1K

Learning in higher dimensions: a strategy for alloy electrocatalyst discovery.

Vladislav A Mints1,2, Jack K Pedersen3, Gustav K H Wiberg1

  • 1Department for Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3 3012 Bern Switzerland matthias.arenz@unibe.ch.

EES Catalysis
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a top-down approach for discovering better energy conversion electrocatalysts. By starting with complex alloys and removing elements, researchers efficiently identified optimal materials, reducing experimental effort.

More Related Videos

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.1K
Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing
03:54

Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing

Published on: March 15, 2024

1.4K

Related Experiment Videos

Last Updated: Jan 20, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.1K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.1K
Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing
03:54

Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing

Published on: March 15, 2024

1.4K

Area of Science:

  • Materials Science
  • Electrochemistry
  • Catalysis

Background:

  • Traditional catalyst discovery uses a bottom-up approach, which is time-consuming.
  • Complex alloys offer potential for improved catalytic performance but are challenging to study comprehensively.

Purpose of the Study:

  • To demonstrate a novel top-down strategy for discovering energy conversion electrocatalysts.
  • To develop a data-driven method that reduces the number of experiments needed for catalyst optimization.

Main Methods:

  • A top-down approach was employed, starting with a complex high entropy alloy (HEA) containing multiple elements.
  • Element down-selection was performed by removing low-performing constituents from the alloy.
  • A machine-learned activity model was created based on experimental data from 200 alloy compositions.

Main Results:

  • The study successfully applied the top-down approach to the Au-Ir-Os-Pd-Pt-Re-Rh-Ru HEA system for the oxygen reduction reaction (ORR).
  • The machine-learned model demonstrated predictive power for the activity of less complex alloys within the HEA space.
  • This method significantly reduces the experimental burden compared to individually studying all constituent alloys.

Conclusions:

  • The top-down approach is an efficient strategy for discovering improved electrocatalysts.
  • This method facilitates the comparison of experimental data with theoretical simulations for catalyst activity modeling.
  • The developed machine-learned model can predict the performance of simpler alloys derived from complex HEAs.