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Related Concept Videos

Phase Transitions: Vaporization and Condensation02:39

Phase Transitions: Vaporization and Condensation

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The physical form of a substance changes on changing its temperature. For example, raising the temperature of a liquid causes the liquid to vaporize (convert into vapor). The process is called vaporization—a surface phenomenon. Vaporization occurs when the thermal motion of the molecules overcome the intermolecular forces, and the molecules (at the surface) escape into the gaseous state. When a liquid vaporizes in a closed container, gas molecules cannot escape. As these gas phase molecules...
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Related Experiment Video

Updated: Jan 13, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Analysis of Multiscale Condensation Phenomena Using a Zero-Shot Computer Vision Framework.

Donghyeong Lee1, Seokwan Roh1, Jaewoo Jeong1

  • 1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

A new AI framework accurately quantifies condensation dynamics by analyzing millions of droplets without prior training. This computer vision approach aids in designing better systems for energy and water applications.

Keywords:
computer visiondroplet condensationphase‐change phenomena

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Area of Science:

  • Multiphase flow
  • Surface science
  • Artificial intelligence

Background:

  • Condensation is crucial for energy and water systems.
  • Quantifying multiscale droplet dynamics is challenging.

Purpose of the Study:

  • Develop a label-free computer vision framework for condensation analysis.
  • Enable accurate quantification of droplet dynamics and condensation rates.

Main Methods:

  • Leveraged the Segment Anything Model (SAM) for zero-shot segmentation.
  • Detected over one million droplets without annotated data.
  • Extracted statistical features (radius, coalescence, mass) and trained a machine learning model for rate prediction.

Main Results:

  • Achieved high accuracy in droplet detection and characterization.
  • Visualized the complete dynamic condensation cycle.
  • Identified contact angle hysteresis as a key factor in droplet behavior.
  • Predicted condensation rates with a 7.8% mean absolute percentage error.

Conclusions:

  • AI frameworks can significantly advance the understanding of dynamic phase-change mechanisms.
  • The developed methods can guide the design of improved surfaces for thermal management, desalination, and water harvesting.