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Surface Tension of Fluid01:22

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When a paint brush is immersed in water, the bristles wave freely inside the water. When it is taken out, the bristles stick together. The reason behind this effect is surface tension.
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Aqueous Solutions and Heats of Hydration02:42

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Deep Neural Networks for Image-Based Dietary Assessment
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Predicting hydration layers on surfaces using deep learning.

Yashasvi S Ranawat1, Ygor M Jaques1, Adam S Foster1,2

  • 1Department of Applied Physics, Aalto University Finland yashasvi.ranawat@aalto.fi ymjaques@gmail.com adam.foster@aalto.fi.

Nanoscale Advances
|September 22, 2022
PubMed
Summary
This summary is machine-generated.

We developed a deep learning model to rapidly predict water density at mineral surfaces. This advance aids understanding of natural processes and development of new technologies at the nanoscale mineral-water interface.

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Area of Science:

  • Geochemistry
  • Materials Science
  • Computational Science

Background:

  • Understanding the nanoscale mineral-water interface is crucial for natural processes like biomineralization and for developing advanced technologies.
  • Atomic force microscopy (AFM) provides high-resolution insights into solid-liquid interfaces, with potential for molecular-scale resolution.
  • Current theoretical methods for interpreting AFM data are computationally demanding and face interpretation challenges.

Purpose of the Study:

  • To develop a computationally efficient deep learning architecture for analyzing the mineral-water interface.
  • To enable rapid prediction of water density profiles at the solid-liquid interface.
  • To overcome the limitations of existing theoretical interpretation techniques.

Main Methods:

  • A novel deep learning architecture was designed and implemented.
  • The model was trained on data related to polymorphs of calcium carbonate.
  • The architecture learns the characteristics of the solid-liquid interface.

Main Results:

  • The deep learning model achieved reasonable accuracy in predicting water density profiles.
  • The developed approach significantly reduces the computational intensity of data interpretation.
  • The model demonstrates the potential for rapid analysis of nanoscale interfaces.

Conclusions:

  • Deep learning offers a powerful and efficient method for characterizing the nanoscale mineral-water interface.
  • This approach can accelerate research in fields relying on mineral-water interactions.
  • The study paves the way for faster development and understanding of mineral-based technologies.