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Related Concept Videos

Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
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Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

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Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
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Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

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Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
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Diffusion01:12

Diffusion

215.7K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
215.7K
Diffusion01:21

Diffusion

6.1K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Related Experiment Video

Updated: Jan 8, 2026

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer
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Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

Published on: October 15, 2015

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Invisible watermarking framework for unlearned diffusion model in online service.

Linlin Wang1, Tianqing Zhu1, Longxiang Gao2

  • 1Faculty of Data Science, city University of Macau, Macao, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced invisible watermarking technique for diffusion models, ensuring copyright protection and content integrity during machine unlearning. The method preserves watermark effectiveness without degrading image quality or unlearning functionality.

Keywords:
Diffusion modelsImage generationMachine unlearningOnline serviceWatermarking

Related Experiment Videos

Last Updated: Jan 8, 2026

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer
07:54

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

Published on: October 15, 2015

8.4K

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Digital Forensics

Background:

  • Diffusion models excel at image generation but raise copyright and ethical concerns.
  • Machine unlearning and watermarking are crucial for addressing these issues.
  • Existing methods face conflicts between unlearning and watermarking.

Purpose of the Study:

  • To propose an advanced invisible watermarking technique for unlearned diffusion models.
  • To ensure copyright protection and ethical content generation.
  • To resolve conflicts between machine unlearning and watermarking.

Main Methods:

  • Developed an advanced invisible watermarking technique integrated with unlearned diffusion models.
  • Designed an optimized noise generation process for watermark embedding.
  • Improved the watermark generator for adaptive embedding.

Main Results:

  • Successfully embedded watermarks without compromising unlearning operations.
  • Preserved watermark invisibility, robustness, and detectability.
  • Ensured generated content quality was not degraded by unlearning.

Conclusions:

  • The proposed technique effectively balances copyright protection and content removal in diffusion models.
  • This approach enables robust and adaptive watermarking in unlearned models.
  • It addresses the challenges of ethical AI and intellectual property in generative models.