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A number of natural and synthetic materials exhibit selective permeation, meaning that only molecules or ions of a certain size, shape, polarity, charge, and so forth, are capable of passing through (permeating) the material. Biological cell membranes provide elegant examples of selective permeation in nature, while dialysis tubing used to remove metabolic wastes from blood is a more simplistic technological example. Regardless of how they may be fabricated, these materials are generally...
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Osmotic pressure beyond concentration restrictions.

Alessandro Grattoni1, Manuele Merlo, Mauro Ferrari

  • 1Dipartimento di Meccanica, Politecnico di Torino, C so Duca degli Abruzzi, 24, 10129, Torino, Italy. alessandro.grattoni@uth.tmc.edu

The Journal of Physical Chemistry. B
|September 21, 2007
PubMed
Summary

This study evaluates predictive osmosis theories against new experimental data and literature analysis. It compares classical models with a novel, parameter-free mechanistic approach for broader concentration applicability.

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Area of Science:

  • Physical Chemistry
  • Biophysics
  • Chemical Engineering

Background:

  • Osmosis is a critical solvent transport process in biology and technology.
  • Classical osmosis models (van't Hoff, Morse) are limited to dilute solutions.
  • Existing extensions often require empirical parameters, reducing predictive power.

Purpose of the Study:

  • To assess the validity of predictive osmosis theories.
  • To compare classical models with a novel mechanistic approach.
  • To analyze experimental and literature data for osmosis modeling.

Main Methods:

  • Experimental validation of osmosis theories.
  • Meta-analysis of existing literature data on osmosis.
  • Comparison with a coupled diffusion model (Granik et al.).

Main Results:

  • Classical theories show limitations outside dilute regimes.
  • The novel mechanistic model demonstrates broader applicability.
  • Experimental data supports the mechanistic approach over empirical models.

Conclusions:

  • Predictive osmosis models need re-evaluation for wider applications.
  • Mechanistic, parameter-free models offer superior predictive capabilities.
  • This work advances the understanding and modeling of osmosis across concentration ranges.