Percolation

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In coffee percolation, soluble compounds leave the coffee grounds and join the water to form coffee. Insoluble compounds (and granulates) remain within the coffee filter.

In physics, chemistry and materials science, percolation (from Latin percōlāre, "to filter" or "trickle through") refers to the movement and filtering of fluids through porous materials.

Background[edit]

During the last decades, percolation theory, an extensive mathematical studies model of percolation, has brought new understanding and techniques to a broad range of topics in physics, materials science, complex networks, epidemiology, and other fields . For example, in geology, percolation refers to filtration of water through soil and permeable rocks. The water flows to groundwater storage (aquifer).

Percolation typically exhibits universality. Statistical physics concepts such as scaling theory, renormalization, phase transition, critical phenomena and fractals are used to characterize percolation properties. Combinatorics is commonly employed to study percolation thresholds.

Due to the complexity involved in obtaining exact results from analytical models of percolation, computer simulations are typically used. The current fastest algorithm for percolation was published in 2000 by Mark Newman and Robert Ziff.[1]

Examples[edit]

  • Coffee percolation, where the solvent is water, the permeable substance is the coffee grounds, and the soluble constituents are the chemical compounds that give coffee its color, taste, and aroma
  • Movement of weathered material down on a slope under the earth's surface
  • Cracking of trees with the presence of two conditions, sunlight and under the influence of pressure
  • Robustness of networks to random and targeted attacks[citation needed]
  • Transport in porous media
  • Epidemic spreading[2][3]
  • Surface roughening[citation needed]
  • Dental Percolation, increase rate of decay under crowns because of a conducive environment for strep mutans and lactobacillus

See also[edit]

References[edit]

  1. ^ Newman, Mark; Ziff, Robert (2000). "Efficient Monte Carlo Algorithm and High-Precision Results for Percolation". Physical Review Letters. American Physical Society. 85 (19): 4104–4107. arXiv:cond-mat/0005264free to read. Bibcode:2000PhRvL..85.4104N. doi:10.1103/PhysRevLett.85.4104. PMID 11056635. Retrieved 19 November 2013. 
  2. ^ Parshani, Roni; Carmi, Shai; Havlin, Shlomo (2010). "Epidemic Threshold for the Susceptible-Infectious-Susceptible Model on Random Networks". Physical Review Letters. 104 (25). arXiv:0909.3811free to read. Bibcode:2010PhRvL.104y8701P. doi:10.1103/PhysRevLett.104.258701. ISSN 0031-9007. 
  3. ^ Grassberger, P. "On the Critical Behavior of the General Epidemic Process and Dynamic Percolation". Mathematical Biosciences. 

Further reading[edit]

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