JOURNAL ARTICLE

Complexity and Nonlinearity of Colloid Electrical Transducers.

  • Published In: International Journal of Unconventional Computing, 2025, v. 20, n. 1/2. P. 123 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: FORTULAN, RAPHAEL; KHEIRABADI, NOUSHIN RAEISI; CHIOLERIO, ALESSANDRO; ADAMATZKY, ANDREW 3 of 3

Abstract

This work explores the complexity and nonlinearity of seven different colloidal suspensions--Au, ferrofluid, TiO2, ZnO, g-C3N4, MXene, and PEDOT:PSS--when electrically stimulated with fractal, chaotic, and random binary signals. The recorded electrical responses were analyzed using entropy, file compression, fractal dimension, and Fisher information measures to quantify complexity. The nonlinearity introduced by each colloid was evaluated by the deviation of the output from the best-fit hyperplane of the input-output mapping. The results showed that TiO2 was the most complex colloid across all inputs, exhibiting high entropy, poor compressibility, and an unpredictable response pattern. The colloids also exhibited significant nonlinearity, making them promising candidates for reservoir computation, where the mapping of inputs into high-dimensional nonlinear states is advantageous. This study provides insight into the dynamics of colloids and their potential for unconventional computational applications that exploit their inherent complexity and nonlinearity, and it provides a rapid method for assessing the suitability of a particular material for use as a computational substrate before others. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Unconventional Computing. 2025/01, Vol. 20, Issue 1/2, p123
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:15487199
  • DOI:10.32908/ijuc.v20.11
  • Accession Number:187273240
  • Copyright Statement:Copyright of International Journal of Unconventional Computing is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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