Complexity and nonlinearity of colloid electrical transducers

TL;DR

This study analyzes the complexity and nonlinearity of various colloidal suspensions under electrical stimulation, revealing their potential for reservoir computing and providing a rapid assessment method for their computational suitability.

Contribution

It introduces a comprehensive analysis of colloid responses using entropy, fractal, and Fisher measures, highlighting TiO2's high complexity and nonlinearity for computational applications.

Findings

TiO2 exhibited the highest complexity and nonlinearity.

Colloids show promise for reservoir computing due to their nonlinear responses.

The study offers a rapid method to evaluate colloids for computational use.

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.