A Dual‐Ion Multiphysics Model for Smart and Sustainable Sensors Based on Bacterial Cellulose
Year: 2025
Type: article
Abstract: Bacterial cellulose (BC) is an emerging smart material, synthesized through microbial fermentation of environmentally friendly substrates, including organic waste. When functionalized with ionic liquids (ILs) and coated with conductive polymers, BC forms soft, sustainable, and electroactive composites, making it suitable for sensors in soft robotics, wearable, biomedical, and environmental monitoring applications. However, modeling frameworks for BC–IL sensors are still lacking, hindering their integration into real‐world applications. To bridge this gap and support smart material design, we propose a novel first‐principle white‐box modeling framework is proposed that couples a 2D finite element method (FEM) for mechanical deformation with 1D FEM sub‐models for ion transport and voltage generation. Specifically, this work introduces the first dual‐carrier multiphysics model for mechanoelectric transduction in BC–IL sensors. The model, experimentally calibrated and validated, resolves the spatio‐temporal dynamics of mechanical deformation and dual‐ion transport, including diffusion, electromigration, and advection. By explicitly incorporating the transport and interaction of both cations and anions, previously neglected in smart‐sensors modeling, the proposed strategy provides a foundational simulation framework for the scalable, rapid, and intelligent design of next‐generation biodegradable and multifunctional smart sensors, advancing the integration of green materials into intelligent systems.
Source: Advanced Intelligent Systems
Authors: Francesca Sapuppo, Giovanna Di Pasquale, Salvatore Graziani, Sara Sadat Hosseini, Luca Patané, Antonino Pollicino, Carlo Trigona, Maria Gabriella Xibilia
Institutions: University of Messina, University of Catania
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,970
Funder: European Commission
OpenAlex ID: W4414986852
