AIMC Topic: Electric Conductivity
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A novel transfer learning framework for non-uniform conductivity estimation with limited data in personalized brain stimulation.
Physics in medicine and biology
May 6, 2025
. Personalized transcranial magnetic stimulation (TMS) requires individualized head models that incorporate non-uniform conductivity to enable target-specific stimulation. Accurately estimating non-uniform conductivity in individualized head models r...
A liquid metal-based sticky conductor for wearable and real-time electromyogram monitoring with machine learning classification.
Journal of materials chemistry. B
Mar 20, 2025
Skin electronics face challenges related to the interface between rigid and soft materials, resulting in discomfort and signal inaccuracies. This study presents the development and characterization of a liquid metal-polydimethylsiloxane (LM-PDMS) sti...
Bioinspired Super-Robust Conductive Hydrogels for Machine Learning-Assisted Tactile Perception System.
Advanced materials (Deerfield Beach, Fla.)
Feb 3, 2025
Conductive hydrogels have attracted significant attention due to exceptional flexibility, electrochemical property, and biocompatibility. However, the low mechanical strength can compromise their stability under high stress, making the material susce...
Machine learning-based prediction of compost maturity and identification of key parameters during manure composting.
Bioresource technology
Dec 26, 2024
Evaluating compost maturity, e.g. via manual seed germination index (GI) measurement, is both time-consuming and costly during composting. This study employed six machine learning methods, including random forest (RF), extra tree (ET), eXtreme gradie...
Sustainable Silk Fibroin Ionic Touch Screens for Flexible Biodegradable Electronics with Integrated AI and IoT Functionality.
Advanced materials (Deerfield Beach, Fla.)
Dec 8, 2024
The increasing prevalence of electronic devices has led to a significant rise in electronic waste (e-waste), necessitating the development of sustainable materials for flexible electronics. In this study, silk fibroin ionic touch screen (SFITS) is in...
Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model.
Water research
Jul 14, 2024
Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or b...
A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification.
Journal of materials chemistry. B
Jun 27, 2024
Physical eutectogels as a newly emerging type of conductive gel have gained extensive interest for the next generation multifunctional electronic devices. Nevertheless, some obstacles, including weak mechanical performance, low self-adhesive strength...
3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles.
Biomaterials
May 24, 2024
The proliferation of medical wearables necessitates the development of novel electrodes for cutaneous electrophysiology. In this work, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is combined with a deep eutectic solvent (DES) a...
Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing.
Advanced materials (Deerfield Beach, Fla.)
Apr 30, 2024
Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and h...
Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning.
Journal of hazardous materials
Apr 23, 2024
Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated exce...
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