Predicting Blood Glucose Levels with Organic Neuromorphic Micro‐Networks

Advanced Science, Journal Year: 2024, Volume and Issue: 11(27)

Published: April 29, 2024

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results reliable predictions, offering timely warnings fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality life diabetic patients. transforming this field, potentially leading to implantable smart devices fully pancreas. However, transition presents several challenges, including need specialized, compact, lightweight, low-power hardware. Organic polymer-based electronics solution as they have ability implement behavior networks, operate at low voltage, possess key attributes like flexibility, stretchability, biocompatibility. Here, study focuses on implementing systems. How minimize network requirements, downscale architecture, integrate with electrochemical neuromorphic organic devices, meeting strict demands implants in-body computation investigated.

Language: Английский

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All‐Polymer Organic Electrochemical Synaptic Transistor With Controlled Ionic Dynamics for High‐Performance Wearable and Sustainable Reservoir Computing

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 18, 2024

Abstract Wearable near/in‐sensor neuromorphic computing is driving next‐generation human‐artificial intelligence (AI) interface, the Internet of Things, and intelligent robots, with reservoir (RC) playing a pivotal role in advancing AI hardware, yet its potential remains underexplored. Herein, an all‐polymer accumulation‐mode organic electrochemical synaptic transistor (OEST) demonstrated controlled ionic dynamics that can facilitate high‐performance wearable RC while allowing entire recyclability. A microporous glycolated conjugated polymer channel (P3gCPDT‐1gT2) affords current output above mA level at <1 V enables both volatile non‐volatile modes combination soft poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/sorbitol electrodes electrolytes (gelatin/glycerol). Particularly, modulation OESTs as nonlinear dynamic reservoirs are elucidated by tuning applied voltages gel compositions. Moreover, such device exhibits performance preservation over >3000 bending cycles allows convenient recyclability using eco‐friendly solvents. sustainable system be thus established configuring units for data processing nonvolatile weight storage single‐layer perceptron readout. Such simple platform achieves up to 90% accuracy voice recognition tasks under bending. Thus, this work facilitates widespread integration multifunctional electronic hardware implementing information low‐cost, body‐conformable, eco‐benign features.

Language: Английский

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AI-powered breakthroughs in material science and biomedical polymers

Journal of Bioactive and Compatible Polymers, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 23, 2024

This commentary examines how Artificial Intelligence (AI) and Machine Learning (ML) are transforming biomedical polymers, drug delivery systems, wearable electronics, smart materials, advanced manufacturing, neuromorphic technologies. AI enhances prediction accuracy, optimizes material properties, accelerates development, enables innovative applications such as biomaterials, personalized medicine, tissue engineering. Specific include predicting polymer optimizing release kinetics, improving system design, creating responsive materials for devices. also advances sensors, flexible 3D/4D printing, sustainable computing, leading to breakthroughs in health monitoring, human-computer interaction, environmental sustainability.

Language: Английский

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