Conducting Hydrogel‐Based Neural Biointerfacing Technologies
Advanced Functional Materials(2025)
Abstract
AbstractNeural biointerfacing, enabling direct communication between neural systems and external devices, holds great promises for applications in brain machine interfaces, neural prosthetics, and neuromodulation. However, current neural electronics made of conventional rigid materials are challenged by their inherent mechanical mismatch with the neural tissues. Hydrogel bioelectronics, with mechanical properties compatible with the neural tissues, represent an alternative to these limitations and enable the next‐generation neural biointerfacing technology. Here, an overview of cutting‐edge research on conducting hydrogels (CHs) bioelectronics for neural biointerfacing development, emphasizing material design principles, manufacturing techniques, essential requirements, and their corresponding application scenarios is presented. Future challenges and potential directions regarding CHs‐based neural biointerfacing technologies, including long‐term reliability, multimodal hydrogel bioelectronics for closed‐loop system and wireless power supply system, are raised. It is believed that this review will serve as a valuable resource for further advancement and implementation of next‐generation neural biointerfacing technology.
MoreTranslated text
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined