High-sensitivity and Energy-Efficient Chloride Ion Sensors Based on Flexible Printed Carbon Nanotube Thin-Film Transistors for Wearable Electronics
TALANTA(2024)
School of Emergency Management
Abstract
The advent of flexible single-walled carbon nanotube thin-film transistors (SWCNT-TFTs) has transformed electronics, providing significant benefits like low operating voltage, reduced power consumption, cost-effectiveness, and improved signal amplification. This study focuses on leveraging these attributes to develop a novel flexible high-sensitivity and energy-efficient chloride ion sensors based on printed flexible SWCNT-TFTs utilizing polymers-sorted semiconducting SWCNTs (sc-SWCNTs) as the active layers and ion liquids-poly(4-vinylphenol as dielectric layers along with the evaporated deposition of aluminum electrodes and printed silver electrodes as the gate and source-drain electrodes, respectively. The sensors exhibit several operational advantages, including low voltage requirements (≤1 V), rapid response speed (5.32 s), significant signal amplification (Up to 702.6%), low power consumption (0.31 μJ at 1 mmol chloride ion), good repeatability, high sensitivity for both low and high concentrations of chloride ion (up to 100 mmol/L) and excellent mechanical flexibility (No obvious changes after bending for 10,000 times with a 5 mm radius). The detection mechanism of chloride ions was analyzed using X-ray Photoelectron Spectroscopy (XPS). It was found that chloride ions react with silver nanoparticles (AgNPs) to form silver chloride (AgCl) on printed electrodes, impeding carrier transport and reducing the currents in SWCNT TFTs. Importantly, our sensors' compatibility with smart devices allows for real-time monitoring of chloride ion levels in human sweat, offering significant potential for daily health monitoring.
MoreTranslated text
Key words
Single-walled carbon nanotube thin-film tran-sistors (SWCNT-TFTs),Chloride ion sensors,Flexible electronics,Printed electronics,Health monitoring
求助PDF
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