WeChat Mini Program
Old Version Features

A Self‐Powered Portable Nanowire Array Gas Sensor for Dynamic NO2 Monitoring at Room Temperature

ADVANCED MATERIALS(2023)

Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems

Cited 35|Views44
Abstract
The fast development of the Internet of Things (IoT) has driven an increasing consumer demand for self-powered gas sensors for real-time data collection and autonomous responses in industries such as environmental monitoring, workplace safety, smart cities, and personal healthcare. Despite intensive research and rapid progress in the field, most reported self-powered devices, specifically NO2 sensors for air pollution monitoring, have limited sensitivity, selectivity, and scalability. Here, a novel photovoltaic self-powered NO2 sensor is demonstrated based on axial p-i-n homojunction InP nanowire (NW) arrays, that overcome these limitations. The optimized innovative InP NW array device is designed by numerical simulation for insights into sensing mechanisms and performance enhancement. Without a power source, this InP NW sensor achieves an 84% sensing response to 1 ppm NO2 and records a limit of detection down to the sub-ppb level, with little dependence on the incident light intensity, even under <5% of 1 sun illumination. Based on this great environmental fidelity, the sensor is integrated into a commercial microchip interface to evaluate its performance in the context of dynamic environmental monitoring of motor vehicle exhaust. The results show that compound semiconductor nanowires can form promising self-powered sensing platforms suitable for future mega-scale IoT systems.
More
Translated text
Key words
gas sensors,InP nanowires,p-n homojunction,self-powered devices
求助PDF
上传PDF
Bibtex
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