Experimental and Theoretical Analysis of Molecular Sensors for Ultra-Sensitive Aldehyde (vocs) Detection Mimicking Human Breath
SSRN Electronic Journal(2022)
Abstract
The detection of low concentration formaldehyde as a potential breath marker for lung cancer is a technical challenge in the current gas sensor field due to its weak electrical signal and its susceptibility to humidity. Here, we report on a combination of density functional theory (DFT) and experimental data to establish that aldehydes in super-saturated water vapor (80% humidity) can be detected using nanotubes molecular field-effect transistors. A thin film of high-purity semiconducting single-wall carbon nanotubes (SWCNTs) network and a catalytic metal were used as the sensing material. The fabricated back-gated Pt-decorated SWCNT FET sensors showed a response of 26.99% to 300 ppm formaldehyde at room temperature and humidity of 80% which is 11.58 times greater than that obtained for its detection using back gated bare SWCNT FET. DFT calculation also confirmed that the sensitivity of Pt-decorated SWCNT toward methanediol (formaldehyde in humidity) detection is 7.29 greater than that obtained for methanediol detection using pristine SWCNT. Moreover, the fabricated back-gated Pt-decorated SWCNT FET sensors exhibited a good recovery of 115 sec towards 300 ppm formaldehyde at 70 °C with sub-ppb theoretical calculation limit-of-detection (LOD). Hence, back-gated Pt-decorated SWCNT FET sensor is a promising candidate for easy screening of lung cancer patients.
MoreTranslated text
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
Related Papers
1995
被引用146 | 浏览
1995
被引用1746 | 浏览
2011
被引用84 | 浏览
2000
被引用301 | 浏览
2006
被引用113 | 浏览
1994
被引用51 | 浏览
2009
被引用100 | 浏览
2007
被引用238 | 浏览
2003
被引用135 | 浏览
2004
被引用61 | 浏览
2009
被引用75 | 浏览
2011
被引用144 | 浏览
2011
被引用29 | 浏览
2000
被引用233 | 浏览
2012
被引用24 | 浏览
2003
被引用160 | 浏览
2015
被引用240 | 浏览
2020
被引用329 | 浏览
2021
被引用33 | 浏览
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