WeChat Mini Program
Old Version Features

An Artificial Olfactory System Based on Synaptic Transistors for Precepting Hazardous Gas to Simulate Organ Injury.

Junshuai Dai, Li Yuan, Yunkuan Wei, Jixing Zhou, Longwei Xue, Xudong Zhang,Jianhua Zhang, Longlong Chen,Xingwei Ding,Hai Liu,Tingting Zhao

ACS sensors(2025)

Cited 0|Views2
Abstract
Recent advances in artificial olfactory systems have attracted significant attention for their potential applications in humanoid robots and intelligent nasal devices capable of identifying objects and sensing hazards; however, the memory function is absent in traditional gas sensors, which is crucial to assess the long-term exposure risks. Meanwhile, due to the high operation temperature requirement, the gas sensors are usually difficult to integrate with the synaptic devices to form artificial olfactory systems. Here, we propose a novel artificial olfactory synaptic device to obtain and memorize formaldehyde information. The device is composed of an ion gel synaptic transistor integrated with a Ag-ZnO gas sensor, which can simulate the adverse effects of formaldehyde exposure to the human body and make an early warning. The Ag-ZnO gas sensor can detect and recognize different concentrations of formaldehyde as the chemiresistive signal at room temperature with ultraviolet irradiation instead of at high temperatures. The formaldehyde-induced resistive changes are transmitted to the gate voltage of the synaptic transistor, modulating the channel conductance to generate varying postsynaptic currents and to store gas information to realize the memory function. In addition, the postsynaptic current data of different concentrations can be imported into a support vector machine (SVM) for accurate identification, and early warning of different concentrations can be realized through the system. This bionic olfactory system provides a promising strategy for the development of advanced artificial intelligence and danger warnings.
More
Translated text
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