Suppr超能文献

一种用于石英增强光声光谱(QEPAS)应用中石英音叉的高灵敏度前置放大器。

A High Sensitivity Preamplifier for Quartz Tuning Forks in QEPAS (Quartz Enhanced PhotoAcoustic Spectroscopy) Applications.

作者信息

Starecki Tomasz, Wieczorek Piotr Z

机构信息

Institute of Electronic Systems, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland.

出版信息

Sensors (Basel). 2017 Nov 3;17(11):2528. doi: 10.3390/s17112528.

Abstract

All the preamplifiers dedicated for Quartz Enhanced PhotoAcoustic Spectroscopy (QEPAS) applications that have so far been reported in the literature have been based on operational amplifiers working in transimpedance configurations. Taking into consideration that QEPAS sensors are based on quartz tuning forks, and that quartz has a relatively high voltage constant and relatively low charge constant, it seems that a transimpedance amplifier is not an optimal solution. This paper describes the design of a quartz QEPAS sensor preamplifier, implemented with voltage amplifier configuration. Discussion of an electrical model of the circuit and preliminary measurements are presented. Both theoretical analysis and experiments show that use of the voltage configuration allows for a substantial increase of the output signal in comparison to the transimpedance circuit with the same tuning fork working in identical conditions. Assuming that the sensitivity of the QEPAS technique depends directly on the properties of the preamplifier, use of the voltage amplifier configuration should result in an increase of QEPAS sensitivity by one to two orders of magnitude.

摘要

迄今为止,文献中报道的所有专用于石英增强光声光谱(QEPAS)应用的前置放大器均基于工作在跨阻配置下的运算放大器。考虑到QEPAS传感器基于石英音叉,且石英具有相对较高的电压常数和相对较低的电荷常数,跨阻放大器似乎并非最佳解决方案。本文描述了一种采用电压放大器配置实现的石英QEPAS传感器前置放大器的设计。文中给出了电路的电气模型讨论和初步测量结果。理论分析和实验均表明,与在相同条件下工作的相同音叉的跨阻电路相比,采用电压配置可使输出信号大幅增加。假设QEPAS技术的灵敏度直接取决于前置放大器的特性,那么采用电压放大器配置应会使QEPAS灵敏度提高一到两个数量级。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b97/5712872/d1e209c4e7b4/sensors-17-02528-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验