PEDOT:PSS/QCM-based multimodal humidity and pressure sensor

Muckley, Eric S.; Lynch, J. M.; Kumar, Rajeev; Sumpter, Bobby G.; Ivanov, Ilia N.

doi:10.1016/j.snb.2016.05.054

Cited by 65 publications

(54 citation statements)

References 35 publications

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“…Diffusion of H 2 O into PEDOT:PSS micron line causes micron line swelling due to protonation of SO 3 H + groups at the PEDOT-PSS interface, resulting in the formation of H 3 O + PSS(SO 3 ) À (eqn (1)). 30 Along with the absorption of water in PSS chains (humidity increases from 11% to 69%), the distance between adjacent PEDOT chains increases. As a result, the hopping conduction of charges among PEDOT chains becomes more difficult to achieve with the increased humidity.…”

Section: Resultsmentioning

confidence: 99%

Fast-response humidity sensor based on laser printing for respiration monitoring

et al. 2020

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Section: Resultsmentioning

confidence: 99%

Fast-response humidity sensor based on laser printing for respiration monitoring

et al. 2020

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“…These networks are also often used in devices with semiconducting metal oxide-based sensors for detection and concentration estimation of various gases. In many cases, sufficiently good results are obtained with MLP networks with one hidden layer [ 19 , 20 , 21 ]. In more complex cases generally are sufficient MLP networks with two hidden layers [ 22 , 23 ].…”

Section: Correction Based On An Artificial Neural Network (Methods mentioning

confidence: 99%

Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique

Roj

2016

Sensors

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The paper presents two methods of dynamic error correction applied to transducers used for the measurement of gas concentration. One of them is based on a parametric model of the transducer dynamics, and the second one uses the artificial neural network (ANN) technique. This article describes research of the dynamic properties of the gas concentration measuring transducer with a typical sensor based on tin dioxide. Its response time is about 8 min, which may be not acceptable in many applications. On the basis of these studies, a parametric model of the transducer dynamics and an adequate correction algorithm has been developed. The results obtained in the research of the transducer were also used for learning and testing ANN, which were implemented in the dynamic correction task. Despite the simplicity of the used models, both methods allowed a significant reduction of the transducer’s response time. For the algorithm based on the parametric model the response time was shorter by approximately eight-fold (reduced up to 40–80 s, i.e., about 2–4 sample periods), whereas with the use of an ANN the output signal was practically fixed after a time equal to one sampling period, i.e., 20 s. In addition, the use of ANN has allowed reducing the impact of the transducer dynamic non-linearity on the correction effectiveness.

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“…Developed by Stanford Research Systems ( SRS ), the commercial QCM200 characterization system has been widely used for gas and liquid-phase QCM applications in different fields [ 101 , 102 , 103 , 104 ] due to its adopted Automatic Gain Control ( AGC ) based oscillator design [ 9 ]. The system mainly consists of an interfacing panel that is connected to an oscillator module.…”

Section: Qcm Electronic Interfacing Systems For Sensing Applicatiomentioning

confidence: 99%

Quartz Crystal Microbalance Electronic Interfacing Systems: A Review

Alassi

Benammar

Brett

2017

Sensors

159

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Quartz Crystal Microbalance (QCM) sensors are actively being implemented in various fields due to their compatibility with different operating conditions in gaseous/liquid mediums for a wide range of measurements. This trend has been matched by the parallel advancement in tailored electronic interfacing systems for QCM sensors. That is, selecting the appropriate electronic circuit is vital for accurate sensor measurements. Many techniques were developed over time to cover the expanding measurement requirements (e.g., accommodating highly-damping environments). This paper presents a comprehensive review of the various existing QCM electronic interfacing systems. Namely, impedance-based analysis, oscillators (conventional and lock-in based techniques), exponential decay methods and the emerging phase-mass based characterization. The aforementioned methods are discussed in detail and qualitatively compared in terms of their performance for various applications. In addition, some theoretical improvements and recommendations are introduced for adequate systems implementation. Finally, specific design considerations of high-temperature microbalance systems (e.g., GaPO 4 crystals (GCM) and Langasite crystals (LCM)) are introduced, while assessing their overall system performance, stability and quality compared to conventional low-temperature applications.

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PEDOT:PSS/QCM-based multimodal humidity and pressure sensor

Cited by 65 publications

References 35 publications

Fast-response humidity sensor based on laser printing for respiration monitoring

Fast-response humidity sensor based on laser printing for respiration monitoring

Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique

Quartz Crystal Microbalance Electronic Interfacing Systems: A Review

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