Influence of Analyte Flow Rate on Signal and Response Time of the Amperometric Gas Sensor with Nafion Membrane

Gębicki, Jacek; Chachulski, Bogdan

doi:10.1002/elan.200804567

Cited by 16 publications

(7 citation statements)

References 29 publications

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“…Further, the flow correction model can reduce the detection error of the system and subsequently improve the detection accuracy. This is consistent with the findings of other researchers [ 33 , 34 , 35 , 36 ], who believe that the gas sensor response is related to the gas flow rate. However, these studies did not integrate machine learning algorithms to correct the pump flow rate or the sensor response.…”

Section: Resultssupporting

confidence: 93%

“…The significance of the effect of the gas flow rate on the sensor response was confirmed, and the relationship between the sensor response and the gas flow rate was explored. Gębicki et al [ 36 ] analyzed the impact of the volumetric flow rate on the signal and response time of a Nafion membrane sulfur dioxide gas sensor. The sensor signals and response times were analyzed for flow rates in the range of 0–100 cm 3 /min.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Sun

Shi

2023

Sensors

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Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm–back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

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Section: Resultssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Sun

Shi

2023

Sensors

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“…While beneficial for wide adoption of our techniques, the sensitivity of the sensors required relatively high ethanol concentrations. Additionally, our measurement of current amplitude could be influenced by a number of factors, such as humidity 51 , 52 , pressure 53 , temperature 3 , 4 , and mean flow rate 54 , 55 . Therefore, the order of measurements were reversed on alternating days to ensure the trends were independent of humidity, pressure, and temperature.…”

Section: Discussionmentioning

confidence: 99%

Sniffing speeds up chemical detection by controlling air-flows near sensors

et al. 2021

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Most mammals sniff to detect odors, but little is known how the periodic inhale and exhale that make up a sniff helps to improve odor detection. In this combined experimental and theoretical study, we use fluid mechanics and machine olfaction to rationalize the benefits of sniffing at different rates. We design and build a bellows and sensor system to detect the change in current as a function of odor concentration. A fast sniff enables quick odor recognition, but too fast a sniff makes the amplitude of the signal comparable to noise. A slow sniff increases signal amplitude but delays its transmission. This trade-off may inspire the design of future devices that can actively modulate their sniffing frequency according to different odors.

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“…Previous workers have also noted that amperometric sensors may be described by an effective diffusion time L 2 /D. [17] First, we consider the current a long time after the concentration step when it has reached a steady-state value. It is straightforward to see that the steady-state current as t t !…”

Section: Approximate Solutions For a Two-layer Modelmentioning

confidence: 99%

Diffusion Models of Mass Transport for the Characterisation of Amperometric Gas Sensors

Saunders,

Mudashiru,

Baron

et al. 2024

ChemElectroChem

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A diffusion model for the analysis of chronoamperometric data in response to a concentration step is developed for amperometric gas sensors. This analysis avoids the difficulties with standard potentiodynamic measurements at the large specific area, high capacitance electrodes employed in these sensors. Despite the fact that typical devices comprise multiple layers with varying thicknesses and diffusivities, we show that typical chronoamperometric traces can be fitted to a simple diffusion model with a single parameter where L is an overall effective thickness of the diffusion barrier and D is an effective diffusion coefficient. Through a comparison of the transient and steady‐state current, independent estimates of L and D in the devices can be made. The model is also extended to cover cases with interfacial kinetic barriers; such kinetic limitations lead to a change in the effective values L and D, but the simple diffusion model remains a good fit to the data. This analysis shows that transient sensor responses can be characterised by a single parameter τ and conversely that deviations from this regression model cannot be assigned to (i) complex layer architectures or (ii) interlayer kinetic barriers. Instead, we show that non‐uniform accessibility effects arising from a distribution of diffusion rates across the device lead to deviations from the simple regression model, but that they may be captured approximately by a more complex model in which τ has a probability distribution.

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Influence of Analyte Flow Rate on Signal and Response Time of the Amperometric Gas Sensor with Nafion Membrane

Cited by 16 publications

References 29 publications

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Sniffing speeds up chemical detection by controlling air-flows near sensors

Diffusion Models of Mass Transport for the Characterisation of Amperometric Gas Sensors

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