Diffusive Representation: A Powerful Method to Analyze Temporal Signals from Metal-Oxide Gas Sensors Used in Pulsed Mode
Round 1
Reviewer 1 Report
In the present paper, the reduction of sensing resistor output variables of a metal-oxide gas sensor is investigated. Three different methods are tested, i.e., polynomial and neural network modelling (static methods) and diffusive representation (dynamic method). From the comparisons it is found that diffusive representation represents the best candidate for data modelling.
In order to consider this paper acceptable for publication, the Authors must adequately correct the following relevant weaknesses.
1) There are many English language errors within the text which must be carefully corrected by the Authors.
2) The graphic quality of all Figures needs to be improved. In particular, in Figures 5a, 7a, 9 and 10a it is not possible to clearly see the difference between the colors of the two curves (also in the legend).
3) The graphic quality of the Equations must also be improved. It is necessary to rewrite them all in Word (as they are proposed they look like pasted figures) and describe in a precise manner all the parameters used within them (Equations 1-7).
4) The Conclusions must be rewritten in a more compact form (the part relating to diffusive representation presents some repetitions).
Therefore, considering the previous observations, in the opinion of the Reviewer this paper should be accepted by minor revision.
Author Response
1) There are many English language errors within the text which must be carefully corrected by the Authors.
Response 1: The paper has been revised by native English colleague
2) The graphic quality of all Figures needs to be improved. In particular, in Figures 5a, 7a, 9 and 10a it is not possible to clearly see the difference between the colors of the two curves (also in the legend).
Response 2: To improve the visibility, all the figures (4a, 4b, 5a, 5b, 7a, 7b, 9a, 9b and 10a, 10b) have been enlarged.
3) The graphic quality of the Equations must also be improved. It is necessary to rewrite them all in Word (as they are proposed they look like pasted figures) and describe in a precise manner all the parameters used within them (Equations 1-7).
Response 3: All the Equations have been rewritten in the Word equation editor
4) The Conclusions must be rewritten in a more compact form (the part relating to diffusive representation presents some repetitions).
Response 4: We have tried to eliminate repetitions in the conclusion by removing a redundant sentence.
Thank you so much for your comments
Reviewer 2 Report
This manuscript presents an efficient method of interpolation for analyzing metal oxide gas sensor response in pulsed heating mode. Metal oxide gas sensors have been widely used for a variety of gas sensing applications. The sensing mechanism is based on the transient behavior of the resistance. While the majority of studies have focused on thermodynamically steady-state conditions, little attention has been given to pulsed mode operation, where sophisticated mathematical modeling/ interpolation techniques are required for high-fidelity gas determination. The interpolation technique developed in this work is a fractional model called diffuse representation that has been applied in several other fields such as electromechanical systems and other controls applications.
The analysis is presented in a clear and concise manner. I believe this work will be of interest to a broad community working in the areas of sensor development and analysis/ interpretation of gas sensor data. I therefore recommend publication of this article in Electronics.
I just had a minor comment - the type of gas used in the experimental setup is not mentioned. What was the gas used in the experiments? How does the sensitivity of the metal oxide gas sensor change with different gases using the diffuse representation method for interpreting the sensor behavior?
Author Response
I just had a minor comment - the type of gas used in the experimental setup is not mentioned. What was the gas used in the experiments? How does the sensitivity of the metal oxide gas sensor change with different gases using the diffuse representation method for interpreting the sensor behavior?
Response 1: Effectively, we didn’t mention the gases used during the entire test. Considering that we obtained similar transient responses even with slight different shape of curves under different gases, we decided to initiate this work only with the sensor response under air as reference. Nevertheless, I’ve added an indication (line 94-95) concerning the whole test under the different gases used (air, CO, air, C3H8, air, NO2, air, and then combination of mixtures)
Response 2: It is known that the sensitivity of metal oxide gas sensors is correlated to operating temperature. For a given sensor, it is possible to modify its sensitivity to one target gas by changing the temperature. That’s why it is interesting to operate the sensor with temperature modulation. Thanks to the reference [11], it is possible to discriminate different gas by observing the shape of thermal transient response. It is also possible to quantify roughly the concentration by analysing precisely the characteristics of these transients (shape, magnitude,…). Thus, the proposed modeling approach allows to identify the key parameters to be exploited for smart i nterpretation of the sensor response.
Thank you so much for your comments
Reviewer 3 Report
Reviewer report on Manuscript Draft entitled ‘Diffusive representation: A powerful method to analyze temporal signals from metal-oxide gas sensors used in pulsed mode’
The main objective of this work was to find the most efficient method to interpolate metal oxide gas sensor used in a pulsed-temperature operating mode. The results have been compared in terms of precision and number of useful output data, as minimum as possible for high performance and rapid data treatment which is great of interest in embedded systems. The ability to significantly reduce the amount of sensing resistor output variables of a metal-oxide gas sensor has been investigated.
The research is interesting from analytical points of view. Research rather well organized and well presented, it is in scope of journal. Manuscript could be published after some improvements:
References are not up to date, last references are of 2011, therefore, some recent references from some other MDPI journals on metal oxide based sensor development (TiO2-x/TiO2-structure based ‘self-heated’ sensor for the determination of some reducing gases. Sensors 2020, 20, 74. // Selectivity of tungsten oxide synthesized by sol-gel method towards some volatile organic compounds and gaseous materials in a broad range of temperatures. Materials 2020, 13, 523. // Tuning of photo-luminescence properties of WO3-based layers by the adjustment of layer formation conditions. Materials 2020, 13, 2814. // Review Insights in the Application of Stoichiometric and Non-Stoichiometric Titanium Oxides for the Design of Sensors for the Determination of Gases and VOCs (TiO2−x and TinO2n−1 vs. TiO2). Sensors 2020, 20, 6833.) could be overviewed and discussed.
Author Response
Response : Effectively, the scope of this work is to try to develop an efficient technique (robust, reliable and fast) of data treatment. As we work on metal oxide sensors, we take the opportunity to exploit our sensor responses as best as possible. Effectively, many other interesting sensors (more recent) are developed. Our modeling approach may be applicated to these new sensors’ responses, that’s why I added few words about that in conclusion/perspectives without citing all references about all new gas sensors.
