Data from: Identifying irregular gasoline and ethanol blends in Brazil using a Love wave sensor
Data files
Sep 14, 2026 version files 639.36 KB
-
README.md
1.24 KB
-
S2P_file_from_the_sensor_with_ethanol_on_the_surface.s2p
70.77 KB
-
S2P_file_from_the_sensor_with_ethanol..s2p
70.83 KB
-
S2P_file_from_the_sensor_with_gasoline_on_the_surface.s2p
71.03 KB
-
S2P_file_from_the_sensor_with_gasoline.s2p
70.78 KB
-
S2P_file_of_the_sensor_with_a_free_surface..s2p
71.06 KB
-
S2P_file_of_the_sensor_with_a_free_surface.s2p
71.04 KB
-
S2P_sensor_file_with_gasoline_and_30__ethanol.s2p
70.79 KB
-
S2P_sensor_file_with_gasoline_and_40__ethanol.s2p
70.76 KB
-
S2P_sensor_file_with_gasoline_and_5__ethanol.s2p
71.07 KB
Abstract
In Brazil, gasoline is the second most widely used fuel for motor vehicles, surpassed only by diesel. National legislation defines Type A gasoline as that which is free of oxygenated additives, while Type C gasoline, the most used, is obtained by blending Type A gasoline with anhydrous ethanol in proportions established by current regulations. However, for various reasons including commercial misconduct these proportions are often not respected, leading to adulterated mixtures that cause financial losses for both the government and consumers. The results presented in this study demonstrate the feasibility of employing Love-wave acoustic sensors for the identification of irregular mixtures in commercial fuels in Brazil. This capability arises from the sensitivity of the sensor to variations in the mechanical and dielectric properties of the analyzed liquid samples. The electrical parameters of the sensor, particularly insertion loss (S₂₁) and reflection loss (S₁₁), were affected by the presence of different liquid mixtures on their sensitive surfaces, allowing their detection and distinction. Therefore, this work highlights the potential of Love-wave sensors as an efficient tool for the preliminary analysis and monitoring of irregular fuel mixtures, contributing to the development of fast, reliable, and high-performance detection techniques for fuel quality control.
Dataset DOI: 10.5061/dryad.70rxwdcc5
Description of the data and file structure
Instructions for using the data
The files are in s2p format, extracted directly from the Vector Network Analyzer. The file descriptions indicate the liquid that was placed on the sensor's sensitive surface. Pre-processing is necessary to remove unwanted noise. The files should be plotted using any software capable of plotting data.
First analysis:
File: S2P_file_from_the_sensor_with_ethanol_on_the_surface.s2p
File: S2P_file_from_the_sensor_with_gasoline_on_the_surface.s2p
File: S2P_file_of_the_sensor_with_a_free_surface.s2p
File: S2P_sensor_file_with_gasoline_and_30__ethanol.s2p
File: S2P_sensor_file_with_gasoline_and_40__ethanol.s2p
Second analysis:
File: S2P_file_from_the_sensor_with_ethanol..s2p
File: S2P_file_from_the_sensor_with_gasoline.s2p
File: S2P_file_of_the_sensor_with_a_free_surface..s2p
File: S2P_sensor_file_with_gasoline_and_5__ethanol.s2p
Code/software
Can use Python or any data plotting software such as Matlab.
