Data_Sheet_1_Improved Gas Selectivity Based on Carbon Modified SnO2 Nanowires.PDF
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
The analysis of ambient (home, office, outdoor) atmosphere in order to check the presence of dangerous gases is getting more and more important. Therefore, tiny sensors capable to distinguish the presence of specific pollutants is crucial. Herein, a resistive sensor based on a carbon modified tin oxide nanowires, able to classify different gases and estimate their concentration, is presented. The C-SnO2 nanostructures are grown by chemical vapor deposition and then used as a conductometric sensor under a temperature gradient. The device works at lower temperatures than pure SnO2, with a better response. Five outputs are collected and combined to form multidimensional data that are specific of each gas. Machine learning algorithms are applied to these multidimensional data in order to teach the system how to recognize different gases. The six tested gases (acetone, ammonia, CO, ethanol, hydrogen, and toluene) are perfectly classified by three models, demonstrating the goodness of the raw sensor response. The gas concentration can also be estimated, with an average error of 36% on the low concentration range 1–50 ppm, making the sensor suitable for detecting the exceedance of the danger thresholds.
Read the peer-reviewed publication