Improved Gas Selectivity Based on Carbon Modified SnO2 Nanowires

Abstract

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-SnO 2 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 SnO 2 , 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.

Review: Influences of Semiconductor Metal Oxide Properties on Gas Sensing Characteristics

“…For instance, Tonezzer et al (2019) used the gas sensor based on carbon-modified NWs grown by chemical vapor deposition to improve gas selectivity. The created device was able to distinguish all tested gases (acetone, ammonia, carbon monoxide, ethanol, hydrogen, and toluene) at low concentration.…”

Nanosensor Based on Thermal Gradient and Machine Learning for the Detection of Methanol Adulteration in Alcoholic Beverages and Methanol Poisoning

“…Since a resistive sensor provides a one-dimensional response (a single pure number, a ratio between two electrical values), it is inherently non-selective. For this reason, the sensor responses at five different working temperatures (180, 210, 240, 270, and 300 °C) were combined to create 5-dimensional points to be processed with multivariate statistical analysis techniques [ 26 ]. The 5D points obtained were analyzed with different techniques in order to evaluate different aspects of the sensor performance.…”

“…The material response changes with both the temperature and volatile compound concentration. This produces a “thermal/chemical fingerprint” which can be the basis of an electronic nose [ 26 ].…”

Single Nanowire Gas Sensor Able to Distinguish Fish and Meat and Evaluate Their Degree of Freshness

“…Since the single response of the resistive sensor is inherently non-selective, the responses at the five working temperatures were combined to create 5-dimensional points that could be processed and analyzed with multivariate statistical analysis techniques [21]. The 5D points were used both for visualization via principal component analysis (PCA) and for classification and quantification with a support vector machine (SVM).…”