2D Molybdenum Carbide MXenes for Enhanced Selective Detection of Humidity in Air.

2D transition metal carbides and nitrides (MXenes) open up novel opportunities in gas sensing with high sensitivity at room temperature. Herein, 2D Mo2 CTx flakes with high aspect ratio are successfully synthesized. The chemiresistive effect in a sub-µm MXene multilayer for different organic vapors and humidity at 101 -104  ppm in dry air is studied. Reasonably, the low-noise resistance signal allows the detection of H2 O down to 10 ppm. Moreover, humidity suppresses the response of Mo2 CTx to organic analytes due to the blocking of adsorption active sites. By measuring the impedance of MXene layers as a function of ac frequency in the 10-2 -106  Hz range, it is shown that operation principle of the sensor is dominated by resistance change rather than capacitance variations. The sensor transfer function allows to conclude that the Mo2 CTx chemiresistance is mainly originating from electron transport through interflake potential barriers with heights up to 0.2 eV. Density functional theory calculations, elucidating the Mo2 C surface interaction with organic analytes and H2 O, explain the experimental data as an energy shift of the density of states under the analyte's adsorption which induces increasing electrical resistance.

Erratum: Kiselev, I., et al. On the Temporal Stability of Analyte Recognition with an E-Nose Based on a Metal Oxide Sensor Array in Practical Applications. Sensors 2018, 18, 550.

The authors wish to make the following corrections to this paper [...].

On the Temporal Stability of Analyte Recognition with an E-Nose Based on a Metal Oxide Sensor Array in Practical Applications.

The paper deals with a functional instability of electronic nose (e-nose) units which significantly limits their real-life applications. Here we demonstrate how to approach this issue with example of an e-nose based on a metal oxide sensor array developed at the Karlsruhe Institute of Technology (Germany). We consider the instability of e-nose operation at different time scales ranging from minutes to many years. To test the e-nose we employ open-air and headspace sampling of analyte odors. The multivariate recognition algorithm to process the multisensor array signals is based on the linear discriminant analysis method. Accounting for the received results, we argue that the stability of device operation is mostly affected by accidental changes in the ambient air composition. To overcome instabilities, we introduce the add-training procedure which is found to successfully manage both the temporal changes of ambient and the drift of multisensor array properties, even long-term. The method can be easily implemented in practical applications of e-noses and improve prospects for device marketing.