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Humidity and moisture effects, frequently called water poisoning, in surroundings are inevitable for various molecular sensing devices, strongly affecting their sensing characteristics. Here, we demonstrate a water-selective nanostructured dehumidifier composed of ZnO/TiO

Breath odor sensing-based individual authentication was conducted for the first time using an artificial olfactory sensor system. Using a 16-channel chemiresistive sensor array and machine learning, a mean accuracy of97% was successfully achieved. The impact of the number of sensors on the accuracy and reproducibility was also demonstrated.

Herein, we explore the hidden molecular recognition abilities of ZnO nanowires uniformly grown on the inner surface of an open tubular fused silica capillary

Polymer-carbon nanocomposite sensor is a promising molecular sensing device for electronic nose (e-nose) due to its printability, variety of polymer materials, and low operation temperature; however, the lack of stability in an air environment has been an inevitable issue. Here, we demonstrate a design concept for realizing long-term stability in a polyethylene glycol (PEG)-carbon black (CB) nanocomposite sensor by understanding the underlying phenomena that cause sensor degradation. Comparison of the sensing properties and infrared spectroscopy on the same device revealed that the oxidation-induced consumption of PEG is a crucial factor for the sensor degradation. According to the mechanism, we introduced an antioxidizing agent (i.e., ascorbic acid) into the PEG-CB nanocomposite sensor to suppress the PEG oxidation and successfully demonstrated the long-term stability of sensing properties under an air environment for 30 days, which had been difficult in conventional polymer-carbon nanocomposite sensors.

We present a method named NPFimg, which automatically identifies multivariate chemo-/biomarker features of analytes in chromatography-mass spectrometry (MS) data by combining image processing and machine learning. NPFimg processes a two-dimensional MS map (m/z vs retention time) to discriminate analytes and identify and visualize the marker features. Our approach allows us to comprehensively characterize the signals in MS data without the conventional peak picking process, which suffers from false peak detections. The feasibility of marker identification is successfully demonstrated in case studies of aroma odor and human breath on gas chromatography-mass spectrometry (GC-MS) even at the parts per billion level. Comparison with the widely used XCMS shows the excellent reliability of NPFimg, in that it has lower error rates of signal acquisition and marker identification. In addition, we show the potential applicability of NPFimg to the untargeted metabolomics of human breath. While this study shows the limited applications, NPFimg is potentially applicable to data processing in diverse metabolomics/chemometrics using GC-MS and liquid chromatography-MS. NPFimg is available as open source on GitHub (http://github.com/poomcj/NPFimg) under the MIT license.

Correction for ‘Breath odor-based individual authentication by an artificial olfactory sensor system and machine learning’ by Chaiyanut Jirayupat et al., Chem. Commun., 2022, DOI: https://doi.org/10.1039/D1CC06384G.