Here, we demonstrate a novel device structure design to enhance the electrical conversion output of a triboelectric device through the piezoelectric effect called as the piezo-induced triboelectric (PIT) device. By utilizing the piezopotential of ZnO nanowires embedded into the polydimethylsiloxane (PDMS) layer attached on the top electrode of the conventional triboelectric device (Au/PDMS-Al), the PIT device exhibits an output power density of 50 μW/cm, which is larger than that of the conventional triboelectric device by up to 100 folds under the external applied force of 8.5 N. We found that the effect of the external piezopotential on the top Au electrode of the triboelectric device not only enhances the electron transfer from the Al electrode to PDMS but also boosts the internal built-in potential of the triboelectric device through an external electric field of the piezoelectric layer. Furthermore, 100 light-emitting diodes (LEDs) could be lighted up via the PIT device, whereas the conventional device could illuminate less than 20 LED bulbs. Thus, our results highlight that the enhancement of the triboelectric output can be achieved by using a PIT device structure, which enables us to develop hybrid nanogenerators for various self-power electronics such as wearable and mobile devices.

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.

The potential feasibility of breath odor sensing-based individual authentication was demonstrated by a 16-channel chemiresistive sensor array and machine learning.