Solution-processed flexible TiS₂/organic superlattice films may find applications in powering wearable electronics.

Various volatile organic compounds (VOCs) in breath exhaled by patients with lung cancer, healthy controls, and patients with lung cancer who underwent surgery for resection of cancer were analyzed by gas condenser-equipped gas chromatography-mass spectrometry (GC/MS) for development of an exhaled breath monitoring prototype system involving metal oxide gas sensors, a gas condenser, and gas chromatography columns. The gas condenser-GC/MS analysis identified concentrations of 56 VOCs in the breath exhaled by the test population of 136 volunteers (107 patients with lung cancer and 29 controls), and selected four target VOCs, nonanal, acetoin, acetic acid, and propanoic acid, for use with the condenser, GC, and sensor-type prototype system. The prototype system analyzed exhaled breath samples from 101 volunteers (74 patients with lung cancer and 27 controls). The prototype system exhibited a level of performance similar to that of the gas condenser-GC/MS system for breath analysis.

Monodispersed nanoparticles consisting of barium titanate (BaTiO3, BT) as the core and polyvinylpyrrolidone (PVP) as the shell were synthesized in a PVP-assisted low-temperature process in an aqueous solution at ambient pressure. In order to clarify the mechanism of this unique BT–PVP nanoparticle growth and the origin of the dispersion, the concentration and molecular weight of PVP used in the synthesis were varied, and the size and dispersion of the resulting nanoparticles in water were investigated by field-emission scanning electron microscopy and dynamic light scattering. Monodispersed nanoparticles with an average size of 130 nm were obtained by using an intermediate PVP concentration of 100 g/L and a PVP molecular weight of 10,000 g/mol. The thickness of the PVP shell was estimated by thermogravimetric analysis. For the highly dispersed BT–PVP, the thickness of PVP adsorbed on the BT surface was around 3–5 nm. Direct SEM observation of monodispersed BT–PVP in an aqueous solution using a unique sample holder was also demonstrated for the first time

We investigated selective detection of the target volatile organic compounds (VOCs) nonanal, n-decane, and acetoin for lung cancer-related VOCs, and acetone and methyl i-butyl ketone for diabetes-related VOCs, in humid air with simulated VOC contamination (total concentration: 300 μg/m³). We used six “grain boundary-response type” sensors, including four commercially available sensors (TGS 2600, 2610, 2610, and 2620) and two Pt, Pd, and Au-loaded SnO₂ sensors (Pt, Pd, Au/SnO₂), and two “bulk-response type” sensors, including Zr-doped CeO₂ (CeZr10), i.e., eight sensors in total. We then analyzed their sensor signals using principal component analysis (PCA). Although the six “grain boundary-response type” sensors were found to be insufficient for selective detection of the target gases in humid air, the addition of two “bulk-response type” sensors improved the selectivity, even with simulated VOC contamination. To further improve the discrimination, we selected appropriate sensors from the eight sensors based on the PCA results. The selectivity to each target gas was maintained and was not affected by contamination.

We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set.