A thin film sensor based on tetragonal SnO2 nanoparticles was fabricated by combining the sol–gel method and a dip-coating technique on a cylindrical glass substrate. The sensing material was produced through a cycling annealing process at 400 and 600 °C, using tin chloride (IV) pentahydrate as a precursor in polyethylene glycol (PEG) solution as a surfactant. Materials were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD), revealing tetragonal phase formation with no impurities. The sensor′s assembly was done with low-cost materials such as Cu electrodes, Cu-Ni tube pins, and glass-reinforced epoxy laminate as the base material. For signal variation, an adequate voltage divider circuit was used to detect ethanol′s presence on the surface of the sensor. The fabricated sensor response to gaseous ethanol at its operating temperature at ambient pressure is comparable to that of a commercial sensor, with the advantage of detecting ethanol at lower temperatures. The sensor response (S = Ra/Rg) to 40 ppm of ethanol at 120 °C was 7.21. A reported mathematical model was used to fit the data with good results.

Composite materials of polyethylene terephthalate with silanized halloysite nanoclay were prepared and characterized. Halloysite was first functionalized with benzoyloxypropyltrimethoxysilane and then incorporated it into the polymer matrix via melt extrusion at 0.5, 1, and 2 wt% clay load ratios. The modified clay was characterized by means of elemental carbon quantification, thermogravimetric analysis, X-ray diffraction, and nitrogen adsorption–desorption. The silanization was confirmed to have taken place with an approximate reaction yield of 5%. While the silanization did not significantly affect the crystal structure or the morphological properties of the clay, a mass loss starting from 190 °C attributed to the organosilane compound used to modify the clay was observed in the reacted samples, along with increased thermal stability. The composite materials exhibited an increase in Young’s modulus and a decrease in the ultimate strain, but not a significant change in the oxygen permeability of the composites with respect to the neat PET. Graphical abstract

Catalyst informatics and catalyst design have the potential to facilitate and speed up catalyst discovery, as this is a complex undertaking involving variables associated with the catalysts themselves and operating conditions. Herein, a Machine Learning (ML)-assisted methodology coupled with data visualization to design descriptors for catalyst materials are proposed using a previously reported literature data set of the Water−Gas Shift (WGS) reaction. This entails two different approaches to represent catalysts as part of the input and propose catalysts based on their predicted CO conversion. The analysis covers the design of the descriptors employed by the models, as well as the results of an inverse prediction, that uncovered potential catalysts that can be researched for high CO conversion (≥95%), with Random Forest Regression predicting promoted Au/CeO 2 −ZrO 2 and Support-Vector Regression predicting promoted Au/CeO 2 , Ru/CeO 2 , and Rh/CeO 2 as the best overall catalyst candidates, and Yb/Au/CeO 2 −ZrO 2 to be of interest for WGS applications at low temperatures.