Abstracted/indexed

Detection of flammable gases is necessary to avoid explosive atmospheres. Commercial pellistors require an operation temperature above 450 °C for the detection of methane. We present a novel wireless low-power catalytic gas sensor system based on non-precious metal catalyst for the detection of methane and propane operated at 350 °C. The combination of a MEMS-based sensor with a low-power radio system provides the opportunity to monitor complex infrastructures without using a power grid.

In thermometry, the displayed temperature value of an object depends on the size of the object. This behaviour, the size-of-source effect, might be a major cause of measurement uncertainty in a thermoscene. The influence of diffraction and digitization can be described advantageously with the modulation transfer function. Especially with very small objects the displayed temperatures are too low. When imaging large objects, not only the edge areas are affected, but also the entire image.

A new metrological atomic force microscope (MAFM) head design is shown. We investigated the MAFM head in the nanomeasuring machine (NMM-1) for different high-precision and large-scale traceable measurement tasks. Due to their integration, the MAFM head can benefit from the large measuring range, high-precision and traceability of the NMM-1 for efficient measurements on different samples. The presented results show the realised macroscale measurements with sub-nanometre resolution.

We propose a surface plasmon resonance (SPR) sensor based on a ZnO / Au hybrid thin-film material structure and experimentally investigate its sensitivity improvement. The Kretschmann-based SPR sensor utilizes ZnO thin films and nanostructures for performance enhancement. The advancement in SPR technology relies on a low-cost, high-sensitivity, and high-selectivity sensor. Metal oxide (MO) has been incorporated into the SPR sensor to be used for detection of biological and chemical compounds.

This paper discusses the deployment of an internet of things (IoT)-based model to monitor the fermentation of tea in a tea factory in Kenya. The model uses deep learning to detect the optimum fermentation of tea as fermentation progresses. To further improve on the results, a majority voting technique based on regions is used. The model shows promising results, and its predictions correlated well with those of the experts.