Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge.

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

Scanning probe microscopy investigations of the electrical properties of chemical vapor deposited graphene grown on a 6H-SiC substrate.

Sublimated graphene grown on SiC is an attractive material for scientific investigations. Nevertheless the self limiting process on the Si face and its sensitivity to the surface quality of the SiC substrates may be unfavourable for later microelectronic processes. On the other hand, chemical vapor deposited (CVD) graphene does not posses such disadvantages, so further experimental investigation is needed. In this paper CVD grown graphene on 6H-SiC (0001) substrate was investigated using scanning probe microscopy (SPM). Electrical properties of graphene were characterized with the use of: scanning tunnelling microscopy, conductive atomic force microscopy (C-AFM) with locally performed C-AFM current-voltage measurements and Kelvin probe force microscopy (KPFM). Based on the contact potential difference data from the KPFM measurements, the work function of graphene was estimated. We observed conductance variations not only on structural edges, existing surface corrugations or accidental bilayers, but also on a flat graphene surface.