An imaging system for microbial corrosion analysis

Abstract

This paper describes a viable and self-contained imaging system able to assess and quantify the effects of microbial corrosion on metals surface. The proposed image processing uses Scanning Electron Microscope micrographs to analyze bacteria attachment on sample surface and to estimate the degree of degradation of the material. After a preliminary brightness and contrast normalization, which refines the image taken by the operator, the software is able to identify dark spots on the clear metal surface. These are then attributed to singly attached bacteria or to larger clusters, which are the most dangerous ones, as they could overlay corrosion pits. After that, the degradation of the material is evaluated through the quantification of microbial attachment on the surface and through dimensional distribution of bacteria clusters.

Biocorrosion on Nanofilms Induces Rapid Bacterial Motions via Iron Dissolution

“…Therefore, the early steps remain difficult to predict and observe experimentally at the micrometer scale. For instance, light reflection and absorption by the metal hinder direct observation of bacteria (see for instance refs − and references herein); alternative scanning electron microscopy has been recently proposed …”

“…For instance, light reflection and absorption by the metal hinder direct observation of bacteria (see for instance refs 14−16 and references herein); alternative scanning electron microscopy has been recently proposed. 17 Here, we work with nanosheets (or nanofilms) composed of iron nanolayers (Figure 1A). Reactivity and stability of the semitransparent nanofilm are studied in the presence of bacteria at the macro-and microscale levels.…”

A metrological approach for multispectral photogrammetry

“…Even though several references can be used in different fields [16]- [18], in this case a specific reference object has to be employed. The reference object was tested using a photogrammetric measuring system, that allows acquisition of both the Visible-reflected (VIS) and the UV-induced luminescence (UVL) images.…”

Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering

“…In [ 20 ], a linear Kalman filter was implemented in an algorithm of 3D orientation detection by combining data from three sensors (a tri-axis accelerometer, a tri-axis gyroscope, and a tri-axis magnetometer). Other examples of sensor fusion applications are reported in [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], where sensor data fusion was implemented using neural networks or Bayesian inference. However, such an approach of combining data from different sensors has never been adopted so far for inductive magnetic measurements.…”