This article describes the determination of ferrite and austenite proportions in the weld zone of duplex steels using image processing and neural networks. These proportions allow conclusions to be drawn about the quality of the weld. For this purpose, neural networks of different architectures were applied to various image data configurations of metallography images. Depending on the architecture and application of the networks, error rates of 2 % to 3 % were achieved, which met expectations.

Methane plays an important role in global warming, and tracing its sources requires precise analysis of its isotopic fingerprint. In this study, we demonstrate the foundation of a system based on optical feedback cavity-enhanced absorption spectroscopy that achieves the stability needed for accurate isotope measurements. This work shows the potential of the method for future climate studies and environmental monitoring.