自动分析

The phrase "automated mineralogy" has been synonymous with the use of scanning electron microscopes (SEM) for decades. The classification of minerals using SEM-based energy dispersive spectroscopy (EDS) chemical measurements has been the only method for rapid, automated phase ID in microscopy.

Making full use of the imaging capability of the X-ray microscope, enhanced by DeepRecon noise-reducing deep learning algorithms, Phase Identifier 3D automatically classifies the mineralogy of the sample based on attenuation measurements. The ability to classify the mineralogy in tomographic scans is unique and when combined with the morphological measurements of the 3D reconstructed entities allows the calculation of standard and customized data outputs such as true grain size/shape and mineral association measurements.

Where 2D automated mineralogy offers liberation based on exposed surfaces or edges, associations based on linear contacts, and modal mineralogy based on phases exposed on the surface of the sample mount, 3D mineralogy measures liberation based on the volume of the grains and their exposure on the surface of the unaltered particle. Associations are based on full mineral surface contacts with other phases, and modal mineralogy accounts for all grains: those exposed on the surface of the particles and those obscured within.

ZEISS Phase Identifier 3D applies X-ray microscopy techniques and deep learning algorithms to execute automated mineralogy analyses in 3D that provide particle identification, mineral classification, and data outputs, including liberation and association measurements.