Chemical Phase Analysis

Fast identification of mineral phases using high-speed mapping

BSE Image and Element Map of a Mineral Sample
Top: BSE sample image with phases
identified by numbers (see text).
Bottom: Element map showing clearly
distinguishable phases

Simple phase differentiation tasks are often carried out using an electron microscope's BSE detector. In some cases this may fail, because density of phases is so similar that the image produces no contrast. This is where fast mapping is useful. Requiring only slightly more time, a wealth of information can be obtained, as the figures show: Where the naked eye can distinguish maybe three phases, mapping finds six.

The acquisition of this map with an resolution of 600 x 450 pixels took 300 s, using an input count rate of 220 kcps. The actual identification of phases was performed by evaluating spectra obtained from the differently colored areas. In this case the mineral proved to be consisting of calcium carbonate (no. 1 in the upper figure), a clay-altered silicate melt (2), calcium-magnesium phosphate (3), a sodium feldspar (4) and a potassium feldspar (5), all embedded in a quartz matrix (M).

Download the corresponding application note #02 (PDF)

Another very interesting application example of fast element mapping is to determine the ratio of alite to belite phases in Portland cement clinker. This ratio is an important parameter to determine the final strength of concrete produced from this cement.

Download the corresponding application note #06 (PDF)

Spectroscopic phase analysis of a ternary alloy

Element and Phase Maps of a Ternary Alloy
Top: Element map of a ternary alloy with regions for phase map creation. Bottom: Phase map of the alloy

ESPRIT Autophase is a powerful automatic chemical phase analysis tool. It is easily set-up using different parameters that can also be varied. This example shows its application to visualize and determine phase content of a ternary high-tech alloy. The rather exotic material is used in aerospace industry and consists of aluminum (Al), ruthenium (Ru) and platinum (Pt).

In this case Autophase was configured to use sample regions in an element map to find similarly composed regions in the rest of the map. This approach provides a much clearer picture of the sample and allows to easily determine area fractions of the phases:

Determined phases and their composition
PhaseArea
fraction / %
Al
/ at.%
Ru
/ at.%
Pt
/ at.%
P146.264.933.61.5
P241.873.217.39.6
P312.073.113.213.7

Download the corresponding application note #03 (PDF)

Advanced light element and low energy X-ray analysis of a TiB2-TiC-SiC ceramic material using EDS spectrum imaging

Phase Map of a Ceramic Material
Chemical phase map of a hard ceramic
material

The sample investigated is a sintered hard ceramic material, mainly composed of titanium di-boride (TiB2), titanium carbide (TiC), silicon carbide (SiC) and a number of minor constituents. A polished but uncoated section of this material was analyzed.

The properties of this material are governed by phase ratios, particle sizes and their distribution. ESPRIT Autophase allows to easily visualize phases and also to quantify ratios based on phase composition, as the figure above shows. Even minor phases that may are difficult to distinguish in a SEM micrograph, like the oxidic phases with a total share of 2.3 % or tungsten carbide (WC) with only 0.3 %, can clearly be seen. The following sample composition was determined:

Area fractions of major and minor phases
PhasesArea
fraction / %
Area fraction
/ pixels
Area
fraction / µm2
SiC33.125,404162
TiB232.424,889159
TiC31.924,490157
Oxides2.31,79211
WC0.32251

Download the corresponding application note #10 (PDF)

Low energy X-ray EDS analysis of ore mineralization at high spatial resolution

Chemical phase map worthington dike
Chemical phase map showing the
distribution of pyrite, chalcopyrite,
cobaltite/gersdorffite and telluride phases

Developments in energy-dispersive spectrometry (EDS) offer advanced analysis of low energy X-ray lines at high spatial resolution. This is demonstrated for cobalt nickel arsenic sulfide grains in samples from the Offset Dike of the Sudbury Igneous Complex (SIC). The minerals of economic interest were detected using a Bruker M4 TORNADO Micro-XRF spectrometer and located by automated feature analysis using SEM-EDS (see Bruker Application Note # EDS-13).

A FE-SEM equipped with a QUANTAX EDS system including an XFlash® 6 | 10 silicon drift detector (SDD) was used to study the element composition in bulk samples at high spatial resolution (pixel size < 50 nm). Spectrum images (HyperMap) were acquired. The EDS databases contain complete spectra for each pixel which supports data mining. The element identification can be improved by using the Maximum Pixel Spectrum function. This function synthesizes a spectrum consisting of the highest count level found in each spectrum energy channel. Even elements which occur in only a few or just one pixel of an element map can be easily identified.

Download the corresponding application note #14 (PDF)