Exeter Analytical offers a technical report that discusses how CHN microanalyser design affects performance (accuracy, precision), ease of use and handling of demanding sample types.
The report examines the consequences of different commercial CHN microanalyser designs when the instruments are run in real laboratory environments.
Data is provided showing the impact of horizontal versus vertical combustion systems.
In a horizontal furnace arrangement, the sample is introduced into the combustion tube on a quartz ladle, which allows the removal of all sample residues after combustion.
In a vertical furnace arrangement, the samples are combusted on top of previously combusted samples.
This difference is a major factor contributing to the advantages of a static system with a horizontal furnace over both the dynamic and hybrid designs.
This build-up of sample residue in the combustion zone of vertical furnace systems is shown to considerably increase the potential for poor analytical data.
The most important criterion for CHN analysis in the majority of analytical laboratories is optimum accuracy across a wide range of sample types.
With constant pressure to increase laboratory productivity, an analyst does not want to set up their analyser with different operational parameters for every different sample type they come across.
Test data from an independent multi-laboratory study is included in the report, demonstrating the superior accuracy routinely achievable, and how the inherently longer-term stability of a horizontal furnace design markedly decreases time lost due to the need for recalibrations and sample re-runs.
Considerable variance in instrument performance can also be seen with more demanding samples.
The advantages of a horizontal furnace design CHN microanalyser is illustrated with applications including time-dependent combustible samples and volatile liquids.
The report concludes that using a horizontal furnace design CHN microanalyser, such as the Exeter Analytical Model 440, allows analysts to routinely produce accurate data on wide-ranging sample types without system reoptimisation, saving precious time and reducing running costs.
