Also known as matrix effects, coexisting element effects, or third element effects, interference refers to the influence of all other components in a sample apart from the analyte. In spectroscopy, this can cause changes in the intensity of spectral lines, leading to errors in the analysis. This is a crucial issue that must be carefully managed in spectroscopic analysis.
In practice, there are often differences in the metallurgical process and physical state between analytical and standard samples, which can cause the calibration curve to shift. Typically, standard samples are in a forged or rolled state, while analytical samples are often in a cast state. To prevent changes in the metallurgical state of a sample from affecting the results, it's essential to ensure that the control samples used have the same metallurgical process and physical state as the analytical samples. This helps to accurately manage the analysis results.
The sampling method and sample preparation are critical factors that impact the precision and accuracy of spectroscopic analysis. For a rapid analysis on the furnace floor, a red-hot sample from the cast steel is cut quickly. If the sample has issues like cracks, impurities, or pores, it must be re-sampled. For low-carbon steel, it should be rapidly cooled in running water to form a martensitic and austenitic structure, which effectively improves the accuracy of carbon analysis. However, it's important to note that high-carbon samples should not be quenched after cutting to prevent cracking.
For the analysis of cast iron and ductile iron, it's necessary to ensure the sample is chilled to form a white iron structure. The sampling temperature, mold release time, and cooling speed must be standardized.
Furthermore, different materials require specific grinding tools. An aluminum oxide grinding wheel is commonly used, and the grit size should be medium—neither too coarse nor too fine. It is crucial to grind off 0.5 to 1.5 mm from the sample surface to remove the oxide layer, as this layer can lead to inaccurate results. This is particularly important for carbon analysis, which is highly sensitive to the presence of an oxide layer.
Errors are an inevitable part of photoelectric emission spectroscopy. It's important to understand them, identify their causes, and take effective measures to eliminate them. In addition to the factors mentioned above, the following can also influence analysis results:
Human Factors: The operator's awareness of quality, sense of responsibility, skill level, and proficiency can all affect the analysis outcome.
Equipment Factors: The condition of the equipment, including whether it's regularly maintained, the stability of the light source, the stability of the argon gas supply, and the condition of the sample preparation equipment, can all impact the accuracy of the results.
Sample Factors: The uniformity and representativeness of the sample, its heat treatment state, and microstructure are important. The uniformity of both standard and control samples, the reliability of their certified values, and the consistency of their microstructure are also key. The grinding method and its effectiveness are also critical.
Methodology Factors: The quality of the analytical curve and its fit, the efficiency of the standardization process, the proper selection of control samples, and the rigor of their value assignment can all affect the analysis.
Environmental Factors: The temperature and humidity of the analysis room, potential electromagnetic interference, and overall cleanliness can affect results. A stable operating environment is essential for ensuring the accuracy of the analysis.
