Discussion on the accuracy of direct reading spectrometer
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Direct Reading Spectrometer
Both are applied to the pre-furnace analysis of the smelting or casting process. In order to obtain an accurate analysis result, in addition to the performance of the spectrometer itself, the correct use, operation, maintenance and management of the instrument are essential. By ensuring proper handling and regular calibration, the spectrometer can fully perform its function and deliver reliable results.
It's inevitable that errors occur during spectral analysis. There are various sources of error. For example, uneven composition between the standard sample and the analyzed sample, differences in structural state, unstable spectrum performance, improper surface treatment of the sample, and impure argon gas can all contribute to inaccuracies. Therefore, it’s crucial for analysts to understand these error sources and develop strategies to minimize them.
The excitation source is one of the most critical components in an optoelectronic spectrometer. Its main role is to provide energy for evaporation, atomization, or excitation of the sample. These processes often occur simultaneously, directly affecting the analytical outcome. Factors such as the melting point, boiling point, atomic weight, chemical reaction, dissociation energy, ionization energy, and excitation energy of the elements influence the emission of spectral lines. Additionally, the characteristics of the light source used play a significant role in determining the efficiency and accuracy of the analysis.
Currently, two common types of light sources are used: arc and spark light sources, which include high-voltage wave-controlled, low-pressure high-speed spark, and high-energy pre-spark sources—often used in metallurgical analysis. Another type is the plasma light source, widely applied across different fields.
Spectroscopic analysis typically involves several types of discharge mechanisms:
1. High-energy pre-spark discharge has a maximum current of 150 amps and a burning time of 150 microseconds, resulting in a more uniform distribution of the burning spots, reducing element interference and bonding effects.
2. Spark-type discharge offers good reproducibility for most elements.
3. Arc-type discharge has lower reproducibility compared to spark discharge but provides better detection limits for trace elements.
When selecting a light source, the following criteria should be met:
1. High sensitivity, so even small changes in element concentration lead to noticeable signal variations.
2. Low detection limit to detect trace components effectively.
3. Good stability to ensure consistent evaporation, atomization, and excitation, leading to precise results.
4. A high ratio of line intensity to background intensity (good signal-to-noise ratio).
5. Fast analysis speed with minimal pre-ignition time.
6. Simple construction, easy operation, and safety.
7. Minimal self-absorption effect and a wide linear range for calibration curves.
The selection of excitation conditions depends on the sample being analyzed. Pre-combustion time varies depending on the material, and this is influenced by factors like the sample’s composition, structure, and the energy and atmosphere of the light source.
Spark discharge in an argon atmosphere can be categorized into two extremes: concentrated discharge and diffusion discharge. Concentrated discharge occurs when the spark is on the metal phase, while diffusion discharge happens when the spark is on non-metallic areas. Common causes of diffusion discharge include impure argon, leaks in the gas system, and oxygen introduced through the sample itself, such as inclusions or cracks. Argon pressure, flow rate, and rinsing time also affect the accuracy of the analysis.
The matrix effect, also known as coexistence element effect, refers to the influence of other components in the sample besides the analyte. This is a major factor in spectral line intensity variation and is considered one of the most complex issues in spectroscopy.
In practice, differences between the smelting process and the physical state of the sample and standard samples often cause the calibration curve to shift. Standard samples are usually forged or rolled, while analysis samples are often cast. To avoid the impact of metallurgical state changes, control samples that match the sample’s metallurgical process and physical condition are used to ensure accurate results.
In spectral analysis, the sampling method and sample preparation are crucial. They directly affect the accuracy of the results. During pre-furnace analysis, a red cut is taken from the as-cast steel sample in the furnace. If the sample shows cracks, inclusions, or pores, it must be resampled. For low-carbon steel, rapid quenching in water helps form martensite and austenite, improving carbon analysis accuracy. For high-carbon samples, careful cutting is needed to prevent cracking. Cast iron and ductile iron samples must be fully white, with standardized sampling procedures including temperature, demolding time, and cooling rate. Different materials require different abrasive tools, typically medium-grit alumina wheels, and the sample surface should be removed by 0.5–1.5 mm, as surface oxides can lead to inaccurate results, especially in carbon analysis.
In summary, errors in detection and analysis are inevitable, but they can be managed. For photoelectric spectroscopy, the main error sources are fivefold:
Person: The operator's awareness, skill level, experience, and physical condition.
Equipment: Proper maintenance of the equipment, including the light source, argon system, and sample preparation tools.
Sample: Uniformity, representativeness, heat treatment, and surface condition of the sample.
Method: Accuracy of the calibration curve, standardization process, and proper selection of control samples.
Environment: Stable room conditions, including temperature, humidity, and electromagnetic interference.
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