Do Different Peak Heights in XRD Analysis Indicate a Different Substance?

In summary, the conversation discusses the use of X-ray diffraction for analyzing samples and determining their chemical composition. The speaker mentions that their sample has similar diffraction peaks to the reference, but with different heights. This raises the question of whether this is enough evidence to conclude that the sample matches the reference or if it could be a different substance. The speaker also asks for recommendations for up-to-date and free reference textbooks for mastering XRD analysis, as they are new to the research field. The expert suggests using Rietveld refinement and programs like Topas to separate patterns and mentions that the 1956 book by Cullity is still a good resource. They also explain that the 2θ values are only dependent on the shape and
  • #1
Baho Ilok
47
5
The major diffraction peaks of my sample have essentially the same 2θ values as the reference (graphically), but have different heights. Can it still count as conclusive evidence that my sample matches the reference? Or does it suggest that my sample is a different substance?

Also, as a side question, can you suggest any good, up-to-date and free reference textbooks (or any other sources) that I should read up to master XRD analysis? The one I'm reading right now is from 1956.

I'm kind of new to the research field. I would really appreciate your help.

Thanks!
 
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  • #2
Its tricky to do. There is a process called Rietveld refinement that can help separate patterns. Programs like Topas are also quite good at doing this. You may have to rely on minor peaks if your reference pattern is in the same Laue pattern as your data.

If the 1956 book is by Cullity, its still very good. The updated version (cullity and stock) is the one I use.
 
  • #3
Baho Ilok said:
Can it still count as conclusive evidence that my sample matches the reference? Or does it suggest that my sample is a different substance?
They are not the same.

If I remember correctly from the 1970s, the 2θ values are a function only of the shape and size of the cell.
The density of the peaks is dependent on which sites in the unit cell are occupied by which atoms / elements. So different peak heights to the reference means different chemistry with the same size unit cell.
 

1. What is XRD data analysis?

XRD data analysis is a technique used to analyze the crystal structure and composition of materials. It involves measuring the diffraction pattern of X-rays as they interact with a sample, and then using mathematical algorithms to interpret the data and determine the atomic and molecular properties of the sample.

2. Why is XRD data analysis important?

XRD data analysis is important because it provides valuable information about the atomic and molecular structure of materials, which is crucial for understanding their physical and chemical properties. This information is used in various fields, such as materials science, chemistry, geology, and pharmaceuticals, to develop new materials and improve existing ones.

3. What types of materials can be analyzed using XRD?

XRD data analysis can be used on a wide range of materials, including crystalline solids, powders, thin films, and liquids. It is especially useful for studying materials with well-ordered atomic structures, such as minerals, metals, ceramics, and polymers.

4. How is XRD data analyzed?

XRD data is analyzed by first collecting the diffraction pattern of a sample using an X-ray diffractometer. The data is then processed using specialized software, which identifies the positions and intensities of the diffraction peaks. These peaks are then matched to a database of known crystal structures to determine the composition and structure of the sample.

5. What are the limitations of XRD data analysis?

While XRD data analysis is a powerful technique, it does have some limitations. For example, it can only provide information about the crystalline structure of a material, so it is not suitable for analyzing amorphous materials. Additionally, the quality and accuracy of the data can be affected by factors such as sample preparation, instrument calibration, and the presence of impurities.

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