In this experiment, we will utilize two different detection modes: a Photo-Multiplier Tube (PMT) and a Charge-Coupled Device (CCD).

PMT Detection Mode:

 In the PMT mode, the experiment involves rotating the diffraction angle to measure the variation in light intensity and a CdSe detector was used to detect the intensity. The PMT is highly sensitive to low light levels and is ideal for detecting small changes in light intensity. By adjusting the diffraction angle using a servo motor controlled by an Arduino UNO board, the PMT can capture the scattered light intensity as a function of the angle, providing precise angular resolution of the light scattering pattern.

CCD Detection Mode:

 In the CCD mode, light intensity is measured by projecting the scattered light onto a transparent paper. The light intensity distribution is in the form of an image that a web camera can capture, and the analysis is performed using image processing techniques. This method allows for simultaneous measurement of the light intensity across multiple angles, making it highly efficient for obtaining a broad range of data in a single capture. The CCD’s ability to record the spatial distribution of light enables more complex analysis, such as capturing interference patterns or visualizing scattering across different regions.

 Both detection methods complement each other: PMT provides high sensitivity and precise angle-by-angle measurement, while CCD offers a comprehensive view of the scattering pattern in a single shot, suitable for image-based analysis.

Both detection methods complement each other: PMT provides high sensitivity and precise angle-by-angle measurement, while CCD offers a comprehensive view of the scattering pattern in a single shot, suitable for image-based analysis.

Step 2: Quick Test with 650 nm Red Laser: Use a 650 nm red laser to perform a quick test on the sample. Ensure that the diffraction rings are clearly visible on the screen. This step verifies that the optical alignment is correct and that the sample is appropriately prepared.

 Step 3: Capture Images with a Webcam: Using a webcam, take a photo of the diffraction pattern projected on the screen. Ensure the image captures the full diffraction rings clearly. Follow the appropriate method for capturing the image, such as adjusting the focus and exposure to optimize the clarity of the pattern.

 STep 4: Analyze the Image Using ImageJ: After capturing the image, open it in the software ImageJ for analysis. Measure the intensity distribution along the centerline of the pattern. In ImageJ, you can create a line profile across the central bright fringe to observe the intensity variations.

 Step 5: Identify and Record the Position of the Bright Fringes: Locate and document the position of the central bright fringe and the two bright fringes on either side of it. These positions will be used to calculate the diffraction angles.

 Step 6: Convert the Fringe Positions to Angles: Based on the experimental setup, use trigonometric formulas to convert the measured distances between the fringes into angles. The formula typically involves using the distance between the screen and the sample, along with the fringe spacing, to calculate the diffraction angle.

 Step 7: Repeat with Different Screen Distances: After completing the initial measurements, adjust the distance between the screen and the sample. Repeat steps 3 through 6, capturing new images, analyzing them, and recording the fringe positions.

Step 8: Complete the Worksheet: After collecting all the necessary data, complete the corresponding learning worksheet by inputting your calculated angles and comparing the results with theoretical predictions based on diffraction principles.

By following these steps, you will conduct a thorough analysis of the diffraction pattern and gain insights into the relationship between the fringe spacing, screen distance, and diffraction angles.

 Step 2: Quick Test with 650 nm Red Laser: Use a 650 nm red laser to perform a quick test by directing the laser at the sample. Check for the appearance of visible diffraction rings. You can proceed with the experiment once you confirm the rings are visible.

 Step 3: Perform the Experiment in a Dark Environment: It is recommended to experiment in a dark environment to obtain more accurate light intensity measurements. This reduces interference from ambient light, ensuring more precise data collection.

 Step 4: Upload Arduino Code for PMT Mode: Upload the provided Arduino code to the Arduino board. Afterward, open the Python code for PMT Mode in Jupyter Notebook and run it. Press the Zero position button to reset the motor to the initial position. Then, press the Start button, which will cause the motor to rotate from -90 degrees to +90 degrees while simultaneously recording the light intensity values.
Once the motor completes its rotation, the program will automatically save the recorded data in the same folder as the code. Please note that if the program is executed multiple times, it will overwrite the existing file. Therefore, after each run, it is recommended that the saved file be renamed to avoid data loss.

 Step 5: Analyze the Data Using Jupyter Notebook: After the data collection, run the Interaction Python Analysis script in Jupyter Notebook. This analysis tool includes an adjustable parameter bar, which allows you to fine-tune the experimental parameters to match the expected particle size. Use this tool to identify the particle size that best fits your data. Additionally, record the error for each data point regarding its angular position, allowing for a more accurate comparison between the experimental results and theoretical predictions.

 Following these steps, you will successfully conduct the PMT-based diffraction experiment and analyze the data to determine the particle size and accuracy of the measurement.