Data for: Simplified Efficiency Calibration Methods for Scintillation Detectors --------------------------------------------------------------------- *by Victor V. Golovko* (ORCID: [0000-0003-4605-7937](https://orcid.org/0000-0003-4605-7937)) The project offers data that was used to develop simplified methods for calibrating the efficiency of scintillation detectors used in nuclear remediation. The study, authored by Victor V. Golovko and conducted at [Canadian Nuclear Laboratories](https://www.cnl.ca) in Chalk River, Ontario, introduces two new calibration approaches called "oversimplified" and "simplified." These methods are designed to make it easier to determine the photo-peak efficiency of scintillation detectors. The research aims to reduce waste and improve resource use in nuclear cleanup activities, in line with efforts for cleaner production and environmental sustainability. By using simplified calibration techniques and a general solution approach, the project effectively set up a scintillation detector system at Chalk River Laboratories. This system was tested with calibrated radioactive sources like 241Am, 57Co, 133Ba, 137Cs, and 60Co to ensure its accuracy. The system has met the acceptance criteria for accuracy, confirming its suitability for screening unstable isotopes at potentially contaminated sites. This screening is important as it helps to reduce the amount of material that needs to undergo detailed radioactive characterization, ultimately lowering the operational costs associated with nuclear remediation. The NaI(Tl) detector system was configured for near contact geometry, a standard measurement distance used at CRL for remediation projects. Detection limits were determined for this configuration, improving the effectiveness of scintillation detectors in nuclear remediation activities. This contributes significantly to environmental sustainability and promotes cleaner production practices. You can find more details about the study in the manuscript entitled 'Simplified Efficiency Calibration Methods for Scintillation Detectors Used in Nuclear Remediation.' The data and methods are documented within the associated R code files. Below, you will find a summary of how to execute the R code to prepare selected tables and figure for the manuscript. ### Simplified Efficiency Calibration Methods for Scintillation Detectors Used in Nuclear Remediation **Overview:** "Simplified Efficiency Calibration Methods for Scintillation Detectors Used in Nuclear Remediation" is a research study by Victor V. Golovko, published in the *Journal of Cleaner Production* in 2024. The study presents new methods to enhance the efficiency of scintillation detectors in nuclear cleanup processes, reducing waste and operational costs. **Publication Information:** - **Journal**: *Journal of Cleaner Production* - **Year**: 2024 - **Article Number**: 143910 - **DOI**: [https://doi.org/10.1016/j.jclepro.2024.143910](https://doi.org/10.1016/j.jclepro.2024.143910) - **Link**: [ScienceDirect Article](https://www.sciencedirect.com/science/article/pii/S0959652624033596) The *Journal of Cleaner Production* is an international open-access journal that supports environmental sustainability and cleaner production practices. It has an impact factor of 9.7 (2024), surpassing that of *Physical Review Letters* (8.1). ### Podcast Discussion For a detailed discussion on the findings of this study, listen to the [podcast](https://osf.io/pbmd2/files/osfstorage/670e6736ccfdf87f730148a9 ) created by AI. ### Key Contributions 1. **Waste Reduction and Cost Efficiency**: The NaI(Tl) detector system significantly reduced the number of soil samples requiring detailed characterization, lowering operational costs for nuclear remediation. 2. **Near-Contact Geometry Calibration**: The detector system was calibrated for near-contact geometry, a configuration commonly used at CRL, with detection limits established. 3. **Sustainability Impact**: The methods support sustainable nuclear remediation by improving resource efficiency and promoting cleaner production practices. ### Summary of Study The study developed two novel methods—termed "oversimplified" and "simplified"—for determining the photopeak efficiency of NaI(Tl) scintillation detectors. Additionally, a "general" solution method was used with radioactive sources such as: - **Americium-241 (241Am)** - **Cobalt-57 (57Co)** - **Barium-133 (133Ba)** - **Cesium-137 (137Cs)** - **Cobalt-60 (60Co)** These methods were used to commission a NaI(Tl) scintillation detector system at Chalk River Laboratories (CRL) for nuclear remediation. ### Keywords - Scintillation detectors - Simplified efficiency calibration methods - True coincidence sum-peak method - Nuclear remediation - Gamma spectrometry ### How to Get R and R Studio **Getting R:** 1. **Visit the CRAN Website**: Go to the Comprehensive R Archive Network (CRAN) at [https://cran.r-project.org/](https://cran.r-project.org/). 2. **Download R**: Select a mirror that is geographically close to you and download the appropriate version of R for your operating system (Windows, Mac, or Linux). 3. **Install R**: Open the downloaded file and follow the installation instructions. **Getting RStudio:** 1. **Visit the RStudio Website**: Navigate to [https://rstudio.com/products/rstudio/download/](https://rstudio.com/products/rstudio/download/). 2. **Download RStudio**: Choose the free version of RStudio Desktop and download it for your operating system. 3. **Install RStudio**: Open the downloaded file and follow the installation instructions to install RStudio. ### How to Run R Code in R Studio 1. **Open RStudio**: Launch RStudio from your applications or programs menu. 2. **Create a New R Script**: Go to File > New File > R Script. 3. **Copy and Paste R Code**: Copy the R code you want to run and paste it into the new R script window in RStudio. 4. **Run the R Code**: - You can run the entire script by pressing the "Run" button at the top of the script pane, or pressing `Ctrl+Enter` (Windows) or `Cmd+Enter` (Mac) while your cursor is in the line of code you want to execute. - Alternatively, highlight the section of the code you wish to run and press the "Run" button. 5. **View Output**: Output from the code will appear in the Console pane, and any variables or data frames created will appear in the Environment pane. ### What the 'Table1.R' Code Does - **Load Necessary Packages**: The script checks if the `xtable` package is installed and installs it if not. This package is used to convert R data frames to LaTeX tables. - **Define Data Frame**: It sets up a data frame named `roi_data` containing data about regions of interest (ROI) from a measurement of radionuclide peaks. The data includes various metrics such as energy levels, centroid values, counts, etc. - **Update Column Names**: It modifies the column names of the `roi_data` data frame to more specific names suited for LaTeX formatting. - **Convert to LaTeX Table**: The `xtable` function is used to convert the `roi_data` data frame into a formatted LaTeX table. The table includes a caption, label, and specific formatting instructions like no booktabs, placement at the top of the page, etc. - **Save LaTeX Code**: The formatted LaTeX code is then written to a file named "Table1.tex". - **Confirmation Message**: After saving, it prints a confirmation message stating that the LaTeX table has been successfully saved. ### What the 'Figure4.R' Code Does 1. **Library Loading**: The script starts by loading two essential R libraries: - `ggplot2`: Used for creating advanced plots and graphs. - `dplyr`: Helps in data manipulation and transformation. 2. **Data Generation**: - `energy`: This vector contains a sequence of energy values ranging from 55 to 1400 keV, incremented by 10 keV. These represent the energies at which detector efficiencies are evaluated. - `efficiency`: It calculates theoretical efficiencies using an exponential decay formula. This formula represents how detector efficiency might naturally decrease as energy increases. 3. **Model Definition**: - A custom function `efficiency_model` defines a mathematical model using four parameters (A, B, C, D). This function models efficiency as a complex function of energy, which could reflect real physical properties of a detector system. 4. **Data Preparation**: - `df`: A dataframe created from specific energy values (`e.val`) and their corresponding efficiencies (`ef.val`). These are likely observed or experimentally determined values. - `fitted_data`: This dataframe spans the entire range of generated energies and stores efficiencies predicted by the fitted model. 5. **Model Fitting**: - The script fits the nonlinear model defined by `efficiency_model` to the data in `df` using the `nls` function, which performs nonlinear least squares fitting. 6. **Plotting**: - Two plots are generated using `ggplot2`. The first plot visually represents how the observed efficiencies compare to those predicted by the model across the defined energy range. Points (blue) show observed efficiencies, and a line (red) shows the model's predictions. - The second plot, optional, enhances the first by adding new estimated efficiencies for additional energy values, plotted in a different color for distinction. 7. **Statistical Analysis**: - The script calculates several statistical metrics to evaluate the quality of the fit, including residuals, the sum of squared residuals (RSS), R-squared, and the root mean square error (RMSE). These metrics help assess how well the model fits the observed data. 8. **Efficiency Estimation for New Energies**: - Additional energies are appended to the initial dataset, and efficiencies for these new points are estimated using the fitted model. This step is crucial for practical applications where predictions at untested energies are needed. 9. **Sorting and Saving**: - The complete set of energies and their estimated efficiencies are sorted and saved into a CSV file. This makes it easy to review or use the estimated data in other applications or analyses. 10. **Output and Documentation**: - Detailed outputs, including the plot and statistical summaries, are printed or saved. These outputs are essential for verifying the model's performance and understanding the behavior of the detector efficiency across different energies.