Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks
by
Yüksel, Mehmet
, Ünsal, Emre
in
Accuracy
/ Algorithms
/ artificial neural networks
/ dose response
/ Dosimetry
/ glow curve deconvolution
/ kinetic parameters
/ Methods
/ Minerals
/ Neural networks
/ Nuclear energy
/ Optimization algorithms
/ plagioclase
/ Radiation
/ thermoluminescence
2025
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks
by
Yüksel, Mehmet
, Ünsal, Emre
in
Accuracy
/ Algorithms
/ artificial neural networks
/ dose response
/ Dosimetry
/ glow curve deconvolution
/ kinetic parameters
/ Methods
/ Minerals
/ Neural networks
/ Nuclear energy
/ Optimization algorithms
/ plagioclase
/ Radiation
/ thermoluminescence
2025
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks
by
Yüksel, Mehmet
, Ünsal, Emre
in
Accuracy
/ Algorithms
/ artificial neural networks
/ dose response
/ Dosimetry
/ glow curve deconvolution
/ kinetic parameters
/ Methods
/ Minerals
/ Neural networks
/ Nuclear energy
/ Optimization algorithms
/ plagioclase
/ Radiation
/ thermoluminescence
2025
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks
Journal Article
Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks
2025
Request Book From Autostore
and Choose the Collection Method
Overview
The thermoluminescence (TL) method is one of the most widely used techniques in various studies, including dosimetric applications, dating of archaeological and geological materials, luminescence spectroscopy of certain insulating or semiconducting phosphors, and the detection of ionizing radiation damage. This study examines the TL properties of plagioclase, a feldspar group mineral, focusing on its dose–response behavior, kinetic parameters, and glow curve characteristics. TL measurements of plagioclase samples were carried out with different ionizing radiation doses ranging from 0.1 to 550 Gy. The results show a strong linear dose–response relationship in the 0.3–550 Gy range, with no evidence of saturation or supralinearity. A computerized glow curve deconvolution (CGCD) analysis revealed that the TL glow curve of the mineral consists of five distinct TL peaks with activation energies ranging from 0.842 eV to 0.890 eV and obeying general order kinetics. In addition, an artificial neural network (ANN) model was developed to predict TL glow curves using three optimization algorithms, including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Among these, the BR algorithm demonstrated the best performance with an accuracy value of 0.99915, a Mean Absolute Error (MAE) of 2.34 × 10−3, and a Mean Squared Error (MSE) of 3.82 × 10−5, outperforming LM and SCG in in terms of generalization and accuracy. The findings of this study demonstrate the effectiveness of combining TL analysis with ANN-based modelling for accurate dose–response predictions and the improved luminescence characterization of plagioclase, supporting the applications of luminescence studies in radiation dosimetry and geochronology.
Publisher
MDPI AG
This website uses cookies to ensure you get the best experience on our website.