Thermoluminescence studies of calcite conducted by bacterial CaCO3 precipitation in organic soil

In this study, the thermoluminescence (TL) properties of the calcite conducted by bacterial calcium carbonate (CaCO₃) precipitation (BCCP) in organic soils were investigated. The bacterial calcium carbonate (CaCO₃) precipitation (BCCP) is a popular technique and has been applied in a variety of civil and geotechnical engineering applications. For example, bacterial calcium carbonate precipitation fills the gaps on the organic ground and makes cementing it with the biological method using bacteria. The study reveals that the calcium carbonate mineral called as calcite has a clear TL glow curve with four main peaks located around 90, 140, 210 and 240 °C, a wide linear dose response region between 140Gy and 2.3 kGy is observed. In addition, a good reusability is seen in the high temperature peaks. The TL glow curve peaks are not affected by reusability. Although the dosimetric peaks at 210 and 240 °C appear to be nearly constant, the TL peak intensities at 90 °C and 140 °C completely faded after 24 and 336 h storage time, respectively. The activation energies (E_a) and frequency factors (s) for peaks at 90 °C, 140 °C, 210 °C and 240 °C were evaluated via variable heating rate (VHR). The activation energy of the peaks in the TL glow curve is lying between 0.57eV and 1.04 eV.

Classification of thermoluminescence features of CaCO3 with long short-term memory model

Calcium carbonate (CaCO₃ ), a mineral commonly found in the Earth's crust, is mainly in the forms of calcite and aragonite. Calcite has the most stable type of thermodynamics at room temperature and ambient pressure. It has wide band gap structure and is of great interest for a wide-range technical and industrial applications due to its physical properties and suitability. In this study, a new method based on the long short-term memory (LSTM) model of deep learning is proposed to classify the thermoluminescence properties such as fading, cycle of measurement, heating rate, and dose-response of CaCO₃ . Therefore the thermoluminescence properties of calcite was investigated as a suitable band structure and its coherent data were used to classify the features using a deep learning LSTM model. Experiments were carried out on a dataset consisting of four classes. The accuracy, precision, and sensitivity values of the proposed model obtained were 98.34, 97.90, and 98.56%, respectively. The classification process of the results obtained from the designed model showed good performance.