PyTherNal: A python program for analyzing curie temperature from thermomagnetic data
Thermomagnetic analysis is performed by bringing subject materials into its cooled and heated state, followed by analyzing the magnetic moment change. Performing these would result in obtaining the Curie Temperature of the materials, which is essential in estimating magnetic minerals contained in ma...
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| Veröffentlicht in: | Journal of physics. Conference series Jg. 2309; H. 1; S. 12035 - 12046 |
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01.07.2022
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| Abstract | Thermomagnetic analysis is performed by bringing subject materials into its cooled and heated state, followed by analyzing the magnetic moment change. Performing these would result in obtaining the Curie Temperature of the materials, which is essential in estimating magnetic minerals contained in material samples. PyTherNal (Python Thermomagnetic Analyzer) is a thermomagnetic analysis tool in Python environment meant to assist in analyzing thermomagnetic data. The advantages of Python in its functionality and flexibility of being used in any operating system (OS) became the main reason for the program to be written in Python. PyTherNal is designed to assist in estimating Curie temperature of materials through thermomagnetic method, by locating the maximum curvature of the highest value of second (2
nd
) derivative of both cooling and heating data. To facilitate these, PyTherNal generates three figures, which are the curves for the thermomagnetic data, its 1
st
derivative, and its 2
nd
derivative. An advantage of the program is that it performs smoothing to increase the accuracy in estimating the Curie temperature as doing so would significantly minimize the variability of the derivative curve. Since the program is written in Python, it is open-source and therefore free to use. It is also capable of cross-platforming. |
|---|---|
| AbstractList | Thermomagnetic analysis is performed by bringing subject materials into its cooled and heated state, followed by analyzing the magnetic moment change. Performing these would result in obtaining the Curie Temperature of the materials, which is essential in estimating magnetic minerals contained in material samples. PyTherNal (Python Thermomagnetic Analyzer) is a thermomagnetic analysis tool in Python environment meant to assist in analyzing thermomagnetic data. The advantages of Python in its functionality and flexibility of being used in any operating system (OS) became the main reason for the program to be written in Python. PyTherNal is designed to assist in estimating Curie temperature of materials through thermomagnetic method, by locating the maximum curvature of the highest value of second (2
nd
) derivative of both cooling and heating data. To facilitate these, PyTherNal generates three figures, which are the curves for the thermomagnetic data, its 1
st
derivative, and its 2
nd
derivative. An advantage of the program is that it performs smoothing to increase the accuracy in estimating the Curie temperature as doing so would significantly minimize the variability of the derivative curve. Since the program is written in Python, it is open-source and therefore free to use. It is also capable of cross-platforming. Thermomagnetic analysis is performed by bringing subject materials into its cooled and heated state, followed by analyzing the magnetic moment change. Performing these would result in obtaining the Curie Temperature of the materials, which is essential in estimating magnetic minerals contained in material samples. PyTherNal (Python Thermomagnetic Analyzer) is a thermomagnetic analysis tool in Python environment meant to assist in analyzing thermomagnetic data. The advantages of Python in its functionality and flexibility of being used in any operating system (OS) became the main reason for the program to be written in Python. PyTherNal is designed to assist in estimating Curie temperature of materials through thermomagnetic method, by locating the maximum curvature of the highest value of second (2nd) derivative of both cooling and heating data. To facilitate these, PyTherNal generates three figures, which are the curves for the thermomagnetic data, its 1st derivative, and its 2nd derivative. An advantage of the program is that it performs smoothing to increase the accuracy in estimating the Curie temperature as doing so would significantly minimize the variability of the derivative curve. Since the program is written in Python, it is open-source and therefore free to use. It is also capable of cross-platforming. |
| Author | Yosia, Gabrian Granito Mariyanto, Mariyanto Nanlohy, George Billy Salim, Christopher |
| Author_xml | – sequence: 1 givenname: George Billy surname: Nanlohy fullname: Nanlohy, George Billy organization: Department of Geophysical Engineering, Faculty of Civil, Planning and Geo Engineering, Institut Teknologi Sepuluh Nopember , Indonesia – sequence: 2 givenname: Gabrian Granito surname: Yosia fullname: Yosia, Gabrian Granito organization: Department of Geophysical Engineering, Faculty of Civil, Planning and Geo Engineering, Institut Teknologi Sepuluh Nopember , Indonesia – sequence: 3 givenname: Christopher surname: Salim fullname: Salim, Christopher organization: Department of Geophysical Engineering, Faculty of Civil, Planning and Geo Engineering, Institut Teknologi Sepuluh Nopember , Indonesia – sequence: 4 givenname: Mariyanto surname: Mariyanto fullname: Mariyanto, Mariyanto organization: Department of Geophysical Engineering, Faculty of Civil, Planning and Geo Engineering, Institut Teknologi Sepuluh Nopember , Indonesia |
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| Cites_doi | 10.3390/geosciences8040116 10.1063/5.0015636 10.1063/1.361766 10.1088/1742-6596/2309/1/012028 10.1016/j.earscirev.2015.09.010 10.1016/j.rgg.2009.10.005 10.1016/j.dib.2019.104092 10.1016/j.scitotenv.2019.04.244 10.1063/5.0015630 10.1063/1.4990932 10.1016/j.jmmm.2017.06.105 10.1186/s40623-018-0906-5 10.1016/j.epsl.2008.11.006 10.1016/j.dib.2019.104348 10.1029/2006JB004507 10.1111/j.1475-4754.2010.00533.x |
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| References | Mariyanto (JPCS_2309_1_012035bib8) 2017 Musgrave (JPCS_2309_1_012035bib10) 2019; 124 Bayreuther (JPCS_2309_1_012035bib16) 1996; 79 Erdyanti (JPCS_2309_1_012035bib18) 2020; 1491 Bijaksana (JPCS_2309_1_012035bib4) 2019; 26 Mariyanto (JPCS_2309_1_012035bib6) 2019; 25 Antareza (JPCS_2309_1_012035bib20) 2022 Yunginger (JPCS_2309_1_012035bib5) 2018; 8 Dunlop (JPCS_2309_1_012035bib11) 2007 Ibragimov (JPCS_2309_1_012035bib13) 2009; 50 Hatakeyama (JPCS_2309_1_012035bib17) 2018; 70 Parés (JPCS_2309_1_012035bib2) 2010; 52 Roberts (JPCS_2309_1_012035bib9) 2015; 151 Erdyanti (JPCS_2309_1_012035bib12) 2020; 2251 Petrovský (JPCS_2309_1_012035bib14) 2006; 111 Arun (JPCS_2309_1_012035bib15) 2018; 448 di Vito (JPCS_2309_1_012035bib1) 2009; 277 Mariyanto (JPCS_2309_1_012035bib7) 2019; 675 Bagaskara (JPCS_2309_1_012035bib19) 2020; 2251 Novala (JPCS_2309_1_012035bib3) 2019; 1204 |
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| SubjectTerms | Curie temperature Data analysis Magnetic moments Magnetism Physics |
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| Title | PyTherNal: A python program for analyzing curie temperature from thermomagnetic data |
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