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We study structural, magnetic and magneto-thermal properties of GdRhIn compound. The room temperature X-ray diffraction measurements show hexagonal crystal structure. Temperature and field dependence of magnetization suggest two magnetic transitions antiferromagnetic to ferromagnetic at 16 K and ferromagnetic to paramagnetic at 34. The heat capacity measurements confirm both the magnetic transitions in GdRhIn. The magnetization data was used to calculate isothermal magnetic entropy change and refrigerant capacity in GdRhIn, which was found to be 10.3 J/Kg-K for the field change of 70 kOe and 282 J/Kg for the field change of 50 kOe, respectively. The large magnetocaloric effect in GdRhIn suggests that the material could be used for magnetic refrigeration at low temperatures., Comment: 11 pages, 4 figures

The progress in materials science has always been associated with the development of functional materials systems, which enables us to design proof-of-concept devices. To advance further, theoretical predictions of new novel materials and their experimental realization is very important. This chapter reviews the intriguing properties of rare earth-based materials and their applications in spintronics. Spintronics is an emerging technology, which exploits spin degree of freedom of an electron along with its charge property. Discovery of various physical phenomena and their industrial applications in the field of magnetic sensors, magnetic recording and non-volatile memories such as magnetic random access memory (MRAM) and spin-transfer torque (STT) MRAM opens several new directions in this field. Materials with large spin polarization, strong spin-orbit coupling, and tunable electronic and magnetic properties offer an excellent platform for the spintronics technology. Combination of rare earths with other elements such as transition metals show broad range of structural, electronic, and magnetic properties which make them excellent candidates for various spintronic applications. This chapter discusses many such materials ranging from Heusler alloys, topological insulators to two-dimensional ferromagnets and their potential applications. The review gives an insight of how rare-earth materials can play a key role in emerging future technology and have great potential in many new spintronic related applications., Comment: 55 pages, 20 figures, 3 tables

The chapter discusses the structural and magnetic properties of equiatomic ternary RTX compounds, where R represents rare earth, T is transition metal, and X belongs to the p block elements. RTX compounds exhibit a variety of crystal structures, which leads to a range of magnetic phenomenon ranging from long range antiferromagnetic-ferromagnetic ordering, unconventional superconductivity, magnetic frustration to spin ices. Coexistence of various magnetic phenomenon result in many exotic properties, which make these materials promising for next generation technological applications. In RTX family of compounds, most transition metals, except manganese, Mn do not contribute significantly to the magnetic moment and behave as non-magnetic. The lack of magnetic behaviour in the transition metal sublattices of these ternary compounds may be attributed to hybridization between p electron states of X atom and d electron states of the transition metal, resulting in the filling of the d band. The dominant interaction in these compounds is of Ruderman Kittel Kasuya Yosida type due to the localized nature of the 4f electrons in the rare earths. The magnetic transition temperature in these materials varies from ultra-low to high temperatures, making them suitable for integration into devices operating at room temperature. The RTX series discussed here encompasses all rare earth elements, a range of transition metals, and various p-block elements such as Al, Ga, In, Si, Ge, Sn, Sb, and Bi. Most of the materials studied are arc melted polycrystalline materials with some in the form of single crystal or thin films., Comment: 102pages, 24 figures, 2 tables

We synthesize CoFeRuSn equiatomic quaternary Heusler alloy using arc-melt technique and investigate its structural, magnetic and transport properties. The room temperature powder X-ray diffraction analysis reveals that CoFeRuSn crystallizes in cubic crystal structure with small amount of DO3 - disorder. The field dependence of magnetization shows non-zero but small hysteresis and saturation behavior up to room temperature, indicating soft ferromagnetic nature of CoFeRuSn. The magnetic moment estimated from the magnetization data is found to be 4.15 {\mu}B / f.u., which is slightly less than the expected Slater-Pauling rule. The deviation in the value of experimentally observed moment from the theoretical value might be due to small disorder in the crystal. The low temperature fit to electrical resistivity data show absence of quadratic temperature dependence of resistivity, suggesting half-metallic behavior of CoFeRuSn. The high Curie temperature and possible half-metallic behavior of CoFeRuSn make it a highly promising candidate for room temperature spintronic applications., Comment: 10 pages, 4 figures

The continuous rise in CO2 emission into the environment is one of the most crucial issues facing the whole world. Many countries are making crucial decisions to control their carbon footprints to escape some of their catastrophic outcomes. There has been a lot of research going on to project the amount of carbon emissions in the future, which can help us to develop innovative techniques to deal with it in advance. Machine learning is one of the most advanced and efficient techniques for predicting the amount of carbon emissions from current data. This paper provides the methods for predicting carbon emissions (CO2 emissions) for the next few years. The predictions are based on data from the past 50 years. The dataset, which is used for making the prediction, is collected from World Bank datasets. This dataset contains CO2 emissions (metric tons per capita) of all the countries from 1960 to 2018. Our method consists of using machine learning techniques to take the idea of what carbon emission measures will look like in the next ten years and project them onto the dataset taken from the World Bank's data repository. The purpose of this research is to compare how different machine learning models (Decision Tree, Linear Regression, Random Forest, and Support Vector Machine) perform on a similar dataset and measure the difference between their predictions., Comment: Submitted to Annals of Data Science, 2022 - Springer