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Elvin Ahmadov

Publications and source records attributed to Elvin Ahmadov.

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Phase equilibria in MnSb2Te4-GeSb2Te4 system and magnetic properties of Mn1-xGexSb2Te4 solid solutions

As a sister compound of the antiferromagnetic topological insulator MnBi2Te4, MnSb2Te4 is also a candidate for exotic magnetic topological phases. On the other hand, the structurally analogous but nonmagnetic phase-change material GeSb2Te4 is also known to exhibit nontrivial band topology. Motivated by their shared crystal structure, the similar ionic radii of Mn2+ and Ge2+, and the opportunity to explore the interplay between magnetism and topology, here we investigate the effects of Ge substitution at Mn sites in MnSb2Te4. The Mn1-xGexSb2Te4 solid solutions were synthesized via high-temperature solid-state reaction and characterized for composition, structure, phase behavior, and magnetism using SEM-EDS, PXRD, DTA, and SQUID. Ge substitution was successful across the full composition range, producing homogeneous, single-phase samples that melt via peritectic reactions, as confirmed by the MnSb2Te4GeS-b2Te4 phase diagram. Ge substitution strengthens the sample's paramagnetism, but with ferrimagnetic ordering up to x = 0.75, with both effective moment and saturation magnetization decreasing with increasing Ge content. Two distinct magnetic transitions - high-temperature paramagnetic to ferrimagnetic and low-temperature ferrimagnetic to ferromagnetic - were identified, with a dome-like shape dependence of the low-temperature magnetic transition on Ge substitution. A negative magnetization was observed in the pristine MnSb2Te4 and x = 0.12 substituted samples, while two distinct spin-flop transitions appeared in the samples with x = 0.32 and x = 0.55 Ge substitutions as a result of competing magnetic orderings. These findings facilitate future selective single-crystal growth of homogeneous, magnetic phases, paving the way for magneto-transport and topological surface states investigations.

cond-mat.mtrl-sci↗

Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning

In recent years, one of the major challenges for financial institutions is the retention of their customers using new methodologies of reliable and profitable segmentation. In the field of banking, the approach of offering all of the services to all the existing customers at the same time does not always work. However, being aware of what to sell, when to sell and whom to sell makes a huge difference in the conversion rate of the customers responding to new services and buying new products. In this paper, we used RFM technique and various clustering algorithms applied to the real customer data of one of the largest private banks of Azerbaijan.

cs.LG↗