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Monalisa Cavalcante

Publications and source records attributed to Monalisa Cavalcante.

2 recordsLinked to original sources

Unifying Magnetic, Electrical, and Thermoelectric Responses in a Non-Collinear Antiferromagnet

Non-collinear antiferromagnets offer unconventional routes for controlling magnetic, electrical, and thermoelectric responses through their complex spin configurations and interfacial symmetry. Here, we detect exceptional interfacial properties arising from a non-collinear antiferromagnet by demonstrating a robust and quantitatively consistent exchange-bias response in an IrMn$_3$/Py heterostructure. The system consists of a 10 nm IrMn$_3$ layer coupled to a 5 nm permalloy (Py) film. Longitudinal magneto-optical Kerr effect (MOKE), anisotropic magnetoresistance (AMR), and anomalous Nernst effect (ANE) measurements independently reveal the same unidirectional field shift of approximately 50 Oe, together with a consistent cosine angular dependence. The simultaneous observation of these signatures establishes a direct correspondence between the interfacial magnetic symmetry imposed by the non-collinear antiferromagnet and the responses of the adjacent ferromagnet. This symmetry is manifested consistently in magnetization reversal, charge transport, and thermally driven voltage generation, demonstrating that the exchange-bias imprint is not restricted to a single experimental observable. Our results reveal a coherent multifunctional response of IrMn$_3$/Py and extend the conventional understanding of exchange bias beyond collinear antiferromagnetic systems. More broadly, they demonstrate the potential of non-collinear antiferromagnets as platforms for coupling magnetic, electrical, and thermoelectric functionalities in spintronic and spin-caloritronic devices.

cond-mat.mtrl-sci↗

New method of image processing via statistical analysis for application in intelligent systems

Image processing has always been a topic of significant importance to society. Recently, this field has gained considerable prominence due to the development of intelligent systems. In this work, we present a new method of image processing that utilizes statistical analysis, specifically designed for applications in intelligent systems. We tested our method on a large collection of images to assess its effectiveness.

physics.data-an↗