journal article·2019-10

Transport properties and electroresistance of manganite based heterostructure

Khushal Sagapariya, D. Venkateshwarlu, Bhargav Rajyaguru, Keval Gadani, V.G. Shrimali, D.D. Pandya, E.P. Amaladass, N.A. Shah, P.S. Solanki

Ceramics International· Volume 45· Issue 15
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Abstract

Abstract In the present communication, ZnO/La0.7Sr0.3MnO3/Al2O3 (ZnO/LSMO/Al2O3) heterostructure was grown using chemical solution deposition (CSD) technique. X–ray diffraction (XRD) measurement confirms the (100) crystallographic oriented growth of LSMO manganite and polycrystalline hexagonal growth of ZnO layer. Temperature dependent resistivity behavior under different applied magnetic fields, performed for two different geometries [current in plane (CIP) and current perpendicular to plane (CPP)], suggests the modifications in metal to insulator transition temperature TP and alterations in resistivity with applied magnetic field and measurement geometry. This also implies the better charge conduction across the ZnO/LSMO interface (CPP) as compared to LSMO film layer (CIP). Various charge conduction mechanisms have been employed to understand the charge transport for observed low temperature resistivity minimum, metallic behavior, insulating state and magnetoresistance (MR) of LSMO film and ZnO/LSMO interface. Electroresistance (ER) and field effect configuration (FEC) have been investigated across the ZnO/LSMO interface by recording the LSMO channel resistance, at room temperature, that suggest the tuning of signature and value of ER by considering forward and reverse bias modes across the interface. FEC studies show the large ER ∼ +750% and −85% across the LSMO channel with its tunability under different interface biases.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Magnetic and transport properties of perovskites and related materials · Advanced Condensed Matter Physics · ZnO doping and properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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