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Effect of Annealing Temperature on the Structural and the Electrical Transport Properties of La 2 NiMnO 6 Nanoparticles
Author(s) -
Chakraborty Deblina,
Nandi Upendranath,
Dey Animesh Kumar,
Dasgupta Papri,
Poddar Asok,
Jana Debnarayan
Publication year - 2018
Publication title -
physica status solidi (b)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.51
H-Index - 109
eISSN - 1521-3951
pISSN - 0370-1972
DOI - 10.1002/pssb.201700436
Subject(s) - annealing (glass) , grain boundary , condensed matter physics , materials science , grain size , ohmic contact , thermal conduction , electrical resistivity and conductivity , conductance , exponent , nanotechnology , microstructure , composite material , physics , layer (electronics) , quantum mechanics , linguistics , philosophy
The effect of annealing temperatures on the structural and the electrical transport properties of La 2 NiMnO 6 nanoparticles is investigated in detail. The grain size, grain boundary and the antisite disorder (ASD) are affected by the annealing temperature during the preparative conditions of annealing. The HRTEM image indicates that the average grain size increases with annealing temperature providing a platform for explanation of the transport data. Both I − V andd I d V − V measurements show that the electrical conduction is non‐Ohmic and characterized by a voltageV 0( A T ) , known as the onset voltage, which scales with the Ohmic conductanceΣ 0( A T )asV 0( A T ) ∼ Σ 0( T )x A T,x A Tbeing the onset exponent having negative values in both cases. A T refers to the fact thatΣ 0( A T )is changed by the annealing temperature (AT).V 0( A T )depends strongly on the grain size and grain boundaries and decreases with the increase in annealing temperature. The negative value ofx A Tis physically understood from the concept of inter‐granular tunneling effect. This non‐Ohmic conduction, onset voltageV 0( A T )and onset exponentx A Tare qualitatively explained by considering the conduction through both the ordered and disordered regions and by applying Glazman–Matveev model. The transport data are systematically analyzed within the framework of scaling formalism.