Grain size determination of superconducting MgB2 powders from magnetization curve, image analysis and surface area measurement

TL;DR

This paper presents a novel method to determine the average grain size of superconducting MgB2 powders using magnetization curves from SQUID magnetometry, validated against SEM and BET techniques, with an emphasis on the method's accuracy and automation.

Contribution

It introduces a magnetic measurement-based approach for grain size evaluation and develops a MATLAB routine for automated SEM image analysis, enhancing accuracy and efficiency.

Findings

Magnetization measurements can reliably estimate average grain size.

SEM and BET techniques provide detailed grain size distribution information.

The developed MATLAB routine automates SEM image analysis effectively.

Abstract

The present article reports a method for the average grain size evaluation of superconducting nano-particles through their magnetic properties. The use of SQUID magnetometry to determine the average MgB2 particle size was investigated and the results compared with those achieved through other techniques. In particular the data obtained from zero field cooled magnetization measurement as function of the temperature were compared with the results obtained by scanning electron microscopy and Brunauer-Emmett-Teller techniques. The particle magnetization was measured by a commercial SQUID magnetometer in magnetic field (1 mT) and temperatures ranging from 5 to 50 K dispersing the powders in a grease medium. The grain size is obtained by fitting the data taking into account the Ginzburg-Landau temperature dependence of the London penetration depth. Variations on typical modeling parameters were explored in order to gain a better picture of the average grain size and the effectiveness of various measurement techniques. We find that it is possible to use the magnetization measurements to determine the average grain size even if the SEM image analysis allows extracting more information about the grain size distribution. Furthermore a Matlab routine has been developed in order to get automatic analysis of SEM images.