In thomasgredig/quantumPPMS: Analzye Quantum Design PPMS data sets

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4
)

library(quantumPPMS)

No diamagnetic correction

The coercivity can be estimated from the $M$ versus $H$ curves. Here is an example to retrieve the coercive field values; note that in this example, the diamagnetic background has not been removed, so the result should be interpreted accordingly

filename = vsm.getSampleFiles()[1]
d = vsm.import(filename)
d1 = vsm.getLoop(d, lp=1, direction=1 )
df = vsm.data.frame(d1)
Hc = vsm.get.Coercivity(df$H, df$M)
print(paste("Coercivity is ", signif(Hc,3),"Oe."))

Let us check whether the value agrees visually:

plot(d1)
plot(df$H, df$M, xlim=c(-(2*Hc),+(2*Hc)), pch=19, col='blue')
lines(df$H, df$M)
abline(h = 0, v=Hc, col='red')

With diamagnetic correction

filename = vsm.getSampleFiles()[1]
d = vsm.import(filename)
dStats = vsm.hystStats(d)
t(dStats)
print(paste("Coercivity is ", signif(dStats$Hc,3),"Oe."))
Hc = dStats$Hc

Let us check whether the value agrees visually:

df = vsm.data.frame(d)
mean(dStats$Susceptibility) -> slope
plot(df$H, df$M - slope*df$H)
lines(df$H, df$M)
plot(df$H, df$M - slope*df$H, xlim=c(-(4*Hc[1]),+(4*Hc[1])), pch=19, col='blue')
lines(df$H, df$M  - slope*df$H, col='blue')
abline(h = 0, v=Hc, col='red')