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