Should multiple testing correct with bonferroni ever reduce a p value's size?

I just did a series of 40 t-tests and then proceeded to use bonferroni correction for multiple testing and my P values reduced in size. Why does this happen? I was my impression that multiple tests correction would always result in an increase in p value size.

Before correction:

           substrate              p_value
19           glycan 0.000139091904182433
23 dermatan sulfate 0.000139091904182433
4            chitin 0.000294435140367691
22           xylose  0.00387472305660014
5       beta-glucan  0.00552400891530821
2         cellulose   0.0130881279666714

After correction:

          substrate      p_value
10           glucose 5.110415e-21
29          fructose 1.745709e-20
26            lignin 7.090204e-18
30 cyclomaltodextrin 3.569263e-10
31  lacto-N-tetraose 3.569263e-10
32       hyaluronate 3.569263e-10

Code used to generate the P values:

# loop labeling one substrate as "A" and every other substrate as "B" then doing a T test between then counts
sub_pvals = NULL
for(sub in unique(cazy_cata_melt$Substrate)){
  df= cazy_cata_melt
  df[df$Substrate != sub,]$Substrate = "B"
  df[df$Substrate == sub,]$Substrate = "A"
  input = cbind(substrate = sub, p_value = t.test(value ~ Substrate, data = df)[[3]][1])
  sub_pvals = rbind.data.frame(sub_pvals, input)
}

#correction for multiple testing
    sub_pvals$p_value = p.adjust(sub_pvals$p_value, method = "bonferroni", n = length(unique(cazy_cata_melt$Substrate)))

#ordering the dataframe
sub_pvals = sub_pvals[order(sub_pvals$p_value),]

Data available here: https://pastebin.com/vsbYGkQW