You could forget the difference between days 1&2, and instead just look at binary variables defined as "does the measured concentration increase". (In the observed data, true for all 3 subjects in the treatment group and false for all subjects in the control group). 

If the null hypothesis is indeed "there is no increase in the parameter", there is no need for p-values, the data is logically inconsistent with this hypothesis ($p=0$)! However, in this case, the control group would be useless. Presumably, we have the control group because we are worried that the increases in concentration might be caused by something completely else than your treatment. To address this, a better formulation of the null hypothesis would be "probability of increasing concentration under treatment $\leq$ probability of increasing concentration in the control". Then, perform some test on the difference of proportions (by e.g [Fisher's exact test ][1]).