it is not clear what you really want to know. When you have a relationship between x and y and s is a suppressor (hence it is uncorrelated with y) then
a) you will get a partial correlation between s and y when you control x
b) the partial effect of x (controlling for s) will be higher as the unconditional correlation/relationship
You can simulate this easily in R
S = rnorm(1000)
C = rnorm(1000)
X = .8*S + .6*C+ rnorm(1000)
Y = .7*C + rnorm(1000)
df = data.frame(X,S,Y)
summary(lm(Y~ X, data=df)) # Effect of X
summary(lm(Y~ S, data=df)) # No effect of S
summary(lm(Y ~ X + S, data=df)) #Suppression
Note that the most probable scenario underlying suppression (that I used for the simulation) is that the X-effect is confounded (in this case, by C) and controlling for X opens a spurious path from S to Y, see
Kim, Y. (2019). The causal structure of suppressor variables. Journal of Educational and Behavioral Statistics, 1076998619825679. https://doi.org/10.3102/1076998619825679
The way I would put it is that the zero order correlation between X and Y is not statistically significant, but that relationship becomes significant after controlling for the suppressor variable.
This is a basic feature of partial regression coefficients, so you should not need a citation for it. But if you want one, you can do a Google search for: suppressor variable in regression. And if you limit that search to Books, you will get references to several statistical texts that cover suppression.