Choosing prior for the regression coefficients in the logistic regression

I'm trying to model Bayesian logistic regression model (using JAGS in R) with my dependent variable (status: 0=alive, 1 death) & independent variable (age) in the categorical form (0=patients < 65 yrs old, 1=patients >=65 yrs old). I'm using normal dist. as a prior for constant term, b0 and Dirichlet dist. as the prior for the regression coefficient,b1. However, i got the error of incorrect number of parameters in distribution ddirch. Am i using the correct prior for b1? or should i just assigned normal prior for it? Below are the codes i used:

# Bayesian logistic model for JAGS #
for( i in 1 : N ) {
status ~ dbern(mu)
mu<-1/(1+exp(-(b0 + b1*age )))
# Prior on constant term, b0
b0 ~ dnorm(0, 1.0E-4)
# Prior on regression coefficient, b1
b1 ~ ddirch(1,1)


Less is more. Stay pure. Stay poor.
I will point out that I am not an expert in this area at all. I ran a similar model earlier in the year and most literature pointed to using normal priors in Bayesian logistic regression. Is your prior going to be flat, if so why not use normal priors?

Also, for my own education, why are you opting to use Dirch, and how does it translate into a log odds value.