For a graph $G$, let its Laplacian be $\Delta = I - D^{-1/2}AD^{-1/2}$, where $A$ is the adjacency matrix, $I$ is the identity matrix and $D$ is the diagonal matrix with vertex degrees. I'm interested in the spectral gap of $G$, i.e. the first nonzero eigenvalue of $\Delta$, denoted by $\lambda_{1}(G)$.

Is it true that a randomly chosen (with uniform distribution) $d$-regular bipartite graph on $(n, n)$ vertices (with multiple edges allowed) has, with probability approaching $1$ as $n \to \infty$, $\lambda_1$ arbitrarily close to $1$ (i.e. we can make arbitrarily close by taking $d$ large enough)?

If yes, is there a reference for this fact?

Proofs of expanding properties for random regular graphs which I have found in the literature usually give the probability only bounded from below by a constant, i. e. $1/2$, although I imagine that actually almost all random graphs have good spectral gap.

Note: by $d$-regular bipartite graph I mean a graph in which each vertex (on the left and on the right) has degree $d$.

The definition of "Laplacian" in this OP seems very unusual to me; also, sincediagonal matrices of course commute with any other matrix, we can simplify the definitionin the OP: $I-D^{-1/2}AD^{-1/2}=I-D^{-1/2}D^{-1/2}A=I-D^{-1}A$. Hence, the OP has $D\cdot\Delta = D - A$, whereas the most usual definition is $\mathrm{Laplacian}(G)=D-A$, cf. e.g. Zoran Stanić:Inequalities for Graph Eigenvalues. LMS LNS 423, p. 12. The definition in the OP seems to be misleading. I won't touch it, out of respect and doubt. Would you please say if and why you intend to use this definition? $\endgroup$