Suppose $f$ is strongly convex and twice differentiable on some interval $I$:
(1) $\forall x,y \in I: \forall t \in (0,1) \implies f(tx + (1-t)y)<tf(x)+(1-t)f(y)$
(2) $\forall x \in I \implies f''(x) \in \mathbb{R}$
My question is what exactly can we say about $f''$ on $I$? For example obviously, $f'' \geq 0$, but there's more to it for the values of $x$ when $f''(x) = 0$. For example, $f(x) = x$ is definitely not strongly convex on any interval, but $f'' \geq 0: \forall x \in \mathbb{R}$.
I think a sufficient condition would be if $f''(z) = 0 \implies \forall \delta > 0: \exists y_1,y_2 \in (z-\delta,z+\delta):y_1<z<y_2$ and $f''(y_1) > 0 < f''(y_2)$. Or in other words, $f$ is not linear at $z$. This, combined with $f'' \geq 0$ on $I$ should ensure strong convexity.
I would like to clarify my understanding by knowing a near-miss example(s). For example, what if $f$ is a function such that is convex on $\mathbb{R}$ but, say, $f''(0) = 0$ and $\forall \delta > 0: \exists x_1, x_2 \in (-\delta, \delta): x_1 < 0 < x_2:f''(x_1)=0=f''(x_2)?$ In my head I see something that looks kind of like $f''(x) = \sin(\frac{1}{x}) + 1$, but I would like an example where I can express $f$ in elementary functions if possible?