Engineered carbonaceous nanomaterials (ECNs), including single-wall carbon nanotubes (SWCNTs), multiwall carbon nanotubes (MWCNTs), graphene, and graphene oxide (GO), are potentially hazardous to the lung. With incremental experience in the use of predictive toxicological approaches, seeking to relate ECN physicochemical properties to adverse outcome pathways (AOPs), it is logical to explore the existence of a common AOP that allows comparative analysis of broad ECN categories. We established an ECN library comprising three different types of SWCNTs, graphene, and graphene oxide (two sizes) for comparative analysis according to a cell-based AOP that also plays a role in the pathogenesis of pulmonary fibrosis. SWCNTs synthesized by Hipco, arc discharge and Co-Mo catalyst (CoMoCAT) methods were obtained in their as-prepared (AP) state, following which they were further purified (PD) or coated with Pluronic F108 (PF108) or bovine serum albumin (BSA) to improve dispersal and colloidal stability. GO was prepared as two sizes, GO-small (S) and GO-large (L), while the graphene samples were coated with BSA and PF108 to enable dispersion in aqueous solution. In vitro screening showed that AP- and PD-SWCNTs, irrespective of the method of synthesis, as well as graphene (BSA) and GO (S and L) could trigger interleukin-1β (IL-1β) and transforming growth factor-β1 (TGF-β1) production in myeloid (THP-1) and epithelial (BEAS-2B) cell lines, respectively. Oropharyngeal aspiration in mice confirmed that AP-Hipco tubes, graphene (BSA-dispersed), GO-S and GO-L could induce IL-1β and TGF-β1 production in the lung in parallel with lung fibrosis. Notably, GO-L was the most pro-fibrogenic material based on rapid kinetics of pulmonary injury. In contrast, PF108-dispersed SWCNTs and -graphene failed to exert fibrogenic effects. Collectively, these data indicate that the dispersal state and surface reactivity of ECNs play key roles in triggering a pro-fibrogenic AOP, which could prove helpful for hazard ranking and a proposed tiered testing approach for large ECN categories.