Functional Monomer Type Determines the Interfacial Properties of Experimental Self-Adhesive Composites Bonded to Dentin

PURPOSE: To evaluate the influence of functional monomer type and powder-to-liquid ratio on the interfacial properties and degree of conversion of experimental self-adhesive flowable resin composites (SAFRCs) bonded to dentin. MATERIALS AND METHODS: Nine experimental SAFRC formulations were developed by varying the powder-to-liquid ratio (1.9, 2.2, 2.5) and the functional monomers included (10-MDP, GPDM, HEMA). Human molars (n = 27) were restored using each formulation and analyzed using a rheometer to assess viscosity, Raman micro-spectroscopy for inter-diffusion zone (IDZ) width, and degree of conversion at the interface (DC%). Two-way ANOVA and post-hoc tests were performed for statistical analysis (α = 0.05). RESULTS: Rheological testing revealed, as expected, non-Newtonian flow behavior in all composites, with significant effects of both powder-to-liquid ratio (P 0.001) and monomer type (P 0.001) on viscosity. 10-MDP composites exhibited optimal viscosity (1.12-2.86 mPa·s) across all ratios, significantly lower than GPDM and HEMA. Raman mapping showed a distinct IDZ with hybrid characteristics for 10-MDP formulations, contrasting with abrupt transitions or gaps in GPDM and HEMA groups. IDZ width was significantly greater in 10-MDP formulations (P 0.0001). The DC% at the interface was highest for 10-MDP and HEMA formulations, exceeding 68%, while GPDM composites showed lower values (P 0.001). CONCLUSION: Functional monomer type critically affects the interfacial bonding performance and conversion rate of SAFRCs, with 10-MDP outperforming GPDM and HEMA in interdiffusion and adhesive quality. Variations in powder-to-liquid ratio influenced viscosity but had a limited impact on interfacial performance. Optimized formulations with 10-MDP may enhance the clinical efficacy of SAFRCs. CLINICAL RELEVANCE STATEMENT: Refining acidic-monomer chemistry and viscosity in SAFRCs could improve their bonding predictability.