AI-based nanotopography of dental adhesives containing TiO₂ nanoparticles with enhanced antibacterial and mechanical properties
Authors/Creators
Description
Highlights of the Study
This study presents an integrated experimental, computational, and artificial intelligence (AI)-based approach to develop and characterize dental adhesives reinforced with silanized TiO₂ nanoparticles (NPs). By systematically varying TiO₂ NP concentrations from 0.1 to 1 wt%, the study demonstrates substantial improvements in mechanical performance, bonding strength, antibacterial activity, and surface characterization, while highlighting the importance of nanoparticle concentration and dispersion in optimizing dental adhesive performance.
Key findings:
🟢 Flexural strength enhancement: Incorporating TiO₂ NPs progressively increased flexural strength, reaching a maximum of 37.36 MPa at 1 wt%, corresponding to a 2.6-fold improvement compared with the unmodified adhesive.
🟢 Flexural modulus optimization: The flexural modulus increased by approximately 33% at 0.3 wt% TiO₂ NPs, demonstrating effective load transfer between the polymer matrix and nanoparticles at lower concentrations, followed by a decline at higher NP contents.
🟢 Diametral tensile strength: DTS reached a maximum of 110.3 MPa at 0.3 wt% TiO₂ NPs, representing approximately a 6% improvement over the base adhesive. Further increases in NP concentration resulted in a substantial reduction in DTS, primarily associated with nanoparticle agglomeration.
🟢 Microshear bond strength: Bond strength increased with TiO₂ NP concentration and reached a maximum of 24.9 MPa at 0.7 wt%, approximately 2.2 times higher than the unmodified adhesive, before declining at higher nanoparticle concentrations.
🟢 Antibacterial performance: TiO₂-containing adhesives demonstrated antibacterial activity against Streptococcus mutans, with the most effective bacterial inhibition observed at approximately 0.3–0.5 wt% TiO₂ NPs.
🟢 Nanoparticle dispersion and interface: SEM and elemental mapping demonstrated relatively uniform nanoparticle distribution and good contact between TiO₂ NPs and the adhesive matrix, supporting the effectiveness of nanoparticle silanization and strong matrix–particle interactions.
🟢 Surface nanotopography: SPIP™ analysis demonstrated concentration-dependent changes in surface morphology, with experimental roughness increasing from 0.29 nm for the unmodified adhesive to 0.52 nm at 1 wt% TiO₂, while waviness increased from 28.29 to 105 pm.
🟢 AI-based surface prediction: Gaussian Process Regression (GPR) successfully predicted surface roughness and waviness across different TiO₂ concentrations. Predicted roughness ranged from 0.20 to 0.46 nm and waviness from 25.07 to 98 pm, closely following the experimental SPIP™ trends.
🟢 Multiscale modeling: Computational analysis demonstrated that nanoparticle agglomeration generated substantially higher localized stress concentrations than uniform NP dispersion, emphasizing nanoparticle distribution as a critical parameter controlling crack formation and mechanical performance.
🟢 Optimal multifunctional performance: The results demonstrate that TiO₂ NP concentration must be carefully optimized because different properties reach their maximum performance at different concentrations, providing a strategy for balancing mechanical strength, adhesion, surface characteristics, and antibacterial activity.
Overall, the study demonstrates that silanized TiO₂ nanoparticles can transform conventional dental adhesives into multifunctional nanocomposites with substantially enhanced mechanical, bonding, and antibacterial performance. The integration of experimental characterization, multiscale modeling, and AI-based nanotopography prediction provides a powerful framework for the rational design and optimization of next-generation dental adhesive systems.
KEYWORDS
Dental Adhesive • Titanium Dioxide Nanoparticles • Surface Modification • Mechanical Properties • Antibacterial Features • Gaussian Process Regression • Nanotopography • Microshear Bond Strength • Streptococcus mutans
HOW TO CITE
Khakbiz, M., Pouyadel, H., & Davari, N. (2025). AI-based nanotopography of dental adhesives containing TiO₂ nanoparticles with enhanced antibacterial and mechanical properties. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 726, 137722.
DOI: 10.1016/j.colsurfa.2025.137722
Publisher: Elsevier
Abstract
Abstract
This study explores the improvement of mechanical and antibacterial properties of dental adhesives through the incorporation of silanized titanium dioxide (TiO₂) nanoparticles (NPs). Adhesives containing 0.1–1 wt% TiO₂ NPs were fabricated and characterized using SEM, EDAX, FTIR, and XRD. The highest flexural strength (37.36 MPa) was observed at 1 wt%, representing a 2.6-fold increase compared to the control. The flexural modulus increased by 33 % at 0.3 wt%, followed by a slight decline at higher concentrations. Diametral tensile strength (DTS) peaked at 110.3 MPa for 0.3 wt% NPs, marking a 6 % improvement over the base adhesive, before decreasing by 31 % due to nanoparticle agglomeration. Microshear bond strength reached a maximum of 24.9 MPa at 0.7 wt%, which is 2.2 times higher than the unmodified sample. Antibacterial performance was assessed using disk diffusion and pour plate methods, with the most effective bacterial inhibition observed at 0.3–0.5 wt% NPs.
Additionally, Gaussian process regression (GPR) modeling accurately predicted surface roughness, waviness, and isotropy, aligning well with experimental SPIP™ software results. These findings demonstrate that TiO₂ NP incorporation, supported by machine learning, can effectively enhance both the structural integrity and antibacterial efficacy of dental adhesives.