journal article·2024-06Open access

Enhancing bonding of zirconia lithium silicate ceramics to dentin: The effect of surface treatment and resin cement

Lama W. Yousef, Mirza R. Baig, Muawia A. Qudeimat, Jukka P. Matinlinna, Mohammad Al-Shammari

Abstract

This laboratory study evaluated the influence of surface treatments on surface topography, and the bonding of CAD/CAM (computer-aided design computer-aided manufacturing) zirconia-containing lithium silicate (ZLS) surfaces to human dentin using various contemporary resin cements. Specimens from ZLS CAD/CAM blocks (Celtra™ Duo and Vita™ Suprinity) underwent three surface pretreatments: grinding only; 5 % hydrofluoric acid etching followed by application of a silane coupling agent, and a self-etching ceramic primer. The specimens were bonded to a flat exposed dentin surface using two etch-and-rinse and two self-adhesive resin cements, and then sectioned into microspecimens for microtensile bond strength (μTBS) testing. Failure modes of bonded surfaces, and ceramic surface roughness and topography were assessed. Results indicated that surface treatment was essential for ZLS-dentin bonding. Hydrofluoric acid and silane, along with the self-etching primer resulted in comparable strengths and outperformed the control group. Etch-and-rinse cements yielded significantly higher μTBS than self-adhesive cements. Significant differences in surface roughness and topography were observed among the treatment groups. Hydrofluoric acid exhibited the highest roughness parameters and most prominent surface changes. These findings highlight the importance of surface treatment for enhancing zirconia-containing lithium silicate bonding to dentin, and emphasize the critical role of the resin cement choice in optimizing dental restorative outcomes.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Dental materials and restorations · Dental Research and COVID-19 · Dental Implant Techniques and Outcomes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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