Hydrothermal Aging, Thickness, and Shade as Determinants of the Optical Performance of CAD/CAM Ultra-Translucent Multilayer Zirconia Veneers

Abstract

Background: Ultra-translucent multilayer zirconia is increasingly used for minimally invasive esthetic restorations, but the influence of hydrothermal aging on the translucency of veneers of different shades and thicknesses, and its possible clinical relevance, remain insufficiently investigated. Methods: Twenty-seven CAD/CAM ultra-translucent multilayer zirconia veneers (KATANA™ UTML) were prepared in three shades (A1, A2, A3) and three thicknesses (0.5, 0.8, 1.0 mm). The translucency parameter (TP) was calculated from CIELAB coordinates measured on white and black backgrounds before and after hydrothermal aging (ISO ISO 6474-2:2019; 134 °C, 2 MPa, 15 h). Because the measurements are retained only as group values and individual specimen data are no longer retrievable, the results are reported descriptively, without inferential statistics; the absolute change (ΔTP) was compared, as an approximate indicative reference, with published perceptibility (0.62) and acceptability (2.62) threshold values. Results: Mean TP decreased as veneer thickness increased, in every shade and at both time points, and was highest for A1 and lowest for A3 at 0.5 and 0.8 mm. Mean TP was lower after aging in all nine groups, but the size of the change differed markedly between thickness–shade combinations, from 0.05 (1.0 mm A1) to 5.30 (1.0 mm A3). Mean ΔTP exceeded the acceptability reference value in three groups, all of them 0.8 mm or thicker, and remained below the perceptibility reference value in two. Conclusions: Within the limits of this descriptive single-material study, veneer thickness was the characteristic most closely associated with translucency, while the aging-related change varied with the thickness–shade combination rather than being uniform. These are observational, material-specific findings that require confirmation in a study with specimen-level data and microstructural characterization.

The authors' abstract, as published at the source. Applied Sciences, 2026 · DOI ↗