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Updated: Feb 28, 2026

Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material
Published on: December 20, 2024
Materials informatics for glass ionomer cements: key features and design
Kenta Tsuchiya1, Atsushi Tomokiyo2, Lauren Takahashi3
1Department of Restorative Dentistry, Hokkaido University, Sapporo, Japan; Dental Biomaterials and Minimally Invasive Dentistry, Departamento de Odontología, Facultad de Ciencias de la Salud, Universidad CEU-Cardenal Herrera, Valencia, Spain.
Objectives:
The objective of this study was to investigate additive-dependent trends in the physicochemical properties of experimental glass ionomer cements (GICs), using an exploratory materials informatics framework as a data-analytical tool.
Methods:
This study used a data-driven approach to analyze experimental GICs modified with fluoride-doped Ca-phosphates (FDCP) or Zn-polycarboxylate bioactive glass (BAG-Zn), compared with commercial Fuji™ IX GP (IX-GP) and Fuji™ II LC (II-LC). The independent variables were material type, particle type, and content (0-20 wt%). pH, ion release, and compressive strength were evaluated at 1, 7, and 28 days. Descriptive analysis and data visualization approaches such as pairwise correlation, parallel coordinate, and clustering analyses were applied to a previously published dataset.
Results:
Pairwise correlation analyses revealed BAG-Zn increased both pH and F⁻ release, whereas FDCP reduced pH but enhanced Ca²⁺ release. The effects of additives varied with base materials-IX-GP tended to lower pH and raise Ca²⁺ release, while II-LC increased pH and compressive strength. Parallel coordinate analyses confirmed that additive type, base material, and mixing ratio were major contributing factors of ion release and strength. Clustering further identified 5-10 wt% BAG-Zn as the optimal composition, with FDCP maintaining favorable mechanical properties even at higher levels.
Significance:
The present materials informatics approach enabled the identification of key variables and additive-dependent trends in experimental GICs. Even with a limited dataset, this exploratory framework provided structured, data-driven insights that may support future experimental design and systematic data expansion toward the development of improved GIC formulations.
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