The effect of micro-mechanical signatures of constituent phases in modern dental restorative materials on their macro-mechanical property: A statistical nanoindentation approach.

Lien, Wen; Yi, Minju D; Jones, Shauna D; Wentworth, Carolina V; Savett, Daniel A; Mansell, Michael R; Vandewalle, Kraig S · J Mech Behav Biomed Mater · 2021

basic_science · Level V

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Abstract

This study utilized a statistical nanoindentation analysis technique (SNT) to measure the amount of organic and inorganic constituents of twenty different brands of dental resin-based composites (RBCs) and tested whether their macro-property such as flexural modulus could be approximated by the proportions of constituents' micromechanical signatures using various rules of mixtures. The probability density function (PDF) of constitutive moduli per RBC brand were measured for three groups, comprised of different indent arrays and inter-indent spacings. SNT was then applied to deconvolute each PDF, from which the effective filler (μ<sup>F</sup>) and matrix (μ<sup>M</sup>) moduli and filler (V<sup>F</sup>) and matrix (V<sup>M</sup>) volume fractions per RBC brand were computed. V<sup>F</sup> and V<sup>M</sup> values obtained via SNT were strongly correlated with V<sup>F</sup> and V<sup>M</sup> obtained via Thermogravimetric Analysis and Archimedes method. The "observed" flexural modulus (E<sub>c</sub><sup>FS</sup>) measured under macro-experiment were well associated with "predicted" effective modulus (E<sub>c</sub><sup>Eff</sup>) measured under nano-experiment, thereby establishing that global modulus was strongly affected by the constituents' micromechanics. However, the "predicted" E<sub>c</sub><sup>Eff</sup> were proportionally higher than the "observed" E<sub>c</sub><sup>FS</sup>. V<sup>F</sup> was a confounder to E<sub>c</sub><sup>FS</sup> and E<sub>c</sub><sup>Eff</sup>, whereby the influence of V<sup>F</sup> on both modular ratios (E<sub>c</sub><sup>FS</sup>/μ<sup>M</sup> and E<sub>c</sub><sup>Eff</sup>/μ<sup>M</sup>) was best modeled by an exponential regression.

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