Graphene-based materials have been shown to have advantageous properties in biomedical and dental applications due to their high mechanical, physiochemical, antibacterial, and stem cell differentiating properties. Although graphene-based materials have displayed appropriate biocompatible properties when used in implant materials for orthopedic applications, little research has been performed to specifically test the biocompatibility of graphene for dental applications. The oral environment, compared to the body, varies greatly and must be considered when evaluating biocompatibility requirements for dental applications. This review will discuss in vitro and in vivo studies that assess graphene’s cytotoxicity, antibacterial properties, and cell differentiation ability to evaluate the overall biocompatibility of graphene-based materials for dental applications. Particle shape, size, and concentration were found to be major factors that affected overall biocompatibility of graphene.

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The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems. ADAM is able to successfully optimize nuclear systems using the gradients of k-eigenvalue problems despite their stochastic nature and uncertainty. Furthermore, it is clearly demonstrated that low-compute time, high-variance estimates of the gradient lead to better performance in the optimization challenge problems tested here.

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