Currently, breast cancer is considered as a health problem worldwide. Furthermore, current treatments neither are capable of stopping its propagation and/or recurrence nor are specific for cancer cells. Therefore, side effects on healthy tissues and cells are common. An increase in the efficiency of treatments, along with a reduction in their toxicity, is desirable to improve the life quality of patients affected by breast cancer. Nanotechnology offers new alternatives for the design and synthesis of nanomaterials that can be used in the identification, diagnosis, and treatment of cancer and has now become a very promising tool for its use against this disease. Among the wide variety of nanomaterials, the scientific community is particularly interested in carbon nanomaterials (fullerenes, nanotubes, and graphene) due to their physical properties, versatile chemical functionalization, and biocompatibility. Recent scientific evidence shows the potential uses of carbon nanomaterials as therapeutic agents, systems for selective and controlled drug release, and contrast agents for diagnosing and locating tumors. This generates new possibilities for the development of innovative systems to treat breast cancer and can be used to detect this disease at much earlier stages. Thus, applications of carbon nanomaterials in breast cancer treatment are discussed in this article.

The mechanical properties of cubic (c-BN) and wurtzite (w-BN) boron nitride structures were investigated and compared using density functional theory (DFT) with several exchange–correlation functionals. This research focuses on determining the bulk modulus ($\mathit{B}$) and, for the first time, the stretching bond force constants ($k_r$), two fundamental parameters that describe the intrinsic stiffness and elastic resistance of these BN structures. Despite their structural similarity with the same tetrahedral coordination between atoms, c-BN and w-BN exhibit subtle differences in bond strength and compressibility that have not been fully clarified at the atomistic level. By systematically analyzing the influence of hybrid and semi-local functionals, consistent relationship between structural configuration and the predicted $\mathit{B}$ and $k_r$ values of both c-BN and w-BN structures were established and compared. These findings not only validate DFT as a reliable approach for assessing the mechanical properties of BN polymorphs, but also offer key parameters for machine learning and advanced multiscale modeling. Therefore, this theoretical study contributes to understanding the origin of mechanical properties in BN structures and supports their design in applications where a particular hardness and stability are required.