Shery Chang1,Inga Kuschnerus1,Yee Yee Khine1,Haotian Wen1
University of New South Wales1
Shery Chang1,Inga Kuschnerus1,Yee Yee Khine1,Haotian Wen1
University of New South Wales1
Contradicting the current opinion that 3-5nm detonation nanodiamonds (DNDs) are monodispersed after their purification, more recent work indicates that even with further surface modifications, DNDs form lacey networks in water in the size range of approximately 100nm. This has significant implications for the use of DNDs in biomedicine, where control of the aggregation behavior is critical to their performance. However, ensuring reliable colloidal stability of DNDs in in-vitro environments is not trivial. This is because any type of nanoparticle surfaces rapidly adsorb macromolecules such as proteins and lipids when being introduced to biological media (e.g. blood or cell culture media containing serum). Additionally, the dynamic environment of high salt concentrations, presence of electrolytes and shifting pH, can result in colloidal destabilization, nanoparticle aggregation, loss of stabilizing ligands on the nanoparticles’ surface and nanoparticle dissolution. Hence, neglecting the characterization of DNDs in biologically relevant buffers may lead to inconsistencies and irreproducibility in data.<br/>In this work we present a systematic study on DNDs with different surface functionalization (-COOH, -OH, -H, -PG) dispersed in buffer with varying pH and saline concentrations using high performance characterization techniques such as small angle X-ray scattering (SAXS) and a new approach of combining machine learning (ML) with cryo-transmission electron microcopy (cryo-TEM) for a quantitative image analysis to get an accurate insight on the performance of DNDs. Our results indicate that depending on the surface functionalization the colloidal behavior and aggregation formation significantly changes. With the help of ML and cryo-TEM we are able to generate a data bank of different scenarios of DNDs behavior in biologically relevant environments, which is a significant aid in creating safer and more efficient DNDs for biomedical applications.