Interactions of Common Biological Buffers with Iron Oxide Nanoparticles.

Iron oxide nanoparticles (IONPs) have been studied extensively for biomedical applications, which require that they be aqueous-stable at physiological pH. The structures of some of these buffers, however, may also allow for binding to surface iron, thus potentially exchanging with functionally relevant ligands, and altering the desired properties of the nanoparticles. We report here on the interactions of five common biologically relevant buffers (MES, MOPS, phosphate, HEPES, and Tris) with iron oxide nanoparticles through spectroscopic studies. The IONPs in this study are capped with 3,4-dihydroxybenzoic acid (3,4-DHBA) to serve as models for IONP functionalized with catechol ligands. Unlike previous studies, which relied exclusively on dynamic light scattering (DLS) and ζ-potential measurements to characterize buffer interactions with IONPs, we use Fourier transform infrared (FTIR) and ultraviolet-visible (UV-visible) spectroscopic techniques to characterize the IONP surface to demonstrate binding of buffers and etching of the IONP surface. Our findings establish that phosphate and Tris bind to the IONP surface, even in the presence of strongly bound catechol ligands. We further observe significant etching of IONPs in Tris buffer, with the release of surface Fe into solution. Minor etching is noted in HEPES, and to a lesser degree, in MOPS, while no etching is observed in MES. Our findings suggest that, while morpholino buffers, such as MES and MOPS, may be more appropriate for use with IONPs, proper buffer selection should always be considered on a case-by-case basis.

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.