Surface functionalization with organic groups involves modifying the surface of nanomaterials by introducing organic molecules such as carboxyl, amine, thiol, or polymer chains. This process can be achieved through covalent or non-covalent bonding, using linker molecules to attach organic functionalities to nanoparticle surfaces. The primary goals are to enhance stability against aggregation and oxidation, improve solubility and dispersibility, and impart specific chemical properties such as biocompatibility or targeting ability.
Organic functional groups play a crucial role in tuning the interactions of nanomaterials with their environment, allowing for applications in drug delivery, bioimaging, catalysis, and environmental remediation. For example, attaching polyethylene glycol (PEG) improves biocompatibility, while other organic ligands enable selective binding of biomolecules or heavy metals. Surface functionalization also helps control the toxicity, cellular uptake, and biodistribution of nanomaterials, which is vital for biomedical uses.
There are two main strategies: in situ modification, where functionalization occurs during synthesis, and post-synthesis modification, where pre-formed nanomaterials are later functionalized. The choice of organic group and functionalization method is driven by the intended application, enabling highly versatile and multifunctional nanomaterials for a variety of scientific and industrial challenges.
In case of multiple functionalizations, populations of 5000 structures are generated for each system with the –OH and –COOH added randomly to the specific surface sites. To choose a representative structure for each group/concentration, first, using the OpenBabel software, the extended connectivity fingerprints (ECFP) with bond diameter four (ECFP4) were generated for each structure. ECFP4 was selected as it is currently one of the most used for similarity searching. Then, the entropy of the binary fingerprint was calculated using the BiEntropy function. This function is capable to identify order and disorder in binary strings. Finally, the most common structure was selected from the entropy distribution.
Some scripts can be downloaded from the LaModel Github.