The use of natural compounds for preparing hybrid molecular films-such as surface coatings made from metal-phenolic networks (MPNs)-is of interest in areas ranging from catalysis and separations to biomedicine. However, to date, the film growth of MPNs has been observed to proceed in discrete steps (≈10 nm per step) where the coordination-driven interfacial assembly ceases beyond a finite time (≈1 min). Here, it is demonstrated that the assembly process for MPNs can be modulated from discrete to continuous by utilizing solid-state reactants (i.e., rusted iron objects). Gallic acid etches iron from rust and produces chelate complexes in solution that continuously assemble at the interface of solid substrates dispersed in the system. The result is stable, continuous growth of MPN films. The presented double dynamic process-that is, etching and self-assembly-provides new insights into the chemistry of MPN assembly while enabling control over the MPN film thickness by simply varying the reaction time.

The self-assembly of molecular building blocks into well-defined macroscopic materials is desirable for developing emergent functional materials. However, the selfassembly of molecules into macroscopic materials remains challenging, in part because of limitations in controlling the growth and robustness of the materials. Herein, we report the molecular self-assembly of nano-to macroscopic free-standing materials through the coordination of metals with natural phenolic molecules. Our method involves a simple and scalable solution-based template dipping process in precomplexed metal−phenolic solutions, enabling the fabrication of free-standing macroscopic materials of customized architectures (2D and 3D geometries), thickness (about 10 nm to 5 μm), and chemical composition (different metals and phenolic ligands). Our macroscopic free-standing materials can be physically folded and unfolded like origami, yet are selectively degradable. Furthermore, metal nanoparticles can be grown in the macroscopic free-standing films, indicating their potential for future applications in biotechnology and catalysis.

Upon exposure to human blood, nanoengineered particles interact with a multitude of plasma components, resulting in the formation of a biomolecular corona. This corona modulates downstream biological responses, including recognition by and association with human immune cells. Considerable research effort has been directed toward the design of materials that can demonstrate a low affinity for various proteins (low-fouling materials) and materials that can exhibit low association with human immune cells (stealth materials). An implicit assumption common to bio–nano research is that nanoengineered particles that are low-fouling will also exhibit stealth. Herein, we investigated the link between the low-fouling properties of a particle and its propensity for stealth in whole human blood. High-fouling mesoporous silica (MS) particles and low-fouling zwitterionic poly­(2-methacryloyloxyethyl phosphorylcholine) (PMPC) particles were synthesized, and their interaction with blood components was assessed before and after precoating with serum albumin, immunoglobulin G, or complement protein C1q. We performed an in-depth proteomics characterization of the biomolecular corona that both identifies specific proteins and measures their relative abundance. This was compared with observations from a whole blood association assay that identified with which cell type each particle system associates. PMPC-based particles displayed reduced association both with cells and with serum proteins compared with MS-based particles. Furthermore, the enrichment of specific proteins within the biomolecular corona was found to correlate with association with specific cell types. This study demonstrates how the low-fouling properties of a material are indicative of its stealth with respect to immune cell association.

Interfacial self-assembly is a powerful organizational force for fabricating functional nanomaterials, including nanocarriers, for imaging and drug delivery. Herein, the interfacial self-assembly of pH-responsive metal-phenolic networks (MPNs) on the liquid-liquid interface of oil-in-water emulsions is reported. Oleic acid emulsions of 100-250 nm in diameter are generated by ultrasonication, to which poly(ethylene glycol) (PEG)-based polyphenolic ligands are assembled with simultaneous crosslinking by metal ions, thus forming an interfacial MPN. PEG provides a protective barrier on the emulsion phase and renders the emulsion low fouling. The MPN-coated emulsions have a similar size and dispersity, but an enhanced stability when compared with the uncoated emulsions, and exhibit a low cell association in vitro, a blood circulation half-life of ≈50 min in vivo, and are nontoxic to healthy mice. Furthermore, a model anticancer drug, doxorubicin, can be encapsulated within the emulsion phase at a high loading capacity (≈5 fg of doxorubicin per emulsion particle). The MPN coating imparts pH-responsiveness to the drug-loaded emulsions, leading to drug release at cell internalization pH and a potent cell cytotoxicity. The results highlight a straightforward strategy for the interfacial nanofabrication of pH-responsive emulsion-MPN systems with potential use in biomedical applications.

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter−property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.