Self-Organization of Iron Sulfide Nanoparticles into Complex Multicompartment Supraparticles.

Self-assembled compartments from nanoscale components are found in all life forms. Their characteristic dimensions are in 50-1000 nm scale, typically assembled from a variety of bioorganic "building blocks". Among the various functions that these mesoscale compartments carry out, protection of the content from the environment is central. Finding synthetic pathways to similarly complex and functional particles from technologically friendly inorganic nanoparticles (NPs) is needed for a multitude of biomedical, biochemical, and biotechnological processes. Here, it is shown that FeS2 NPs stabilized by l-cysteine self-assemble into multicompartment supraparticles (mSPs). The NPs initially produce ≈55 nm concave assemblies that reconfigure into ≈75 nm closed mSPs with ≈340 interconnected compartments with an average size of ≈5 nm. The intercompartmental partitions and mSP surface are formed primarily from FeS2 and Fe2 O3 NPs, respectively. The intermediate formation of cup-like particles enables encapsulation of biological cargo. This capability is demonstrated by loading mSPs with DNA and subsequent transfection of mammalian cells. Also it is found that the temperature stability of the DNA cargo is enhanced compared to the traditional delivery vehicles. These findings demonstrate that biomimetic compartmentalized particles can be used to successfully encapsulate and enhance temperature stability of the nucleic acid cargo for a variety of bioapplications.

Spanning Network Gels from Nanoparticles and Graph Theoretical Analysis of Their Structure and Properties.

Gels self-assembled from colloidal nanoparticles (NPs) translate the size-dependent properties of nanostructures to materials with macroscale volumes. Large spanning networks of NP chains provide high interconnectivity within the material necessary for a wide range of properties from conductivity to viscoelasticity. However, a great challenge for nanoscale engineering of such gels lies in being able to accurately and quantitatively describe their complex non-crystalline structure that combines order and disorder. The quantitative relationships between the mesoscale structural and material properties of nanostructured gels are currently unknown. Here, it is shown that lead telluride NPs spontaneously self-assemble into a spanning network hydrogel. By applying graph theory (GT), a method for quantifying the complex structure of the NP gels is established using a topological descriptor of average nodal connectivity that is found to correlate with the gel's mechanical and charge transport properties. GT descriptions make possible the design of non-crystalline porous materials from a variety of nanoscale components for photonics, catalysis, adsorption, and thermoelectrics.

Structural Analysis of Nanoscale Network Materials Using Graph Theory.

Many materials with remarkable properties are structured as percolating nanoscale networks (PNNs). The design of this rapidly expanding family of composites and nanoporous materials requires a unifying approach for their structural description. However, their complex aperiodic architectures are difficult to describe using traditional methods that are tailored for crystals. Another problem is the lack of computational tools that enable one to capture and enumerate the patterns of stochastically branching fibrils that are typical for these composites. Here, we describe a computational package, StructuralGT, to automatically produce a graph theoretical (GT) description of PNNs from various micrographs that addresses both challenges. Using nanoscale networks formed by aramid nanofibers as examples, we demonstrate rapid structural analysis of PNNs with 13 GT parameters. Unlike qualitative assessments of physical features employed previously, StructuralGT allows researchers to quantitatively describe the complex structural attributes of percolating networks enumerating the network's morphology, connectivity, and transfer patterns. The accurate conversion and analysis of micrographs was obtained for various levels of noise, contrast, focus, and magnification, while a graphical user interface provides accessibility. In perspective, the calculated GT parameters can be correlated to specific material properties of PNNs (e.g., ion transport, conductivity, stiffness) and utilized by machine learning tools for effectual materials design.