Magnetite-graphene hybrids have been synthesized via a chemical reaction with a magnetite particle size of approximately 10 nm. The composites are superparamagnetic at room temperature and can be separated by an external magnetic field. As compared to bare magnetite particles, the hybrids show a high binding capacity for As(III) and As(V), whose presence in the drinking water in wide areas of South Asia has been a huge problem. Their high binding capacity is due to the increased adsorption sites in the M-RGO composite which occurs by reducing the aggregation of bare magnetite. Since the composites show near complete (over 99.9%) arsenic removal within 1 ppb, they are practically usable for arsenic separation from water.

Although various carbon nanomaterials including activated carbon, carbon nanotubes, and graphene have been successfully demonstrated for high-performance ultracapacitors, their capacitances need to be improved further for wider and more challenging applications. Herein, using nitrogen-doped graphene produced by a simple plasma process, we developed ultracapacitors whose capacitances (∼280 F/g(electrode)) are about 4 times larger than those of pristine graphene based counterparts without sacrificing other essential and useful properties for ultracapacitor operations including excellent cycle life (>200,000), high power capability, and compatibility with flexible substrates. While we were trying to understand the improved capacitance using scanning photoemission microscopy with a capability of probing local nitrogen-carbon bonding configurations within a single sheet of graphene, we observed interesting microscopic features of N-configurations: N-doped sites even at basal planes, distinctive distributions of N-configurations between edges and basal planes, and their distinctive evolutions with plasma duration. The local N-configuration mappings during plasma treatment, alongside binding energy calculated by density functional theory, revealed that the origin of the improved capacitance is a certain N-configuration at basal planes.

The most frequent infectious diseases in humans-and those with the highest potential for rapid pandemic spread-are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission route, very little is known about the dynamic patterns of CPIs. Using wireless sensor network technology, we obtained high-resolution data of CPIs during a typical day at an American high school, permitting the reconstruction of the social network relevant for infectious disease transmission. At 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 m among 788 individuals. The data revealed a high-density network with typical small-world properties and a relatively homogeneous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact network data were most effective at high vaccination coverage.disease dynamics | network topology | public health | human interactions

Stretchable, wireless health monitoring patches to evaluate heart rate, blood oximetry, UV exposure, and skin coloration.