Packing and size determination of colloidal nanoclusters.

Here we demonstrate a rapid and quantitative means to characterize the size and packing structure of small clusters of nanoparticles in colloidal suspension. Clustering and aggregation play important roles in a wide variety of phenomena of both scientific and technical importance, yet characterizing the packing of nanoparticles within small clusters and predicting their aerodynamic size remains challenging because available techniques can lack adequate resolution and sensitivity for clusters smaller than 100 nm (optical techniques), perturb the packing arrangement (electron microscopies), or provide only an ensemble average (light scattering techniques). In this article, we use electrospray-differential mobility analysis (ES-DMA), a technique that exerts electrical and drag forces on the clusters, to determine the size and packing of small clusters. We provide an analytical model to determine the mobility size of various packing geometries based on the projected area of the clusters. Data for clusters aggregated from nominally 10 nm gold particles and nonenveloped viruses of various sizes show good agreement between measured and predicted cluster sizes for close-packed spheres.

Bioinspired methodology for artificial olfaction.

Artificial olfaction is a potential tool for noninvasive chemical monitoring. Application of "electronic noses" typically involves recognition of "pretrained" chemicals, while long-term operation and generalization of training to allow chemical classification of "unknown" analytes remain challenges. The latter analytical capability is critically important, as it is unfeasible to pre-expose the sensor to every analyte it might encounter. Here, we demonstrate a biologically inspired approach where the recognition and generalization problems are decoupled and resolved in a hierarchical fashion. Analyte composition is refined in a progression from general (e.g., target is a hydrocarbon) to precise (e.g., target is ethane), using highly optimized response features for each step. We validate this approach using a MEMS-based chemiresistive microsensor array. We show that this approach, a unique departure from existing methodologies in artificial olfaction, allows the recognition module to better mitigate sensor-aging effects and to better classify unknowns, enhancing the utility of chemical sensors for real-world applications.