As the physicochemical properties of drug delivery systems are governed not only by the material properties which they are compose of but by their size that they conform, it is crucial to determine the size and distribution of such systems with nanometer-scale precision. The standard technique used to measure the size distribution of nanometer-sized particles in suspension is dynamic light scattering (DLS). Recently, nanoparticle tracking analysis (NTA) has been introduced to measure the diffusion coefficient of particles in a sample to determine their size distribution in relation to DLS results. Because DLS and NTA use identical physical characteristics to determine particle size but differ in the weighting of the distribution, NTA can be a good verification tool for DLS and vice versa. In this study, we evaluated two NTA data analysis methods based on maximum-likelihood estimation, namely finite track length adjustment (FTLA) and an iterative method, on monodisperse polystyrene beads and polydisperse vesicles by comparing the results with DLS. The NTA results from both methods agreed well with the mean size and relative variance values from DLS for monodisperse polystyrene standards. However, for the lipid vesicles prepared in various polydispersity conditions, the iterative method resulted in a better match with DLS than the FTLA method. Further, it was found that it is better to compare the native number-weighted NTA distribution with DLS, rather than its converted distribution weighted by intensity, as the variance of the converted NTA distribution deviates significantly from the DLS results.

Recent developments of 3D-graphene and 3D-boron-nitride have become of great interest owing to their potential for ultra-light flexible electronics. Here we demonstrate the first synthesis of novel 3D-BNC hybrids. By specifically controlling the compositions of C and BN, new fascinating properties are observed, such as highly tunable electrical conductivity, controllable EMI shielding properties, and stable thermal conductivity. This ultra-light hybrid opens up many new applications such as for electronic packaging and thermal interface materials (TIMs).

In data stream clustering, it is desirable to have algorithms that are able to detect clusters of arbitrary shape, clusters that evolve over time, and clusters with noise. Existing stream data clustering algorithms are generally based on an online-offline approach: The online component captures synopsis information from the data stream (thus, overcoming real-time and memory constraints) and the offline component generates clusters using the stored synopsis. The online-offline approach affects the overall performance of stream data clustering in various ways: the ease of deriving synopsis from streaming data; the complexity of data structure for storing and managing synopsis; and the frequency at which the offline component is used to generate clusters. In this article, we propose an algorithm that (1) computes and updates synopsis information in constant time; (2) allows users to discover clusters at multiple resolutions; (3) determines the right time for users to generate clusters from the synopsis information; (4) generates clusters of higher purity than existing algorithms; and (5) determines the right threshold function for density-based clustering based on the fading model of stream data. To the best of our knowledge, no existing data stream algorithms has all of these features. Experimental results show that our algorithm is able to detect arbitrarily shaped, evolving clusters with high quality.

Wearable biosensors hold significant potential for healthcare and environmental applications, and the development of flexible and biocompatible sensing platforms for high accuracy detection of physiological biomarkers remains an elusive goal. Herein, an ultrasensitive, flexible sensor is described that is based on a 3D hierarchical biocomposite comprised of hollow, natural pollen microcapsules that are coated with a conductive graphene layer. Modular assembly of the graphene‐coated microcapsules onto an ultrathin polyethylene terephthalate layer enables a highly flexible sensor configuration with tunable selectivity afforded by subsequent covalent immobilization of antibodies against target antigens. In a proof‐of‐concept example, the biosensor demonstrates ultrahigh sensitivity detection of prostate specific antigen (PSA) down to 1.7 × 10−15m with real‐time feedback and superior performance over conventional 2D graphene‐coated sensors. Importantly, the device performance is consistently high across various bending conditions. Taken together, the results demonstrated in this work highlight the merits of employing lightweight biocomposites as modular building blocks for the design of flexible biosensors with highly responsive and sensitive molecular detection capabilities.

A plasmonic nanohole sensor for virus-like particle capture and virucidal drug evaluation is reported. Using a materials-selective surface functionalization scheme, passive immobilization of virus-like particles only within the nanoholes is achieved. The findings demonstrate that a low surface coverage of particles only inside the functionalized nanoholes significantly improves nanoplasmonic sensing performance over conventional nanohole arrays.