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How nanoparticles distribute in living cells and overcome cellular barriers are important criteria in the design of drug carriers. Pair-correlation microscopy is a correlation analysis of fluctuation in the fluorescence intensity obtained by a confocal line scan that can quantify the dynamic properties of nanoparticle diffusion including the number of mobile nanoparticles, diffusion coefficient, and transit time across a spatial distance. Due to the potential heterogeneities in nanoparticle properties and the complexity within the cellular environment, quantification of averaged auto- and pair-correlation profiles may obscure important insights into the ability of nanoparticles to deliver drugs. To overcome this issue, we used phasor analysis to develop a data standardizing method, which can segment the scanned line into several subregions according to diffusion and address the spatial heterogeneity of nanoparticles moving inside cells. The phasor analysis is a fit-free method that represents autocorrelation profiles for each pixel relative to free diffusion on the so-called phasor plots. Phasor plots can then be used to select subpopulations for which the auto- and pair-correlation analysis can be performed separately. We demonstrate the phasor analysis for pair-correlation microscopy for investigating 16 nm, Cy5-labeled silica nanoparticles diffusing across the plasma membrane and green fluorescent proteins (GFP) diffusing across nuclear envelope in MCF-7 cells.

Knowledge of the interaction between aptamer and protein is integral to the design and development of aptamer-based biosensors. Nanoparticles functionalized with aptamers are commonly used in these kinds of sensors. As such, studies into how the number of aptamers on the nanoparticle surface influence both kinetics and thermodynamics of the binding interaction are required. In this study, aptamers specific for interferon gamma (IFN-γ) were immobilized on the surface of gold nanoparticles (AuNPs), and the effect of surface coverage of aptamer on the binding interaction with its target was investigated using fluorescence spectroscopy. The number of aptamers were adjusted from an average of 9.6 to 258 per particle. The binding isotherm between AuNPs-aptamer conjugate and protein was modeled with the Hill-Langmuir equation, and the determined equilibrium dissociation constant ( K ' D ) decreased 10-fold when increasing the coverage of aptamer. The kinetics of the reaction as a function of coverage of aptamer were also investigated, including the association rate constant ( k on ) and the dissociation rate constant ( k off ). The AuNPs-aptamer conjugate with 258 aptamers per particle had the highest k on , while the k off was similar for AuNPs-aptamer conjugates with different surface coverages. Therefore, the surface coverage of aptamers on AuNPs affects both the thermodynamics and the kinetics of the binding. The AuNPs-aptamer conjugate with the highest surface coverage is the most favorable in biosensors considering the limit of detection, sensitivity, and response time of the assay. These findings deepen our understanding of the interaction between aptamer and target protein on the particle surface, which is important to both improve the scientific design and increase the application of aptamer-nanoparticle based biosensor.

We report the development of metal-organic framework (MOF)-based probes for the direct and rapid detection and quantification of perfluorooctanoic acid (PFOA) by mass spectrometry. Four water-resistant MOFs-ZIF-8, UiO-66, MIL88-A, and Tb 2 (BDC) 3 -were coated on poly(dopamine) precoated stainless steel needles and used to rapidly preconcentrate PFOA from water for direct analysis by nanoelectrospray ionization mass spectrometry. The analytical performance of each MOF for detecting PFOA was correlated with both the calculated binding energy of the MOF for PFOA and the relative change in the surface area of the MOF upon exposure to PFOA. MOF-functionalized probes can be used for the rapid (<5 min) and sensitive quantification of PFOA molecules at low ng L -1 levels in environmental water samples (i.e., tap water, rainwater, and seawater) with no sample preparation. The limit of detection of PFOA in ultrapure water was 11.0 ng L -1 . Comparable accuracy to an accredited analytical method was achieved, despite the MOF-functionalized probe approach being ∼40 times quicker and requiring ∼10 times less sample. These features indicate that MOF-coated probes are promising for the direct and rapid monitoring of polyfluorinated substances and other pollutants in the field.

In industry as well as many areas of scientific research, data collected often contain a number of responses of interest for a chosen set of exploratory variables. Optimization of such multivariable multiresponse systems is a challenge well suited to genetic algorithms as global optimization tools. One such example is the optimization of coating surfaces with the required absolute and relative sensitivity for detecting analytes using devices such as sensor arrays. High-throughput synthesis and screening methods can be used to accelerate materials discovery and optimization; however, an important practical consideration for successful optimization of materials for arrays and other applications is the ability to generate adequate information from a minimum number of experiments. Here we present a case study to evaluate the efficiency of a novel evolutionary model-based multiresponse approach (EMMA) that enables the optimization of a coating while minimizing the number of experiments. EMMA plans the experiments and simultaneously models the material properties. We illustrate this novel procedure for materials optimization by testing the algorithm on a sol-gel synthetic route for production and optimization of a well studied amino-methyl-silane coating. The response variables of the coating have been optimized based on application criteria for micro- and macro-array surfaces. Spotting performance has been monitored using a fluorescent dye molecule for demonstration purposes and measured using a laser scanner. Optimization is achieved by exploring less than 2% of the possible experiments, resulting in identification of the most influential compositional variables. Use of EMMA to optimize control factors of a product or process is illustrated, and the proposed approach is shown to be a promising tool for simultaneously optimizing and modeling multivariable multiresponse systems.