Dielectrophoretic concentration of low-abundance nanoparticles using a nanostructured tip.

Electric field-induced concentration has the potential for application in highly sensitive detection of nanoparticles (NPs) for disease diagnosis and drug discovery. Conventional two-dimensional planar electrodes, however, have shown limited sensitivity in NP concentration. In this paper, the dielectrophoretic (DEP) concentration of low-abundance NPs is studied using a nanostructured tip where a high electric field of 3 × 10(7) V m(-1) is generated. In experimental studies, individual 2, 10, and 100 nm Au NPs are concentrated to a nanotip using DEP concentration and are detected by scanning transmission and scanning electron microscopes. The DEP force on Au NPs near the end of a nanotip is computed according to the distance, and then compared with Brownian motion-induced force. The computational study shows qualitative agreement with the experimental results. When the experimental conditions for DEP concentration are optimized for 8 nm-long oligonucleotides, the sensitivity of a nanotip is 10 aM (10 attomolar; nine copies in a 1.5 µl sample volume). This DEP concentrator using a nanotip can be used for molecular detection without amplification.

Quantifying uncertainties in the microvascular transport of nanoparticles.

The character of nanoparticle dispersion in the microvasculature is a driving factor in nanoparticle-based therapeutics and bio-sensing. It is difficult, with current experimental and engineering capability, to understand dispersion of nanoparticles because their vascular system is more complex than mouse models and because nanoparticle dispersion is so sensitive to in vivo environments. Furthermore, uncertainty cannot be ignored due to the high variation of location-specific vessel characteristics as well as variation across patients. In this paper, a computational method that considers uncertainty is developed to predict nanoparticle dispersion and transport characteristics in the microvasculature with a three step process. First, a computer simulation method is developed to predict blood flow and the dispersion of nanoparticles in the microvessels. Second, experiments for nanoparticle dispersion coefficients are combined with results from the computer model to suggest the true values of its unknown and unmeasurable parameters-red blood cell deformability and red blood cell interaction-using the Bayesian statistical framework. Third, quantitative predictions for nanoparticle transport in the tumor microvasculature are made that consider uncertainty in the vessel diameter, flow velocity, and hematocrit. Our results show that nanoparticle transport is highly sensitive to the microvasculature.

On the near-wall accumulation of injectable particles in the microcirculation: smaller is not better.

Although most nanofabrication techniques can control nano/micro particle (NMP) size over a wide range, the majority of NMPs for biomedical applications exhibits a diameter of ~100 nm. Here, the vascular distribution of spherical particles, from 10 to 1,000 nm in diameter, is studied using intravital microscopy and computational modeling. Small NMPs (≤100 nm) are observed to move with Red Blood Cells (RBCs), presenting an uniform radial distribution and limited near-wall accumulation. Larger NMPs tend to preferentially accumulate next to the vessel walls, in a size-dependent manner (~70% for 1,000 nm NMPs). RBC-NMP geometrical interference only is responsible for this behavior. In a capillary flow, the effective radial dispersion coefficient of 1,000 nm particles is ~3-fold larger than Brownian diffusion. This suggests that sub-micron particles could deposit within diseased vascular districts more efficiently than conventional nanoparticles.

Nanoscale sensor analysis using the immersed molecular electrokinetic finite element method.

The concentration and detection of molecular biomarkers remain as a challenge to develop point-of-care diagnostic devices. An electric field induced concentration has been studied for such purposes but with limited success due to limited efficacy. This paper presents a computational study for investigating the molecular concentration and retention efficacy of single nanowire (SNW) and dendritic nanotip (DNT) sensors. Our computational results indicate that compared to a DNT, the SNW sensor produces higher dielectrophoretic (DEP) forces in the vicinity of the terminal end of the tip. Furthermore, the magnitude of the DEP force increases exponentially as the diameter of the SNW is decreased, resulting in a further improved retention efficacy of NPs. However, the SNW sensor's concentration efficacy was not much improved for NPs smaller than 10 nm diameter when the nanowire diameter was reduced from 500 to 50 nm. Compared to the SNW, the DNT sensor showed improved concentration efficacy due to multiple points of electric field concentrations, which retard the exponential decay of the DEP force resulting in a greater widespread region where the DEP force dominates the Brownian motion forces. When oligonucleotides are used as a target particle, the DEP force can be used to elongate oligonucleotides to further enhance the concentration and retention efficacy.

Multiscale Simulation as a Framework for the Enhanced Design of Nanodiamond-Polyethylenimine-based Gene Delivery.

Nanodiamonds (NDs) are emerging carbon platforms with promise as gene/drug delivery vectors for cancer therapy. Specifically, NDs functionalized with the polymer polyethylenimine (PEI) can transfect small interfering RNAs (siRNA) in vitro with high efficiency and low cytotoxicity. Here we present a modeling framework to accurately guide the design of ND-PEI gene platforms and elucidate binding mechanisms between ND, PEI, and siRNA. This is among the first ND simulations to comprehensively account for ND size, charge distribution, surface functionalization, and graphitization. The simulation results are compared with our experimental results both for PEI loading onto NDs and for siRNA (C-myc) loading onto ND-PEI for various mixing ratios. Remarkably, the model is able to predict loading trends and saturation limits for PEI and siRNA, while confirming the essential role of ND surface functionalization in mediating ND-PEI interactions. These results demonstrate that this robust framework can be a powerful tool in ND platform development, with the capacity to realistically treat other nanoparticle systems.