Magnetic liquid marbles : Toward “Lab in a Droplet“

Liquid marbles exhibit great potential for use as miniature labs for small-scale laboratory operations, such as experiment and measurement. While important progress has been made recently in exploring their applications as microreactions, “on-line“ measurement of the components inside the liquid still remains a challenge. Herein, it is demonstrated that “on-line“ detection can be realized on magnetic liquid marbles by taking advantage of their unique magnetic opening feature. By partially opening the particle shell, electrochemical measurement is carried out with a miniaturized three-electrode probe and the application of this technique for quantitative measurement of dopamine is demonstrated. Fully opened magnetic liquid marble makes it feasible to detect the optical absorbance of the liquid in a transmission mode. With this optical method, a glucose assay is demonstrated. Moreover, when magnetic particle shell contains low melting point material, e.g., wax, the liquid marble shows a unique encapsulation ability to form a rigid shell after heating, which facilitates the storage of the non-volatile ingredients. These unique features, together with the versatile use as microreactors, enable magnetic liquid marbles to function as a miniature lab (or called “lab in a droplet“), which may find applications in clinical diagnostics, biotechnology, chemical synthesis, and analytical chemistry.

Performance analysis of a field-effect-transistor based aptasensor

Prostate cancer is one of the most diagnosed cancers which leads to a considerable number of deaths due to the lack of early and sensitive detection. This paper presents an aptamer functionalized field effect (FET) based biosensor for the detection of prostate cancer. Prostate specific antigen (PSA) is considered as the biomarker for prostate cancer whose detection is confirmed by attaching aptamers onto the sensor surface. Through the modelling and numerical simulation, the paper aims to evaluate and predict the performance parameters such as sensitivity, settling time, and limit of detection (LOD) of a label-free FET based electronic biosensor. Various sensor parameters such as structure (i.e., geometry), type of the FET (e.g., nanowire FET, spherical FET, ion-selective FET, and magnetic particle) radius of the FET channel and incubation time are optimized and analyzed. In addition, concentration of analyte biomolecules, diffusion coefficients and affinity to the receptor molecules are also investigated to determine the optimize performance parameters.

The effect of nano-particle TiO2 fillers on structure and transport in polymer electrolytes

Nano-particle oxide fillers including TiO₂, SiO₂ and Al₂O₃ have previously been shown to have a significant affect on the properties of polymer electrolytes, especially those based on polyether–lithium salt systems. In some cases, conductivity increases of more than one order of magnitude have been reported in crystalline PEO-based complexes. In this work, we report on the effects of TiO₂ on a completely amorphous polyether-based system to remove the complication of multiple phases presented by the semi-crystalline nature of PEO. Multinuclear magnetic resonance spectroscopy has shown that the lithium ion environment is changed by the addition of filler. Vibrational spectroscopy shows that the filler influences the disordered-longitudinal acoustic modes (DLAM) in the case of an amorphous polyether and suggests an interaction between the filler surface and the polymer. Positron annihilation lifetime spectroscopy indicates an increase in free volume upon addition of filler to an amorphous polyether–salt complex, coinciding with an apparent increase in polymer mobility as determined from ¹H T₂ NMR measurements. Impedance spectroscopy has shown clear evidence of an inter-phase region that may be more or less conductive than the bulk polymer electrolyte itself. The data support a model which includes conduction through an interfacial region in addition to the bulk polymer

Magnet-induced temporary superhydrophobic coatings from one-pot synthesized hydrophobic magnetic nanoparticles.

In this paper, we report on the production of superhydrophobic coatings on various substrates (e.g., glass slide, silicon wafer, aluminum foil, plastic film, nanofiber mat, textile fabrics) using hydrophobic magnetic nanoparticles and a magnet-assembly technique. Fe3O4 magnetic nanoparticles functionalized with a thin layer of fluoroalkyl silica on the surface were synthesized by one-step coprecipitation of Fe2+/Fe3+ under an alkaline condition in the presence of a fluorinated alkyl silane. Under a magnetic field, the magnetic nanoparticles can be easily deposited on any solid substrate to form a thin superhydrophobic coating with water contact angle as high as 172°, and the surface superhydrophobicity showed very little dependence on the substrate type. The particulate coating showed reasonable durability because of strong aggregation effect of nanoparticles, but the coating layer can be removed (e.g., by ultrasonication) to restore the original surface feature of the substrates. By comparison, the thin particle layer deposited under no magnetic field showed much lower hydrophobicity. The main reason for magnet-induced superhydrophobic surfaces is theformation of nano- and microstructured surface features. Such a magnet-induced temporary superhydrophobic coating may have wide applications in electronic, biomedical, and defense-related areas.

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.

Parameter estimation of the BOLD fMRI model within a general particle filter framework

This work demonstrates a novel Bayesian learning approach for model based analysis of Functional Magnetic Resonance (fMRI) data. We use a physiologically inspired hemodynamic model and investigate a method to simultaneously infer the neural activity together with hidden state and the physiological parameter of the model. This joint estimation problem is still an open topic. In our work we use a Particle Filter accompanied with a kernel smoothing approach to address this problem within a general filtering framework. Simulation results show that the proposed method is a consistent approach and has a good potential to be enhanced for further fMRI data analysis.