Evaluation of a Microfluidic Device for the Electrochemical Determination of Halide Content in Ionic Liquids

A microfluidic device designed for electrochemical studies on a microliter scale has been utilized for the examination of impurity levels in ionic liquids (ILs). Halide impurities are common following IL synthesis, and this study demonstrates the ability to quantify low concentrations of halide in a range of ILs to levels of similar to 5 ppm, even in ILs not currently measurable using other methods such as ion chromatography. To validate the mixer device, the electrochemistry of ferrocene was also examined and compared with spectroscopic and bulk electrochemistry measurements. An automated

Quantitative mapping of aqueous microfluidic temperature with sub-degree resolution using fluorescence lifetime imaging microscopy

The use of a water-soluble, thermo-responsive polymer as a highly sensitive fluorescence-lifetime probe of microfluidic temperature is demonstrated. The fluorescence lifetime of poly(N-isopropylacrylamide) labelled with a benzofurazan fluorophore is shown to have a steep dependence on temperature around the polymer phase transition and the photophysical origin of this response is established. The use of this unusual fluorescent probe in conjunction with fluorescence lifetime imaging microscopy (FLIM) enables the spatial variation of temperature in a microfluidic device to be mapped, on the micron scale, with a resolution of less than 0.1 degrees C. This represents an increase in temperature resolution of an order of magnitude over that achieved previously by FLIM of temperature-sensitive dyes

Use of a novel micro-fluidic device to create arrays for multiplex analysis of large and small molecular weight compounds by surface plasmon resonance

There is an increasing demand to develop biosensor monitoring devices capable of biomarker profiling for predicting animal adulteration and detecting multiple chemical contaminants or toxins in food produce. Surface plasmon resonance (SPR) biosensors are label free detection systems that monitor the binding of specific biomolecular recognition elements with binding partners. Essential to this technology are the production of biochips where a selected binding partner, antibody, biomarker protein or low molecular weight contaminant, is immobilised. A micro-fluidic immobilisation device allowing the covalent attachment of up to 16 binding partners in a linear array on a single surface has been developed for compatibility with a prototype multiplex SPR analyser.

The immobilisation unit and multiplex SPR analyser were respectively evaluated in their ability to be fit-for-purpose for binding partner attachment and detection of high and low molecular weight molecules. The multiplexing capability of the dual technology was assessed using phycotoxin concentration analysis as a model system. The parent compounds of four toxin groups were immobilised within a single chip format and calibration curves were achieved. The chip design and SPR technology allowed the compartmentalisation of the binding interactions for each toxin group offering the added benefit of being able to distinguish between toxin families and perform concentration analysis. This model is particularly contemporary with the current drive to replace biological methods for phycotoxin screening.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.