Search

The toxicity of metal ions to flora and fauna makes the monitoring of metals in the environment vital. Current methods of metal ion monitoring involve using classical elemental analysis techniques such as atomic absorption spectroscopy (AAS), inductively coupled plasma mass spectroscopy (ICPMS), and anodic stripping voltammetry (ASV). These classical analytical techniques are reliable and fast but suffer from two key problems. Firstly, they require the sample to be transported from the site of collection to a laboratory; secondly, they monitor either the total metal ion concentration or a labile concentration. The true toxicity of metal ions in the environment, however, is related to the amount of bioavailable metal rather than total metal.[1] Bioavailability is a somewhat loosely defined term as what is regarded as a bioavailable metal may vary from one species to the next.[2] As the water safety guidelines can only reflect the reliable measurement techniques available, the current guidelines attempt to assess bioavailability using a combination of techniques including filtration, ion exchange, ASV, chemical modeling of toxicity; and, if necessary, the effect on the target organism.[3] Such analyses are complicated and certainly not particularly compatible with monitoring in the field. So the challenges to the analytical community are to develop methods for on-site measurement of metal ion levels which may give an indication of the bioavailability of at least some metals. For any new analytical method to be competitive with AAS, ICP-MS, and ASV it is essential that it is reliable, robust, easy to use, and able to measure a suite of metals (all criteria met by these classical methods). The ability to undertake analyses in the field, cheaply and easily, is also desirable. These essential and desired criteria for a new analytical method are compatible with what biosensors are claimed to be able to achieve.[4] Biosensors have most frequently been applied to the detection of organic and biological molecules.[4–8] Thus far there has been little research into the detection of metals using biosensors. In the quest to have analytical methods which measure metals in the field, biosensors appear to be ideal as the recognition molecule is a biological molecule and hence could provide an indication of how the metal ions interact with a particular organism. The possibilities of detecting metal ions using biosensors and biological molecules have begun to attract more interest recently. The purpose of this article is to outline what has been achieved thus far in the detection of metal ions using biosensors. Initially we will discuss more mature biosensor technologies for monitoring metals which use enzymes and bacteria whereupon attention will turn to some new approaches which have recently been described. It is important to emphasize that selectivity in metal ion sensing is often more important than sensitivity because trace levels of heavy metals are present in a sample containing other ionic species which are often a million-fold more concentrated.[9]

Mixtures of benzene, toluene, ethylbenzene, p-xylene and naphthalene dissolved in water were probed with an array of partially selective gold nanoparticle chemiresistor sensors. A full factorial experimental design was followed to generate every possible combination (unary, binary, ternary, quaternary and quinary). The nominal concentrations of the individual components in the mixtures were 0, 0.5, 1, 5 or 10 mg/L and the combined concentrations were between 0 and 45 mg/L, which are relevant to EPA defined maximum contaminant levels in drinking water. Several different statistical techniques were used to predict the component concentrations in the mixtures based on the sensor array responses. The most accurate technique was a non-linear ensemble method called random forests. The overall root mean square error between the predicted and measured concentrations (residuals) was 0.2–1.5 mg/L for the mixtures with a nominal component concentration of 10 mg/L. The accuracy of the random forests predictions was not unduly affected by increasing mixture complexity. Random forests analysis is a statistical technique suitable for quantifying the relationship between responses of partially selective sensors to the concentration of different hydrocarbons in water.

We report on the development of a sensor system for the detection of hydrocarbons in water. The sensor system is based on functionalized gold nanoparticle chemiresistors that change their electrical resistance when exposed to chemical compounds. We fabricated arrays with sixteen chemiresistors, where each chemiresistor can contain gold nanoparticles functionalized with a different linker. The array of semi-selective sensors was exposed to solutions containing benzene, toluene, ethylbenzene, and xylenes (BTEX) dissolved in water. Using linear discriminant analysis we demonstrated that each one of the BTEX compounds could be distinguished.