Jordi Riu

Nanosensors in environmental analysis.

Nanoscience and nanotechnology deal with the study and application of structures of matter of at least one dimension of the order of less than 100 nm (1 nm=one millionth of a millimetre). However, properties related to low dimensions are more important than size. Nanotechnology is based on the fact that some very small structures usually have new properties and behaviour that are not displayed by the bulk matter with the same composition. This overview introduces and discusses the main concepts behind the development of nanosensors and the most relevant applications in the field of environmental analysis. We focus on the effects (many of which are related to the quantum nature) that distinguish nanosensors and give them their particular behaviour. We will review the main types of nanosensors developed to date and highlight the relationship between the property monitored and the type of nanomaterial used. We discuss several nanostructures that are currently used in the development of nanosensors: nanoparticles, nanotubes, nanorods, embedded nanostructures, porous silicon, and self-assembled materials. In each section, we first describe the type of nanomaterial used and explain the properties related to the nanostructure. We then briefly describe the experimental set up and discuss the main advantages and quality parameters of nanosensing devices. Finally, we describe the applications, many of which are in the environmental field.

Gas sensors based on nanostructured materials

Gas detection is important for controlling industrial and vehicle emissions, household security and environmental monitoring. In recent decades many devices have been developed for detecting CO(2), CO, SO(2), O(2), O(3), H(2), Ar, N(2), NH(3), H(2)O and several organic vapours. However, the low selectivity or the high operation temperatures required when most gas sensors are used have prompted the study of new materials and the new properties that come about from using traditional materials in a nanostructured mode. In this paper, we have reviewed the main research studies that have been made of gas sensors that use nanomaterials. The main quality characteristics of these new sensing devices have enabled us to make a critical review of the possible advantages and drawbacks of these nanostructured material-based sensors.

Real-time potentiometric detection of bacteria in complex samples.

Detecting and identifying pathogen bacteria is essential to ensure quality at all stages of the food chain and to diagnose and control microbial infections. Traditional detection methods, including those based on cell culturing, are tedious and time-consuming, and their further application in real samples generally implies more complex pretreatment steps. Even though state-of-the-art techniques for detecting microorganisms enable the quantification of very low concentrations of bacteria, to date it has been difficult to obtain successful results in real samples in a simple, reliable, and rapid manner. In this Article, we demonstrate that the label-free detection and identification of living bacteria in real samples can be carried out in a couple of minutes and in a direct, simple, and selective way at concentration levels as low as 6 colony forming units/mL (CFU) in complex matrices such as milk or 26 CFU/mL in apple juice where the pretreatment step of samples is extremely easy. We chose Escherichia coli ( E. coli ) CECT 675 cells as a model organism as a nonpathogenic surrogate for pathogenic E. coli O157:H7 to test the effectiveness of a potentiometric aptamer-based biosensor. This biosensor uses single-walled carbon nanotubes (SWCNT) as excellent ion-to-electron transducers and covalently immobilized aptamers as biorecognition elements. The selective aptamer-target interaction significantly changes the electrical potential, thus allowing for both interspecies and interstrain selectivity and enabling the direct detection of the target. This technique is therefore a powerful tool for the immediate identification and detection of microorganisms. We demonstrate the highly selective detection of living bacteria with an immediate linear response of up to 10(4) CFU/mL. The biosensor can be easily built and used, is regenerated without difficulty, and can be used at least five times with no loss in the minimum amount of detected bacteria.

Estimating uncertainties of analytical results using information from the validation process

A new approach for calculating uncertainties of analytical results based on the information from the validation process is proposed. This approach complements the existing approaches proposed to date and can be applied to any validated analytical method. The precision estimates generated during the process of assessment of the accuracy take into account the uncertainties of preprocessing steps and analytical measurement steps as long as the different factors that influence these steps are representatively varied in the whole validation process. Since the accuracy of an analytical method should be always assessed before applying it to future working samples, little extra work needs to be done to estimate the final uncertainty. Other sources of uncertainty not previously considered (e.g. uncertainty associated to sampling, to differences between the test-sample and the working sample, etc.) are subsequently included and mathematically combined with the uncertainty arising from the assessment of the accuracy to provide the overall uncertainty. These ideas are illustrated with a case study of the determination of copper in contaminated land.

Jack-knife technique for outlier detection and estimation of standard errors in PARAFAC models

Abstract In the last years, multi-way analysis has become increasingly important because it has proved to be a valuable tool, e.g. in interpreting data provided by instrumental methods that describe the multivariate and complex reality of a given problem. Parallel factor analysis (PARAFAC) is one of the most widely used multi-way models. Despite its usefulness in many applications, up to date there is no available tool in the literature to estimate the standard errors associated with the parameter estimates. In this study, we apply the so-called jack-knife technique to PARAFAC in order to find the associated standard errors to the parameter estimates from the PARAFAC model. The jack-knife technique is also shown to be useful for detecting outliers. An example of fluorescence data (emission/excitation landscapes) is used to show the applicability of the method.

Label-free detection of Staphylococcus aureus in skin using real-time potentiometric biosensors based on carbon nanotubes and aptamers

In this paper we report the first biosensor that is able to detect Staphylococcus aureus in real-time. A network of single-walled carbon nanotubes (SWCNTs) acts as an ion-to-electron potentiometric transducer and anti-S. aureus aptamers are the recognition element. Carbon nanotubes were functionalized with aptamers using two different approaches: (1) non-covalent adsorption of drop-casted pyrenil-modified aptamers onto the external walls of the SWCNTs; and (2) covalent bond formation between amine-modified aptamers and carboxylic groups previously introduced by oxidation at the ends of the SWCNTs. Both of these approaches yielded functional biosensors but there were large differences in the minimum detectable bacteria concentration and sensitivity values. With covalent functionalization, the minimum concentration detected was 8×10(2)colony-forming units (CFU)/mL and the sensitivity was 0.36 mV/Decade. With the non-covalent approach, the sensitivity was higher (1.52 mV/Decade) but the minimum concentration detected was greatly affected (10(7) CFU/mL). In both cases, potential as a function of Decade of bacteria concentration was linear. Functional biosensors were used to test real samples from freshly excised pig skin, contaminated with the target microorganism, as a surrogate for human skin.

Graphene-based potentiometric biosensor for the immediate detection of living bacteria

In this communication we present a potentiometric aptasensor based on chemically modified graphene (transducer layer of the aptasensor) and aptamers (sensing layer). Graphene oxide (GO) and reduced graphene oxide (RGO) are the basis for the construction of two versions of the aptasensor for the detection of a challenging living organism such as Staphylococcus aureus. In these two versions, DNA aptamers are either covalently (in the GO case) or non-covalently (in the RGO case) attached to the transducer layer. In both cases we are able to selectively detect a single CFU/mL of S. aureus in an assay close to real time, although the noise level associated to the aptasensors made with RGO is lower than the ones made with GO. These new aptasensors, that show a high selectivity, are characterized by the simplicity of the technique and the materials used for their construction while offering ultra-low detection limits in very short time responses in the detection of microorganisms.

Ion-selective electrodes using multi-walled carbon nanotubes as ion-to-electron transducers for the detection of perchlorate

A solid contact ion-selective electrode using for the first time multi-walled carbon nanotubes (MWCNT) for the transducer material was developed for detecting perchlorate in water. To demonstrate the excellent ion-to electron transducer ability of the MWCNTs, a 15 microm thick layer of carboxylated MWCNT was deposited between an acrylic membrane selective to perchlorate ions and a glassy carbon rod used as the substrate and electrical conductor. The electrodes showed a Nernstian response of 57 mV decade(-1) (standard deviation of 3 mV decade(-1) over time and different electrodes) across a wide linear range of 10(-6) to 10(-2) M. The limit of detection was 10(-7.4) M of perchlorate. The response time was less than 10 s for activities higher than 10(-6) M and the intermediate-term potential stability shows a small drift of 0.22 mV h(-1) recorded over 5 hours. The electrode displays a selectivity comparable to liquid-contacted ISEs containing the same membrane.

Fast picomolar selective detection of bisphenol A in water using a carbon nanotube field effect transistor functionalized with estrogen receptor-α

In this paper we report a biosensor for the fast, ultrasensitive and selective determination of bisphenol A in water. It is based on a field effect transistor (FET) in which a network of single-walled carbon nanotubes (SWCNTs) acts as the conductor channel. SWCNTs are functionalized for the first time with a nuclear receptor, the estrogen receptor alpha (ER-alpha), which is adsorbed onto the SWCNTs and acts as the sensing part of the biosensor. SWCTNs are subsequently protected to prevent the non-specific binding of interferences. With this biosensor we can detect picomolar concentrations of BPA in only 2 min of analysis. Selectivity has been tested against possible interferences such as fluoranthene, pentacloronitrobenzene and malathion, and this is the first device that experimentally shows that small molecules can also be selectively detected at ultralow concentrations using a CNTFET biosensor.

Measurement uncertainty in analytical methods in which trueness is assessed from recovery assays

We propose a new procedure for estimating the uncertainty in quantitative routine analysis. This procedure uses the information generated when the trueness of the analytical method is assessed from recovery assays. In this paper, we assess trueness by estimating proportional bias (in terms of recovery) and constant bias separately. The advantage of the procedure is that little extra work needs to be done to estimate the measurement uncertainty associated to routine samples. This uncertainty is considered to be correct whenever the samples used in the recovery assays are representative of the future routine samples (in terms of matrix and analyte concentration). Moreover, these samples should be analysed by varying all the factors that can affect the analytical method. If they are analysed in this fashion, the precision estimates generated in the recovery assays take into account the variability of the routine samples and also all the sources of variability of the analytical method. Other terms related to the sample heterogeneity, sample pretreatments or factors not representatively varied in the recovery assays should only be subsequently included when necessary. The ideas presented are applied to calculate the uncertainty of results obtained when analysing sulphides in wine by HS-SPME-GC.