SECM Investigation of Carbon Composite Thermoplastic Electrodes.

Thermoplastic electrodes (TPEs) are carbon composite electrodes consisting of graphite and thermoplastic polymer binder. TPE production is a solvent-based method, which makes it easy to fabricate and pattern into complex geometries, contrary to classical carbon composite electrodes. Depending on the composition (carbon type, binder, and composition ratio), TPEs can give excellent electrochemical performance and high conductivity. However, these TPEs are relatively new electrode materials, and thorough electrochemical characterization is still missing to understand and predict why large differences between TPEs exist. We used scanning electrochemical microscopy (SECM) as a screening tool to characterize TPEs. SECM data treatment based on scanning probe microscopy imaging allows a fast and easy comparison of the numerous images, as well as the optimization of the preparation. Experiments suggest that TPEs behave as a network of interacting microelectrodes made by electrochemically active islands isolated between less active areas. Higher carbon content in TPEs is not always indicative of more uniform electrodes with better electrochemical performances. Using various SECM redox probes, it is possible to select a specific graphite or polymer type for the analyte of interest. For example, TPEs made with COC:3569 are the best compromise for general detection, whereas PMMA:11 µm is better suited for catechol-like polyphenol analysis.

Electrochemical Dithiothreitol Assay for Large-Scale Particulate Matter Studies.

Particulate matter (PM) air pollution is associated with human morbidity and mortality. Measuring PM oxidative potential has been shown to provide a predictive measurement between PM exposure and adverse health impacts. The dithiothreitol (DTT) assay is commonly used to measure the oxidative potential of PM2.5 (PM less than 2.5 µm aerodynamic diameter). In the common, kinetic form of this assay, the decay of DTT is quantified over time (indirectly) using 5,5'-dithiobis(2-nitrobenzoic acid) (DTNB, Ellman's reagent) via UV/vis absorbance spectroscopy. The loss of DTT can also be quantified directly using electrochemical detection. The objectives of this work were (1) to evaluate the electrochemical assay, using commercially available equipment, relative to the UV/vis absorbance assay, and (2) to apply the electrochemical method to a large (>100) number of PM2.5 aerosol filter samples. Also presented here is the comparison an end-point assay to the kinetic assay, in an attempt to reduce the time, labor, and materials neccssary to quantify PM oxidative potential. The end-point, electrochemical assay gave comparable results to the UV/vis absorbance assay for PM filter sample analysis. Finally, high filter mass loadings (higher than about 0.5 µg PM per mm2 filter) lead to sub-optimal DTT assay performance, which suggests future studies should limit particle mass loadings on filters.

Polycaprolactone-enabled sealing and carbon composite electrode integration into electrochemical microfluidics.

Combining electrochemistry with microfluidics is attractive for a wide array of applications including multiplexing, automation, and high-throughput screening. Electrochemical instrumentation also has the advantage of being low-cost and can enable high analyte sensitivity. For many electrochemical microfluidic applications, carbon electrodes are more desirable than noble metals because they are resistant to fouling, have high activity, and large electrochemical solvent windows. At present, fabrication of electrochemical microfluidic devices bearing integrated carbon electrodes remains a challenge. Here, a new system for integrating polycaprolactone (PCL) and carbon composite electrodes into microfluidics is presented. The PCL : carbon composites have excellent electrochemical activity towards a wide range of analytes as well as high electrical conductivity (∼1000 S m-1). The new system utilizes a laser cutter for fast, simple fabrication of microfluidics using PCL as a bonding layer. As a proof-of-concept application, oil-in-water and water-in-oil droplets are electrochemically analysed. Small-scale electrochemical organic synthesis for TEMPO mediated alcohol oxidation is also demonstrated.

Characterizing nonconstant instrumental variance in emerging miniaturized analytical techniques.

Measurement variance is a crucial aspect of quantitative chemical analysis. Variance directly affects important analytical figures of merit, including detection limit, quantitation limit, and confidence intervals. Most reported analyses for emerging analytical techniques implicitly assume constant variance (homoskedasticity) by using unweighted regression calibrations. Despite the assumption of constant variance, it is known that most instruments exhibit heteroskedasticity, where variance changes with signal intensity. Ignoring nonconstant variance results in suboptimal calibrations, invalid uncertainty estimates, and incorrect detection limits. Three techniques where homoskedasticity is often assumed were covered in this work to evaluate if heteroskedasticity had a significant quantitative impact-naked-eye, distance-based detection using paper-based analytical devices (PADs), cathodic stripping voltammetry (CSV) with disposable carbon-ink electrode devices, and microchip electrophoresis (MCE) with conductivity detection. Despite these techniques representing a wide range of chemistries and precision, heteroskedastic behavior was confirmed for each. The general variance forms were analyzed, and recommendations for accounting for nonconstant variance discussed. Monte Carlo simulations of instrument responses were performed to quantify the benefits of weighted regression, and the sensitivity to uncertainty in the variance function was tested. Results show that heteroskedasticity should be considered during development of new techniques; even moderate uncertainty (30%) in the variance function still results in weighted regression outperforming unweighted regressions. We recommend utilizing the power model of variance because it is easy to apply, requires little additional experimentation, and produces higher-precision results and more reliable uncertainty estimates than assuming homoskedasticity.