Charge carrier pairing can impart efficient reduction efficiency to core/shell quantum dots: applications for chemical sensing.

Semiconductor quantum dots (QDs) are bright fluorophores that have significant utility for imaging and sensing applications. Core QDs are often employed in chemosensing via redox processes that modulates their fluorescence in the presence of an analyte. However, such particles lack robust surface passivation and generally contain a sizable portion of nonfluorescent QDs, which is detrimental to the detection limit. We investigated an approach to "turn on" non-fluorescent core QDs by lightly overcoating them with a thin shell of a higher bandgap semiconductor. The shell augments the population of sensing chromophores and increases the emission lifetime; however, it simultaneously mollifies redox processes that are responsible for analyte sensitivity to begin with. This balancing act was successfully applied to enhance the sensitivity of CdZnS/ZnS QDs towards 2,4,6-trinitrotoluene (TNT). Unexpectedly, it was found that CdZnS/ZnS QDs with very thick shells retained substantial sensitivity to TNT. This observation may be due to close coupling of the reduced substrate with the QD hole that is enabled by the near-degeneracy of holes in the core CdZnS and ZnS shell. The ability of core/shell QDs to retain substantial reducing power may have implications for other applications that can benefit from the enhanced stability of robust core/shell nanomaterials.

Contact Printing of a Quantum Dot and Polymer Cross-Reactive Array Sensor.

The incorporation of organic polymeric materials into chemical sensors and electro-optic devices has the potential to greatly advance these fields. A major challenge to their incorporation is the fabrication of thin films due to their intolerance of thermal deposition methods and solvent compatibility challenges. Here, a method for contact printing of quantum dot (QD) and organic polymer (OP) composites for the production of thin-film chemical sensors is described. The method described here allows for the repeatable, low-cost, and relatively simple production of thin films of QD/OP composites for use in chemical sensor arrays by a dry transfer process of the polymer on an elastomer stamp to the sensor substrate.

Enhance the Discrimination Precision of Graphene Gas Sensors with a Hidden Markov Model.

Sensors are the key element to enable smart electronics and will play an important role in the emerging big data era. In this work, we reported an experimental study and a data-analytical characterization method to enhance the precision of discriminating chemically and structurally similar gases. Graphene sensors were fabricated by conventional photolithography and measured with feature analysis against different chemicals. A new hidden Markov model assisted with frequency spectral analysis, and the Gaussian mixture model (K-GMM-HMM) is developed to discriminate similar gases. The results indicated that the new method achieved a high prediction accuracy of 94%, 27% higher than the maximum value obtained by the conventional methods or other feature transient analysis methods. This study indicated that graphene gas sensors with the new K-GMM-HMM analysis are very attractive for chemical discrimination used in future smart electronics.

Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants.

A cross-reactive array of semiselective chemiresistive sensors made of polymer-graphene nanoplatelet (GNP) composite coated electrodes was examined for detection and discrimination of chemical warfare agents (CWA). The arrays employ a set of chemically diverse polymers to generate a unique response signature for multiple CWA simulants and background interferents. The developed sensors' signal remains consistent after repeated exposures to multiple analytes for up to 5 days with a similar signal magnitude across different replicate sensors with the same polymer-GNP coating. An array of 12 sensors each coated with a different polymer-GNP mixture was exposed 100 times to a cycle of single analyte vapors consisting of 5 chemically similar CWA simulants and 8 common background interferents. The collected data was vector normalized to reduce concentration dependency, z-scored to account for baseline drift and signal-to-noise ratio, and Kalman filtered to reduce noise. The processed data was dimensionally reduced with principal component analysis and analyzed with four different machine learning algorithms to evaluate discrimination capabilities. For 5 similarly structured CWA simulants alone 100% classification accuracy was achieved. For all analytes tested 99% classification accuracy was achieved demonstrating the CWA discrimination capabilities of the developed system. The novel sensor fabrication methods and data processing techniques are attractive for development of sensor platforms for discrimination of CWA and other classes of chemical vapors.

Discrimination Enhancement with Transient Feature Analysis of a Graphene Chemical Sensor.

A graphene chemical sensor is subjected to a set of structurally and chemically similar hydrocarbon compounds consisting of toluene, o-xylene, p-xylene, and mesitylene. The fractional change in resistance of the sensor upon exposure to these compounds exhibits a similar response magnitude among compounds, whereas large variation is observed within repetitions for each compound, causing a response overlap. Therefore, traditional features depending on maximum response change will cause confusion during further discrimination and classification analysis. More robust features that are less sensitive to concentration, sampling, and drift variability would provide higher quality information. In this work, we have explored the advantage of using transient-based exponential fitting coefficients to enhance the discrimination of similar compounds. The advantages of such feature analysis to discriminate each compound is evaluated using principle component analysis (PCA). In addition, machine learning-based classification algorithms were used to compare the prediction accuracies when using fitting coefficients as features. The additional features greatly enhanced the discrimination between compounds while performing PCA and also improved the prediction accuracy by 34% when using linear discrimination analysis.

Quantum Dot and Polymer Composite Cross-Reactive Array for Chemical Vapor Detection.

A cross-reactive chemical sensing array was made from CdSe Quantum Dots (QDs) and five different organic polymers by inkjet printing to create segmented fluorescent composite regions on quartz substrates. The sensor array was challenged with exposures from two sets of analytes, including one set of 14 different functionalized benzenes and one set of 14 compounds related to security concerns, including the explosives trinitrotoluene (TNT) and ammonium nitrate. The array was broadly responsive to analytes with different chemical functionalities due to the multiple sensing mechanisms that altered the QDs' fluorescence. The sensor array displayed excellent discrimination between members within both sets. Classification accuracy of more than 93% was achieved, including the complete discrimination of very similar dinitrobenzene isomers and three halogenated, substituted benzene compounds. The simple fabrication, broad responsivity, and high discrimination capacity of this type of cross-reactive array are ideal qualities for the development of sensors with excellent sensitivity to chemical and explosive threats while maintaining low false alarm rates.

Fluorescent polymer sensor array for detection and discrimination of explosives in water.

A fluorescent polymer sensor array (FPSA) was made from commercially available fluorescent polymers coated onto glass beads and was tested to assess the ability of the array to discriminate between different analytes in aqueous solution. The array was challenged with exposures to 17 different analytes, including the explosives trinitrotoluene (TNT), tetryl, and RDX, various explosive-related compounds (ERCs), and nonexplosive electron-withdrawing compounds (EWCs). The array exhibited a natural selectivity toward EWCs, while the non-electron-withdrawing explosive 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) produced no response. Response signatures were visualized by principal component analysis (PCA), and classified by linear discriminant analysis (LDA). RDX produced the same response signature as the sampled blanks and was classified accordingly. The array exhibited excellent discrimination toward all other compounds, with the exception of the isomers of nitrotoluene and aminodinitrotoluene. Of particular note was the ability of the array to discriminate between the three isomers of dinitrobenzene. The natural selectivity of the FPSA toward EWCs, plus the ability of the FPSA to discriminate between different EWCs, could be used to design a sensor with a low false alarm rate and an excellent ability to discriminate between explosives and explosive-related compounds.

Characterization of a cationic phosphonium surfactant for micellar electrokinetic chromatography: using the linear solvation energy relationships model.

A phosphonium surfactant is introduced as a pseudostationary phase for MEKC and its performance and selectivity are compared to that of an analogous ammonium surfactant. The linear solvation energy relationship model has been applied to the two cationic surfactants, allowing the contributions of five chemical factors to the interactions between solutes and the micelles to be evaluated. Differences in the pseudophases cohesivity and acid/base interactions were observed. Despite the significant differences observed in the solvation parameter results the two phases have remarkably similar electrophoretic properties, with the anodic EOF produced by the dynamic coating and the electrophoretic mobility of the two surfactants being statistically equal.

Cationic surfactants for micellar electrokinetic chromatography: 1. Characterization of selectivity using the linear solvation energy relationships model.

MEKC and the linear solvation energy relationship (LSER) model have been applied to two series of cationic surfactants. The synthetic flexibility of the quaternary ammonium group is exploited to generate the two series, one consisting of linear substitutions and the other incorporating the ammonium into ring structures of varying size. The effects of the head group structure on the CMC, aggregation number, and electrophoretic properties of the surfactants were determined. These surfactants were also characterized with the LSER model, which allowed the contributions of five chemical factors to the interactions between solutes and the micelles to be evaluated. Trends were observed in the cohesivity and polarity of the linear surfactant series, with both increasing with the size of the head group. No trends in the LSER parameters were observed in the cyclic series, but the LSER results do show that the surfactants with cyclic head groups provide a significantly different solvation environment from the linear series. Additional trends were observed in the aggregation behavior and chromatographic properties of the surfactants. These included changes in the CMCs, aggregation numbers, EOF, and electrophoretic mobility of the micelles that correlate to changes in head group size.

Cationic surfactants for micellar electrokinetic chromatography: 2. Representative applications to acidic, basic, and hydrophobic analytes.

Changes in MEKC chemical selectivity that are induced by changes in the headgroup structure of cationic surfactants are examined. Separations of acidic, basic, and hydrophobic solutes are examined. The acidic analytes are comprised of methoxyphenols, which are of interest due to their prevalence in wood smoke. The basic solutes consist of compounds often found in forensic urine analysis, and represent typical basic pharmaceuticals. The hydrophobic solutes are six pharmaceutical corticosteroids used in replacement therapy of adrenocortical insufficiency and nonspecific treatment of inflammatory and allergic conditions. The role of the headgroup was found to be quite significant when analyzing acidic compounds with not all the surfactants being able to resolve all of the analytes. The headgroup also induced migration order switches among the acidic analytes. All of the surfactants examined here in were found to be suitable for the analysis of basic analytes with each surfactant providing unique selectivity. The hydrophobic solutes were separated best with the larger more hydrophobic surfactant headgroups. The steroid separation with these two surfactants was achieved without the use of organic modifiers or a mixed micellar phase.

Electrokinetic chromatographic characterization of novel pseudo-phases based on N-alkyl-N-methylpyrrolidinium ionic liquid type surfactants.

MEKC and linear solvation free energy relationships (LSFERs) have been used to characterize the solute distribution between water and self-assemblies formed from ionic liquid type mono-chain cationic surfactants containing a cyclic pyrrolidinium head group. Several features of the solvation environment afforded by these micellar solutions were found to be quite different from that of CTAB, a structurally analogous cationic surfactant with a conventional, acyclic quaternary ammonium head group. None of the LSFER coefficients were found to vary in any systematic way with increasing alkyl chain length for these unique surfactants. Overall, however, these surfactants display different behavior than do all known cationic detergents such as CTAB. In chemical terms, pseudo-phases formed by these N-alkyl-N-methylpyrrolidinium bromides interact more strongly with polar compounds and less strongly with compounds having nonbonding or pi-electrons and are more cohesive compared to the well-studied CTAB.