Development of ionic liquid (IL)-based colorimetric sensor arrays for detection and identification of chemicals in both the aqueous and vapor phases is reported. These facile and inexpensive optoelectronic sensors were fabricated by using ionic liquids (ILs) derived from readily available pH indicator dyes. A series of 12 different chemosensory ILs were synthesized by pairing anionic pH indicator dyes with trihexyl(tetradecyl)phosphonium ([P 66614 ]) cation via an ion exchange reaction. The incorporation of the [P 66614 ] cation imparted hydrophobic characteristics to these ILs, and this induced hydrophobicity led to their desired low solubility in aqueous solutions, as well as eliminated the need for a specialized hydrophobic matrix/substrate for immobilization. In this manuscript, four different matrices, i.e. glass microfiber filter papers, cotton threads, silica thin layer chromatography (TLC) plates, and alumina TLC plates, were employed for fabrication of sensor arrays. These sensor arrays were used to analyze pH values of aqueous solutions as well as for detection of acidic and basic vapors. To further prove the applicability of these IL sensor arrays as tools to sense closely related complex materials, the arrays were applied to successful discrimination of aqueous solutions of smoke from three commercially available cigarettes. The digital data generated from these sensor arrays were used in developing predictive models for accurately identifying various analytes. Two approaches were used for developing the models, and two methods were applied for assessing the predictive accuracy of the models. Use of cotton threads as a matrix led to development of a more flexible, low volume, and lightweight array to estimate pH and detect a variety of vapors. These wearable arrays may possibly be incorporated into bandages, sweatbands, diapers, and similar systems. Overall, these IL-based sensor arrays should provide a new research direction in the development of advanced colorimetric sensor arrays for detection and identification of a range of analytes relevant to many different applications.
There is a continuing need to develop high-performance sensors for monitoring organic solvents, primarily due to the environmental impact of such compounds. In this regard, colorimetric sensors have been a subject of intense research for such applications. Herein, we report a unique virtual colorimetric sensor array based on a single ionic liquid (IL) for accurate detection and identification of similar organic solvents and mixtures of such solvents. In this study, we employ eight alcohols and seven binary mixtures of ethanol and methanol as analytes to provide a stringent test for assessing the capabilities of this array. The UV-visible spectra of alcoholic solutions of the IL used in this study show two absorption bands. Interestingly, the ratio of absorbance for these two bands is found to be extremely sensitive to alcohol polarity. A virtual sensor array is created by using four different concentrations of IL sensor, which allowed identification of these analytes with 96.4-100% accuracy. Overall, this virtual sensor array is found to be very promising for discrimination of closely related organic solvents.
Sensitive and selective detection of proteins from complex samples has gained substantial interest within the scientific community. Early and precise detection of key proteins plays an important role in potential clinical diagnosis, treatment of different diseases, and proteomic research. In the study reported here, six different compounds belonging to a group of uniform materials based on organic salts (GUMBOS) have been synthesized using three thiacarbocyanine (TC) dyes and employed as fluorescent sensors. Fluorescence properties of micro- and nanoaggregates of these TC-based GUMBOS formed in phosphate buffer solutions are studied in the absence and presence of seven proteins. Fluorescence response patterns of these TC-based GUMBOS were analyzed by linear discriminant analysis (LDA). The constructed LDA model allowed discrimination of these seven proteins at various concentrations with 100% accuracy. The sensing and discrimination abilities of these TC-based GUMBOS were further evaluated in mixtures of two major proteins, i.e., human serum albumin and hemoglobin. Fluorescence response patterns of these mixtures were analyzed by LDA. This model allowed discrimination of various mixtures with 100% accuracy. Moreover, spiked urine samples were prepared and the responses of these sensors were collected and analyzed by LDA. Remarkably, discrimination of these seven proteins was also achieved with 100% accuracy.
We report a sensor array approach, based on a novel group of 6-(p-toluidino)-2-naphthalenesulfonate (TNS)-based organic salts, for sensitive and label-free sensing of proteins.
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