Combinatorial Sensing Of Metal Ions Based On Gold Nanoparticle-dye Array

With the development of society and technology, people have higher demands forquality of life. However, the problem of environmental pollution has been a seriousthreat to public life and health, such as heavy metal ions in water. So, fast and effectivedetection of harmful pollutants is very important. Conventional sensors rely on a “lockand key” recognition principle, they owe a high specificity and selectivity. But thedesign of such sensors is time-consuming and sensitive material is difficult to get.Combinatorial sensing, as a new strategy of chemical and biological sensing that attractsgreat attention, has recently been developed in order to improve the sensor selectivityand to achieve multiplex detection, or in case of specific receptors difficult to obtain.The mechanism underlying the combinatorial sensing relies on: the target differentiallyinteracts with a series of sensitive materials, or the target competes with the componentsin multiple interaction equilibria, leading to a differential or competitive patternconsisting of a group of distinct responses. The combinatorial sensing based on anon-specific receptor array and pattern analysis enables the sensor design be moreflexible and simple, displaying a great potential for practical applications such asenvironmental monitoring and food safety.First, we have developed a novel combinatorial array composed of goldnanoparticle and dyes. An array of cross-reactive receptors which were prepared bydifferent combinations of building blocks created a characteristic pattern for eachcomponent. As a result, this array didn’t need to be highly specific for any analytes.Based on this concept, we designed an array for pattern recognition of metal ions.Dye-adsorbed gold nanoparticle possesses two specific absorption peaks of goldnanoparticle and dye in visible spectral region. The characteristic spectrum is related tothe dye structure, pH and metal ion. Through selecting different dyes and pH, wefabricated a sensor array with a series of characteristic spectra. The sensor array hasdifferential binding of metal ions. So the serial characteristic spectra could be changedwhen the sensor array interacts with each metal ion, generating a visible spectral patternof AuNPs-dye-metal ion. As a result, the sensor array, AuNPs respectively combinedwith different dyes (RB, MG, MB) in pH6.6,7.2and7.8.Then, The AuNPs-BD array was used for recognition of individual metal ions andbinary mixtures of metal ions by combination with pattern recognition, respectively. For the discrimination of Hg2+, Cu2+, Ni2+, Pb2+, Zn2+, Cd2+, Fe2+and Mn2+, The sensornumber in the array was optimally reduced to three by Principal Component Analysis(PCA). The result shows that we can recognize eight metal ions by an optimized sensorarray consisted of3sensors (AuNPs-RB, AuNPs-MG, AuNPs-MB in pH7.8).Furthermore, the binary mixtures of metal ions (Pb2+、Zn2+、Cd2+、Mn2+) werediscriminated in satisfaction. The thesis also discusses application of two kinds ofcommonly pattern recognition method, the principal component analysis (PCA) andlinear discriminant analysis (LDA), in data analysis. Signal response patterns wereanalyzed by utilizing PCA and LDA in the experiment, through the comparison of theexperimental results, LDA had more advantages in recognizing individual metal ionsand binary mixtures of metal ions compared to PCA.