Gold catalysts have attracted considerable interests since the extraordinary activity of gold for the oxidation of CO at low temperature was reported. 1-7 Especially, a variety of highly active gold catalysts have been pursued for a series of important industrial reactions. 2,8 Therefore, it has become crucial in the field of catalysis to develop innovative approaches allowing rapid evaluation of the activity by screening a large diversity of catalyst candidates for a specific catalytic reaction. An intensive effort is currently devoted toward the development of high-throughput screening approaches, such as IR thermography, 9 laser-induced fluorescence imaging, 10,11 and resonance-enhanced multiphoton ionization. 12 Although those techniques are very elegant, a simple and straightforward approach should be exploited to achieve rapid screening for catalytic activity of gold catalysts.The catalytic reaction of some compounds is accompanied by chemiluminescence (CL), which has been studied by many research groups. [13][14][15] In our previous study, we have also observed the CL emission of many analytes during catalytic reaction on nanomaterials and designed a series of sensors for measuring alcohols, amines, thiols, and other compounds. 16 Moreover, different morphology and components of catalytic nanomaterials led to different CL responses. 16b We have also concluded that luminescent efficiencies and spectral shapes of the CL depended on the kinds of reactants and catalysts. 16a Recently, it was found that the CL intensity was correlated with the yield of acetaldehyde from ethanol oxidation over a Ce 1-x Zr x O 2 catalyst. 16c Those experimental results indicate that the CL is closely related to properties of the reactants and the catalysts. Therefore, it is reasonable to establish a new and rapid approach for evaluating and screening the catalysts based on the CL performance of corresponding catalytic reactions.In this communication, the proof of principle of the CL screening method has been presented by evaluating the catalytic activity of a gold catalyst on the CO conversion as the model. We measured the CL responses of CO oxidation on oxide-supported gold catalysts and correlated the CL features with the catalytic activity for the CO conversion. Moreover, an array was designed for the CL imaging by spotting different gold catalysts on a chip. The brightness of the image is consistent with the corresponding catalytic activities of those catalysts.A variety of the commercially available oxide supports, including acidic oxide SiO 2 , basic oxide ZnO and MgO, and other oxide TiO 2 and ZrO 2 , which have been extensively studied for the gold catalysis in converting CO to CO 2 , were chosen for preparing the supported gold catalysts because these supports displayed different effect on the activity of the gold catalyst. 4 The representative TEM images of these supported gold catalysts prepared with a colloidal deposition method are shown in Figure S1. Size distribution of the gold nanoparticles (NPs) is homogeneous...

We report a simple and novel colorimetric sensor array for rapid identification of microorganisms. In this study, four gold nanoparticles (AuNPs) with diverse surface charges were used as sensing elements. The interactions between AuNPs and microorganisms led to obvious color shifts, which could be observed by the naked eye. A total of 15 microorganisms had their own response patterns and were differentiated by linear discriminant analysis (LDA) successfully. Moreover, microorganism mixtures could also be well discerned. The method is simple, fast (within 5 s), effective, and visual, showing the potential applications in pathogen diagnosis and environmental monitoring.

A cross-reactive chemiluminescence (CL) sensor array based on catalytic nanomaterials was constructed for the discrimination and identification of flavors in cigarettes. A total of 21 nanomaterials, including metal oxides, metal oxides deposited on carbon nanotubes (CNTs), gold nanoparticles deposited on metal oxides, and carbonate, have been carefully selected as sensing elements of the array. Each flavor gives its unique CL pattern from the array, which is able to be employed for the discrimination and identification of flavors. Hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) were used to analyze the patterns. The obtained CL patterns are temperature dependent, thus additional discrimination power could be provided by changing the working temperature of the array. Quantification of the flavors has been performed according to the emission intensity on the specific sensing element. The linear ranges of the sensor array for the flavors are in the range of 20-2000 ppmv with the limits of detection below 10 ppmv, which vary with the kinds of flavors. Six brands of cigarettes have been discriminated by their CL patterns obtained with the present sensor array. The robust and reversible response of this array, combined with its simple instrumentation, indicates the promise of this array for real world application.

The efficient identification of bacteria is of considerable significance in clinical diagnosis. Herein, a novel colorimetric sensor array was developed for the detection and identification of bacteria based on the specific affinity and electrostatic interaction between Wulff-type 4-mercaptophenylboronic acid (MPBA)-mercaptoethylamine (MA) cofunctionalized AgNPs (MPBA-MA@AgNPs) and bacteria at various pH. In the neutral and alkaline conditions, AgNPs tended to be dispersed due to the specific affinity between cis-diol residues contained in carbohydrate-rich compositions on the bacterial cell surface and MPBA. Bacterial cells have different carbohydrate compositions on their surface. The differential binding affinity of MPBA on the surface of AgNPs to cis-diol residues of various carbohydrates resulted in a different color change of AgNPs, which could be tuned by pH. On the contrary, AgNPs tended to be aggregated due to the electrostatic interaction between positively charged MA and negatively charged bacteria under acidic conditions. Therefore, using various pH buffer solutions as the sensing elements and MPBA-MA@AgNPs as the indicator, bacteria could be differentiated from each other by their own color response patterns. Moreover, the complex bacteria mixtures could be well discriminated. The method is simple, efficient, and visual and has a potential application in pathogen diagnosis.