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Two-dimensional MXenes are the newly emerging family of nanomaterials with competitive performance for nano-device development. Surface functional groups and abundant binding sites make these materials ideal candidates for sensor applications. Herein, we report the successful fabrication of a MXene-based nano-bio device for capturing, sensing, and filtering the Escherichia coli (E. coli) bacteria. Mannose carbohydrate, which binds strongly to E.coli's fimH protein via glucan multivalent interactions, is used as the bio-receptor element. MXene's structure was engineered to guarantee efficient E. coli capturing without mannose detachment. Electrochemical impedance spectroscopy (EIS) and cyclic... 

Metabonomics and other "omic" fields are essential science in analytical chemistry. Modern analytical instruments such as proton nuclear magnetic resonance (1H-NMR) can provide the great quantity of analytical information. In order to assign unknown samples, chemometric methods recognition build classification model based on experimental data. Firstly, some current strategies regarding disease diagnosis are exhibited in metabonomic studies. Some diseases such as crohn's disease can be difficult to diagnose since its signs and symptoms may be similar to other medical problems or often mimic other symptoms. Applications of NMR and supervised pattern recognition in the field of metabonomics are... 

Artificial neural networks were used to predict the oxidation peaks potentials of 7 monosaccharides under linear sweep voltammetry regime. Two sets of descriptors, one based on molecular properties calculated through DFT and another based on simple geometric distributions of hydroxyl groups and asymmetric carbon atoms along molecular chains, were employed to introduce the molecules to networks. Relatively, simple networks of (3,3,1) and (3,3,3,1) structures with the number of epochs not exceeding 15 through training process were capable of correctly predicting the peaks positions with R values in the range of 0.97-0.99. © 2008 Elsevier Ltd. All rights reserved