Enzyme-Free Amplification Strategy for Biosensing Using Fe3+-Poly(glutamic acid) Coordination Chemistry.

In this work, we outline a signal amplification strategy using the coordination chemistry between Fe3+ and poly(glutamic acid) (PGA) for biosensing applications. The theoretical calculation based on density functional theory shows that PGA has a much higher binding affinity with Fe3+ than the other metal ions. Guided by this rationale, we prepare a PGA-mediated signal probe through conjugating PGA onto polystyrene (PS) nanoparticles to form a brushlike nanostructure for Fe3+ coordination. This PGA-PS brush (PPB) has a large loading capacity of Fe3+ with a number of 1.92 × 108 Fe atoms per nanoparticle that greatly amplifies the signals for assays in an enzyme-free way. Combined with ferrozine coloration-based readout, this PPB-mediated amplification is further applied for the enzyme-free immunoassay that shows an ultrahigh sensitivity for detection of microcystins-LR (12 pg/mL), a 5-fold enhancement compared with that of traditional enzyme-linked immunosorbent assay (ELISA) (60 pg/mL). In addition, the good stability, rapid response, and long shelf life make this enzyme-free amplification strategy a promising platform for point-of-care biosensing applications.

[Mapping environmental vulnerability from ETM + data in the Yellow River Mouth Area].

The environmental vulnerability retrieval is important to support continuing data. The spatial distribution of regional environmental vulnerability was got through remote sensing retrieval. In view of soil and vegetation, the environmental vulnerability evaluation index system was built, and the environmental vulnerability of sampling points was calculated by the AHP-fuzzy method, then the correlation between the sampling points environmental vulnerability and ETM + spectral reflectance ratio including some kinds of conversion data was analyzed to determine the sensitive spectral parameters. Based on that, models of correlation analysis, traditional regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the spectral reflectance and the environmental vulnerability. With this model, the environmental vulnerability distribution was retrieved in the Yellow River Mouth Area. The results showed that the correlation between the environmental vulnerability and the spring NDVI, the September NDVI and the spring brightness was better than others, so they were selected as the sensitive spectral parameters. The model precision result showed that in addition to the support vector model, the other model reached the significant level. While all the multi-variable regression was better than all one-variable regression, and the model accuracy of BP neural network was the best. This study will serve as a reliable theoretical reference for the large spatial scale environmental vulnerability estimation based on remote sensing data.