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Herein we introduce the first successful assay of biomolecule by in situ selective crystallization based quartz crystal microbalance (QCM). Selective crystallization of CaCO3 on QCM sensor surface was utilized as an efficient mass amplification strategy and enhanced the sensitivity of QCM significantly. High specificity is guaranteed by the cooperation of two functional groups: −N(CH3)3 and −COOH. Passivation of sensor surface is realized by self-assembly of −N(CH3)3, which effectively inhibited the nonspecific crystallization. The DNA target is detected through hybridization of probe DNA labeled with −COOH, which can effectively promot the in situ surface crystallization of CaCO3. The concentration of target DNA is reflected by the frequency shift of QCM which is directly induced by the surface crystallization. The selective crystallization based QCM platform is simple, straightforward, high sensitive, and high specific. We demonstrate the excellent LOD (2 aM DNA) and a linear range of 10aM to 1 nM for D...

The surface crystallization of CaCO3 on gold was monitored by a quartz crystal microbalance (QCM). Quantitative control of the grown crystals was realized by adjusting the ratio of two functional groups, -N(CH3)3 and -COOH, on SAMs. Crystals with uniform size, morphology and polymorphism were obtained. The amount of crystals formed was found to increase with an increase in the -COOH group. The proposed quantitative control of crystallization can be an effective mass amplification strategy for QCM to enhance its assay sensitivity.

(1) Background: For realizing an effective flood detection of Synthetic Aperture Radar (SAR) images in water regions, a post-classification comparison algorithm with Multi-depth Flood Detection Convolutional Neural Network (MDFD-CNN) is proposed in this paper. The MDFD-CNN is used to classify and extract the water regions in the SAR image, aiming at solving the flood detection after high-resolution SAR water regions extraction in complex terrain. (2) Methods: What the MDFD-CNN owning a two-branches network with different depth is used for extracting respectively the water regions in the bi-temporal SAR images using the saliency detection. In the process of recognizing the water regions, to improve the reliability of the training samples and reduce the number of training samples, MDFD-CNN uses the SAR backscatter coefficient significance detection to obtain the salient regions of the extracted training samples. Moreover, a piecewise back-propagation is adopted to optimize the network. With the detected water regions of the bi-temporal images, a post-classification comparison is implemented for detecting the changes in water regions. (3) Results: Compared with the manually extracted features used by conventional methods, what the MDFD-CNN extracted is more robust. Meanwhile, experimental results illustrates the effectiveness, especially strong noise immunity, of the proposed method. (4) Conclusions: The proposed method offers a valid way for flood detection in water regions of SAR images in realistic environments.

Casing failure is a common problem in oilfield. This paper studies on cement loop damage during the repair of casing failure well based on the theories of composite materials mechanics, interface mechanics and damage mechanics, and a composite model was established. Through the model, the distributions of stress and displacement fields were obtained. Moreover, the influences of friction coefficient on interface were also studied. And the cement loop failure length is also determined. Finally, the case shows that the model is suitable for repairing casing failure better.