Coupling nanomaterials with biomolecular recognition events represents a new direction in nanotechnology toward the development of novel molecular diagnostic tools. Here a graphene oxide (GO)‐based multicolor fluorescent DNA nanoprobe that allows rapid, sensitive, and selective detection of DNA targets in homogeneous solution by exploiting interactions between GO and DNA molecules is reported. Because of the extraordinarily high quenching efficiency of GO, the fluorescent ssDNA probe exhibits minimal background fluorescence, while strong emission is observed when it forms a double helix with the specific targets, leading to a high signal‐to‐background ratio. Importantly, the large planar surface of GO allows simultaneous quenching of multiple DNA probes labeled with different dyes, leading to a multicolor sensor for the detection of multiple DNA targets in the same solution. It is also demonstrated that this GO‐based sensing platform is suitable for the detection of a range of analytes when complemented with the use of functional DNA structures.
A fluorescence sensor for Ag(I) ions is developed based on the target-induced conformational change of a silver-specific cytosine-rich oligonucleotide (SSO) and the interactions between the fluorogenic SSO probe and graphene oxide.
Graphene is a particularly useful nanomaterial that has shown great promise in nanoelectronics. Because of the ultrahigh electron mobility of graphene and its unique surface properties such as one-atom thickness and irreversible protein adsorption at surfaces, graphene-based materials might serve as an ideal platform for accommodating proteins and facilitating protein electron transfer. In this work, we demonstrate that graphene oxide (GO) supports the efficient electrical wiring the redox centers of several heme-containing metalloproteins (cytochrome c, myoglobin, and horseradish peroxidase) to the electrode. Importantly, proteins retain their structural intactness and biological activity upon forming mixtures with GO. These important features imply the promising applications of GO/protein complexes in the development of biosensors and biofuel cells.
We have developed a surface-enhanced Raman scattering (SERS)-active substrate based on gold nanoparticle-decorated chemical vapor deposition (CVD)-growth graphene and used it for multiplexing detection of DNA. Due to the combination of gold nanoparticles and graphene, the Raman signals of dye were dramatically enhanced by this novel substrate. With the gold nanoparticles, DNA capture probes could be easily assembled on the surface of graphene films which have a drawback to directly immobilize DNA. This platform exhibits extraordinarily high sensitivity and excellent specificity for DNA detection. A detection limit as low as 10 pM is obtained. Importantly, two different DNA targets could be detected simultaneously on the same substrate just using one light source.
A gold nanoprobe that can respond colorimetrically to Hg(2+) is designed and coupled with a power-free PDMS device; the system can be used for rapid and visual detection of low micromolar Hg(2+) in real environmental samples.
The synthesis of single‐crystalline diamond nanorods (with diameters of 4–8 nm and with lengths up to 200 nm) via the hydrogen plasma post‐treatment of multiwalled carbon nanotubes is described. The diamond nanorods (see Figure) are identified as having a core–sheath structure with the inner core being diamond crystal and the outer shell being composed of amorphous carbon. A growth mechanism for diamond nanorods is proposed.
The split equality problem has extraordinary utility and broad applicability in many areas of applied mathematics. Recently, Moudafi proposed an alternating CQ algorithm and its relaxed variant to solve it. However, to employ Moudafi's algorithms, one needs to know a priori norm (or at least an estimate of the norm) of the bounded linear operators (matrices in the finite-dimensional framework). To estimate the norm of an operator is very difficult, but not an impossible task. It is the purpose of this paper to introduce a projection algorithm with a way of selecting the stepsizes such that the implementation of the algorithm does not need any priori information about the operator norms. We also practise this way of selecting stepsizes for variants of the projection algorithm, including a relaxed projection algorithm where the two closed convex sets are both level sets of convex functions, and a viscosity algorithm. Both weak and strong convergence are investigated.
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