Dual‐signal amplification strategy for ultrasensitive chemiluminescence detection of PDGF–BB in capillary electrophoresis

Many efforts have been made toward the achievement of high sensitivity in capillary electrophoresis coupled with chemiluminescence detection (CE‐CL). This work describes a novel dual‐signal amplification strategy for highly specific and ultrasensitive CL detection of human platelet‐derived growth factor–BB (PDGF–BB) using both aptamer and horseradish peroxidase (HRP) modified gold nanoparticles (HRP–AuNPs–aptamer) as nanoprobes in CE. Both AuNPs and HRP in the nanoprobes could amplify the CL signals in the luminol–H₂O₂ CL system, owing to the excellent catalytic behavior of AuNPs and HRP in the CL system. Meanwhile, the high affinity of aptamer modified on the AuNPs allows detection with high specificity. As proof‐of‐concept, the proposed method was employed to quantify the concentration of PDGF–BB from 0.50 to 250 fm with a detection limit of 0.21 fm. The applicability of the assay was further demonstrated in the analysis of PDGF–BB in human serum samples with acceptable accuracy and reliability. The result of this study exhibits distinct advantages, such as high sensitivity, good specificity, simplicity, and very small sample consumption. The good performances of the proposed strategy provide a powerful avenue for ultrasensitive detection of rare proteins in biological sample, showing great promise in biochemical analysis. Copyright © 2015 John Wiley & Sons, Ltd.     

The Improvement of Performance for Continuous-Variable Quantum Key Distribution with Imperfect Gaussian Modulation

Gaussian modulation is one of the key steps for the implementation of continuous-variable quantum key distribution (CVQKD) schemes. However, imperfection in the Gaussian modulation may introduce modulation noise that can deteriorate the performance of CVQKD systems. In this paper, we mainly investigate how to improve the performance of a CVQKD system from different aspects. First, we explore the several different origins, impacts and monitoring schemes for the modulation noise in detail. Then, we discuss the practical performance of a CVQKD system with an untrusted noise model and neutral party model, respectively. These analyses indicate that the neutral party model should be reasonably regarded as a general noise model, which will passively and greatly raise the performance of the system. Further, we propose a dynamic auto-bias control scheme to actively resist the modulation noise which comes from the drift of bias point of the amplitude modulator. Together these methods contribute to the improvement of the practical performance of CVQKD systems with imperfect Gaussian modulation.

     

Transition-Metal Oxides/Carbides@Carbon Nanotube Composites as Multifunctional Electrocatalysts for Challenging Oxidations and Reductions

The rapid development of renewable-energy technologies such as water splitting, rechargeable metal–air batteries, and fuel cells requires highly efficient electrocatalysts capable of the oxygen-reduction reaction (ORR) and the oxygen-evolution reaction (OER). Herein, we report a facile sonication-driven synthesis to deposit the molecular manganese vanadium oxide precursor [Mn₄V₄O₁₇(OAc)₃]³⁻ on multiwalled carbon nanotubes (MWCNTs). Thermal conversion of this composite at 900 °C gives nanostructured manganese vanadium oxides/carbides, which are stably linked to the MWCNTs. The resulting composites show excellent electrochemical reactivity for ORR and OER, and significant reactivity enhancements compared with the precursors and a Pt/C reference are reported. Notably, even under harsh acidic conditions, long-term OER activity at low overpotential is reported. In addition, we report exceptional activity of the composites for the industrially important Cl₂ evolution from an aqueous HCl electrolyte. The new composite material shows how molecular deposition routes leading to highly active and stable multifunctional electrocatalysts can be developed. The facile design could in principle be extended to multiple catalyst classes by tuning of the molecular metal oxide precursor employed.     

共轭线性对称性及其对PT-对称量子理论的应用

传统量子系统的哈密顿是自伴算子,哈密顿的自伴性不仅保证系统遵循酉演化和保持概率守恒,而且也保证了它自身具有实的能量本征值,这类系统称为自伴量子系统.然而,确实存在一些物理系统(如PT-对称量子系统),其哈密顿不是自伴的,这类系统称为非自伴量子系统.为了深入研究PT-对称量子系统,并考虑到算子PT的共轭线性性,首先讨论了共轭线性算子的一些性质,包括它们的矩阵表示和谱结构等;其次,分别研究了具有共轭线性对称性和完整共轭线性对称性的线性算子,通过它们的矩阵表示,给出了共轭线性对称性和完整共轭线性对称性的等价刻画;作为应用,得到了关于PT-对称及完整PT-对称算子的一些有趣性质,并通过一些具体例子,说明了完整PT-对称性对张量积运算不具有封闭性,同时说明了完整PT-对称性既不是哈密顿算子在某个正定内积下自伴的充分条件,也不是必要条件.     

LC‐MS/MS analysis of Gegen Qinlian Decoction and its pharmacokinetics after oral administration to rats

A liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) method was developed and validated for the simultaneous determination of 12 constituents of Gegen Qinlian Decoction (GQD), namely puerarin, daidzein, baicalin, wogonoside, wogonin, liquiritin, liquiritigenin, berberine, jatrorrhizine, palmatine, coptisine and glycyrrhetic acid, in rat plasma. The plasma samples were spiked with the internal standard (IS) carbamazepine acidified with HCl and extracted by liquid–liquid extraction with ethyl acetate. Chromatographic separation was achieved on a Shiseido Capcell PAK C₁₈ column utilizing a gradient elution profile and a mobile phase consisting of (A) 0.1% formic acid in water and (B) acetonitrile. Detection was performed in the multiple reaction monitoring mode using electrospray ionization in the positive ion mode at a flow rate of 0.3 mL/min and a run time of 8 min. All of the calibration curves gave good linearity (r > 0.9930) over the concentration range from 0.6–360 to 16.2–9720 ng/mL for all components. The intra‐ and inter‐day precisions were <15.0% in terms of the relative standard deviation, and the accuracies were within ±13.7% in terms of the relative error. The method was successfully applied to investigate the pharmacokinetics of the major active compounds of Gegen Qinlian Decoction after its oral administration to rats. Copyright © 2014 John Wiley & Sons, Ltd.     

Neural network representations of quantum many-body states

Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.