Specific Oxygenated Groups Enriched Graphene Quantum Dots as Highly Efficient Enzyme Mimics

Significant progress is achieved for the utilization of graphene quantum dots as enzyme mimics in various biomedical fields recently. Although promising, the biocatalytic performance is far from satisfactory. Here, the rational design and synthesis of specific oxygenated groups enriched graphene quantum dots (o‐GQDs) via a facile oxidation reflux route is reported. These well‐prepared o‐GQDs with uniform size exhibit an ultrahigh peroxidase‐like activity in a wide range of pH values, and their superior performance is verified by using glucose detection as a typical model. Compared with classical nanozymes, these o‐GQDs show multiple times higher enzymatic activity. It is believed that the super facile synthesis strategy can greatly facilitate the practical use of o‐GQDs as enzyme mimics in the future.     

A cross-layer design: energy efficient multilevel dynamic feedback scheduling in wireless sensor networks using deadline aware active time quantum for environmental monitoring

Recently, Packet scheduling plays a vital role in Wireless Sensor Networks (WSNs). The major key challenges include delay, packet dropping, energy consumption and lifetime due to constraints in energy and computing resources. All the research works on packet scheduling scheme in WSN uses only First Come First Served (FCFS) and Dynamic Multilevel Priority (DMP) schemes. FCFS works based on packet arrival time, it leads to starvation and high processing overhead for real-time packets. DMP works in multilevel with dynamic priority reduces the transmission overhead and bandwidth; it consumes more resources for real-time task leads to deadlock. To solve these problems, this work presents Multilevel Dynamic Feedback Scheduling (MDFS) algorithm. The sensor node classifies the emergency and normal data into three different ready queues named as high, medium and low priority, respectively. The queues are connected with a feedback mechanism; each packet from the sensor node has its own time quantum value based on the deadline. The updated time quantum value is compared with the boundary value of the queues, depends on the updated value the data packets are moved between queues with help of feedback mechanism. The simulation result proves that the projected MDFS outperforms in WSN environment.     

芘-邻香兰素类Cu(II)荧光传感器的制备及其性能

采用芘为荧光基团、肼为连接臂，根据席夫碱反应，制备芘-邻香兰素类Cu(II)荧光传感器（TM），其结构经熔点、1HNMR、13CNMR、IR、MS和元素分析表征。TM在乙腈体系中对Cu(II)有很好的紫外、荧光和比色响应，响应时间为12 min，不受其他金属离子干扰且对Cu(II)的识别可视、可逆。通过荧光滴定法计算出TM对Cu(II)的检出限为0.48 μmol/L。TM具有桔黄色的固体荧光，但其乙腈溶液荧光很弱，通过对比加水或Cu(II)的荧光强度，结合紫外和荧光滴定以及量子化学计算，探讨了TM的聚集诱导发光（AIE）识别机理。TM可用于自来水中Cu(II)的检测，回收率为91.2%~107.3%，相对误差为6.3%~8.8%。高选择性和对Cu(II)的快速响应使TM有望成为新的AIE型荧光传感器，用于实际样品中Cu(II)的检测。     

两种甲基丙烯酸二甲氨乙酯基季铵盐的合成及缓蚀性能

以甲基丙烯酸二甲氨乙酯为母体，分别以氯丙烯和氯化苄为桥连基设计合成了两种季铵盐（DMA-1和DMA-2）。利用红外光谱、核磁共振氢谱证实了所得产物与设计相一致。再依次通过失重实验、电化学实验、AFM、接触角等手段测试了Q235钢在含有DMA-1 和DMA-2的1mol/L盐酸中的缓蚀性能，探讨了其在Q235钢表面的吸附和缓蚀机理。结果显示：在较高温度下，DMA-1和DMA-2仍能牢固吸附在Q235钢表面，当所加入缓蚀剂的浓度为100 mg/L 时，缓蚀率均可达92.7%以上，显示出良好的缓蚀性能，且 DMA-1比DMA-2具有更好的缓蚀效果。热力学计算结果表明DMA-1和DMA-2在Q235钢表面的吸附是化学吸附，过程自发、放热，且符合Langmuir等温吸附，属于良好的阳极型缓蚀剂。量子化学计算结果表明了DMA-1更容易提供或接受电子，进一步研究了DMA-1和DMA-2的分子结构与活性位点。     

卤代效应对乙酸胍非线性光学性质的影响

针对具有应用价值的卤代乙酸胍（GXA）非线性光学（NLO）晶体研究不系统的问题，设计了系列三卤代和系列氯代的乙酸胍（GAc）体系，通过量子化学计算研究了系列GXA的结构、NLO性质和构效关系，探讨了卤代效应可能的影响机理。结果表明，卤代原子显著地改变了GXA分子构象，并通过不同方式参与强氢键作用来影响超分子堆积。卤代主要通过影响GXA分子的LUMO来改变其能隙（E_gap）。GXA的平均偶极矩（μ_total）和平均极化率<α>随卤原子数量以及半径的增大而增大。超极化率总体上相对于GAc降低，但影响复杂，其中，平均第1超极化率（β_total）随卤原子数量的增多而降低，平均第2超极化率（<γ>）则随卤原子的变大而显著增强。此外，并未发现E_gap与μ_total及<γ>之间存在明显的相关性。     

Controllable 1D Patterned Assembly of Colloidal Quantum Dots on PbSO4 Nanoribbons

Control of the 1D self‐assembly pattern of colloidal quantum dots (QDs) on PbSO₄ nanoribbon (NRb) templates is achieved. The internal structure of the NRbs is investigated by X‐ray diffraction, revealing the isotropic packing of the PbSO₄ nanoclusters in the NRbs. Colloidal QDs in a chloroform/hexane mixture are adsorbed onto the region close to the edges of the NRbs and form a 1D assembly of straight single line or double lines by controlling the amount of OAm. This is the first demonstration of a densely packed 1D self‐assembly of colloidal QDs with a straight line pattern without the use of any molecular bridge or adhesive. Atomic force microscopy measurements of the NRbs show depressions in the phase profile along the width of the NRbs, corresponding to the position of the 1D QD chain. The amount of adsorbed QDs on the NRbs in solution decreases as the addition of OAm increases, suggesting that additional OAm prevents interaction between the QDs and NRbs but facilitates the uniform adsorption of the 1D assembly. The low‐dimensional self‐assembly of colloidal QDs in this study opens up the possibility for the creation of anisotropically assembled QD superstructures.     

Organic Light-Emitting Diodes: Pushing Toward the Limits and Beyond

Organic light-emitting diodes (OLEDs) are established as a mainstream light source for display applications and can now be found in a plethora of consumer electronic devices used daily. This success can be attributed to the rich luminescent properties of organic materials, but efficiency enhancement made over the last few decades has also played a significant role in making OLEDs a practically viable technology. This report summarizes the efforts made so far to improve the external quantum efficiency (EQE) of OLEDs and discusses what should further be done to push toward the ultimate efficiency that can be offered by OLEDs. The study indicates that EQE close to 58% and 80% can be within reach without and with additional light extraction structures, respectively, with an optimal combination of cavity engineering, low-index transport layers, and horizontal dipole orientation. In addition, recent endeavors to identify possible applications of OLEDs beyond displays are presented with emphasis on their potential in wearable healthcare, such as OLED-based pulse oximetry as well as phototherapeutic applications based on body-attachable flexible OLED patches. OLEDs with fabric-like form factors and washable encapsulation strategies are also introduced as technologies essential to the success of OLED-based wearable electronics.     

Quantum Coin Method for Numerical Integration

Light transport simulation in rendering is formulated as a numerical integration problem in each pixel, which is commonly estimated by Monte Carlo integration. Monte Carlo integration approximates an integral of a black-box function by taking the average of many evaluations (i.e. samples) of the function (integrand). For N queries of the integrand, Monte Carlo integration achieves the estimation error of . Recently, Johnston [Joh16] introduced quantum super-sampling (QSS) into rendering as a numerical integration method that can run on quantum computers. QSS breaks the fundamental limitation of the convergence rate of Monte Carlo integration and achieves the faster convergence rate of approximately which is the best possible bound of any quantum algorithms we know today [NW99]. We introduce yet another quantum numerical integration algorithm, quantum coin (QCoin) [AW99], and provide numerical experiments that are unprecedented in the fields of both quantum computing and rendering. We show that QCoin's convergence rate is equivalent to QSS's. We additionally show that QCoin is fundamentally more robust under the presence of noise in actual quantum computers due to its simpler quantum circuit and the use of fewer qubits. Considering various aspects of quantum computers, we discuss how QCoin can be a more practical alternative to QSS if we were to run light transport simulation in quantum computers in the future.     

Automatic recommendation to appropriate log levels

A log statement is one of the key tactics for a developer to record and monitor important run-time behaviors of our system in a development phase and a maintenance phase. It composes of a message for stating log contents, and a log level (eg, debug or warn) to denote the severity of a message and controlling its visibility at run time. In spite of its usefulness, a developer does not tend to deeply consider which log level is appropriate in writing source code, which causes the system to be unmaintainable. To address this issue, this paper proposes an automatic approach to validating the appropriateness of the log level in consideration of the semantic and syntactic features and recommending a proper alternative log level. We first build the semantic feature vector to quantify the semantic similarity among application log messages using the word vector space, and the syntactic feature vector to capture the application context that surrounds the log statement. Based on the feature vectors and machine learning techniques, the log level is automatically validated, and an alternative log level is recommended if the log level is invalid. For the evaluation, we collected 22 open-source projects from three application domains, and obtained the 77% of precision and 75% of recall in validating the log levels. Also, our approach showed 6% higher accuracy than that of the developer group who has 7 to 8 years of work experience, and 72% of the developers accepted our recommendation.