有机-有机界面效应的原位及非原位研究

有机-有机异质结构已被广泛应用于各种有机电子器件,包括有机发光二极管(OLEDs)、有机场效应晶体管(OFETs)和有机太阳能电池等。全面理解有机-有机异质结构的界面效应,对于器件的设计和性能优化具有重要意义。然而由于有机半导体具有多样的化学特性以及分子间较弱的范德华力,界面电荷传输特性与有机-有机电子结构、环境气氛等密切相关。在此,我们报道了随着顶层半导体并五苯(pentacene)的沉积,并五苯/酞菁氧钒(VOPc)异质结构的原位实时电学性能监测。结果显示,异质结构晶体管的p型迁移率从0.4 cm2∙V−1∙s^(−1)下降至0.2 cm2∙V−1∙s^(−1),而n型迁移率从0.01 cm2∙V−1∙s^(−1)迅速增加至约0.9 cm2∙V−1∙s^(−1)。这种n型输运行为的增强归因于pentacene向VOPc的界面电子转移效应以及由此导致的VOPc层中陷阱态的填充。此外,非原位实验对比表明,当晶体管制备过程暴露于大气时会明显抑制这种界面电荷转移效应,导致沉积pentacene后n型输运几乎没有得到改善。薄膜形态、开尔文探针力显微镜(KPFM)和X射线光电子能谱(XPS)的结果表明,界面处存在从pentacene到VOPc的电子转移。进一步的密度泛函理论(DFT)计算表明,由于pentacene/VOPc之间较强的相互作用,pentacene往VOPc的电荷转移量约为0.15 e。此外,O_(2)/H2O的存在会抑制这种界面电荷转移效应,这与我们的实验结果一致。本研究通过原位电学表征对有机-有机界面之间的电荷转移效应给出了深入解释,有利于进一步的器件性能优化及界面效应分析。

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems

At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability.But the BFO-based algorithm is easy to fall into local optimum.Therefore,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local optimization.Firstly,the binary matrix is constructed according to whether atoms are selected or not.And the support set of the sparse signal is recovered according to the BOMP-based algorithm.Then,the QBFO-based algorithm is used to obtain the estimated channel matrix.The optimization function of the least squares method is taken as the fitness function.Based on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria position.Simulation results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(MIMO)systems.Meanwhile,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.

超级电容器用二维Ti_3C_2T_x过渡金属碳/氮化物材料的研究进展

近年来二维过渡金属碳/氮化物(MXene)材料由于其独特的物理/化学性能,在储能领域引起广泛的关注。其中以二维Ti3C2Tx材料的研究最为普遍。MAX相是一类三元氮化物和/或碳化物,其化学式为Mn+1AXn (n=1～3),M代表过渡金属元素(如Sc, Ti, Zr, Hf, V, Nb, Ta, Cr, Mo等),X是碳和/或氮,A主要是IIIA或IVA族元素。根据n不同,MAX相的晶体结构包括3种类型。MAX相中,M-X和M-A键强度都很高。无法通过剪切或其它机械方法分层剥离。由于M-A键比M-X键具有更高的化学活性,可以通过化学刻蚀M-A键并辅助剥离方法获得单层/少层的MXenes材料。表面基团随机分布,对电化学性能有重要的影响。调控表面基团的种类和数量是当前研究的重要内容。本工作介绍了MXene相的基本结构,分析了相结构与性能的关系。总结了通过离子插入、热处理、表面改性、电极设计和元素掺杂等手段改善MXene相材料电化学性能的研究进展,简要介绍了MXenes与碳材料、氧化物、聚合物复合在超级电容器领域中的应用进展。对MXene相材料的结构、制备及电化学性能等方面进行了综述,指出了MXene相材料用于超级电容器领域存在的主要问题及未来的发展方向。

Site-Selective Polyolefin Hydrogenolysis on Atomic Ru for Methanation Suppression and Liquid Fuel Production

Catalytic hydrogenolysis of end-of-life polyolefins can produce value-added liquid fuels and therefore holds great promises in plastic waste reuse and environmental remediation.The major challenge limiting the recycling economic benefit is the severe methanation(usually>20%)induced by terminal C–C cleavage and fragmentation in polyolefin chains.Here,we overcome this challenge by demonstrating that Ru single-atom catalyst can effectively suppress methanation by inhibiting terminal C–C cleavage and preventing chain fragmentation that typically occurs on multi-Ru sites.The Ru single-atom catalyst supported on CeO_(2)shows an ultralow CH_(4)yield of 2.2%and a liquid fuel yield of over 94.5%with a production rate of 314.93 gfuels gRu^(−1)h^(−1)at 250℃for 6 h.Such remarkable catalytic activity and selectivity of Ru single-atom catalyst in polyolefin hydrogenolysis offer immense opportunities for plastic upcycling.