Self-powered high-performance topological crystalline insulators tin selenide/silicon dioxide/silicon heterojunction broadband photodetectors for weak signal detection

Topological crystalline insulators have been demonstrated to possess a broadband absorption spectrum owing to the gapless topological surface states and narrow bulk band gap, which are promising candidates for new generation optoelectronic devices application. Herein, we fabricated a high-quality topological crystalline insulator tin selenide (SnSe)/SiO₂/Si heterostructure using a simple magnetron sputtering method. Our strategy is to build high-quality heterojunctions on silicon wafers by directly growing films to achieve sufficiently large interfacial barriers, enhance the photovoltaic effect of heterojunctions, and make heterojunctions achieve better photo response characteristics. At zero voltage, the current of heterojunction varies from extremely low dark current (∼10⁻¹⁰ A) to high photocurrent (∼10⁻⁵ A). These advantages make the SnSe/SiO₂/Si heterostructure show a superior photoresponsivity of 39.2 AW⁻¹, an excellent detectivity (D*) of 4.2 × 10¹⁶ cmHz^1/2W⁻¹, a large broadband photoresponse from 365 nm–980 nm and a fast response speed of approximately microseconds. The over-all properties have greatly exceeded those of the reported 2D-based vertical VDW heterojunctions, those 2D film/Si heterojunctions and other Topological insulators/Si heterojunctions photodetectors respecting D*, on-off ratio, bandwidth etc. The SnSe/SiO₂/Si heterojunctions will provide more chance for future optoelectronic devices applications.     

Resistive random access memory based on gallium oxide thin films for self-powered pressure sensor systems

The resistive switching (RS) behavior of a gallium oxide (Ga₂O₃) thin film for use in resistive random access memory (RRAM) was investigated. Ta/Ga₂O₃/Pt memory devices exhibited favorable RS behavior, such as a small distribution of switching parameters and switching cycles of more than 3 × 10⁶. X-ray photoelectron spectroscopy and the current transport mechanism indicated that that the RS behavior was attributed to the local variation on the Schottky barrier near the Pt electrode interface due to oxygen vacancies. A hybrid system for self-powered data storage and deletion was built by combining the RRAM device with a commercial Pb(Zr_1-xTi_x)O₃ piezoelectric ceramic as a pressure sensor/power generator. The excellent anti-interference and reuse performance of the system indicated promising potential for the application of this memory device.     

Zero-biased and visible-blind UV photodetectors based on nitrogen-doped ultrananocrystalline diamond nanowires

Taking advantage of the superflat surface of ultrananocrystalline diamond (UNCD), highly precise UNCD nanowire (NW) arrays were fabricated to develop high-performance UV photodetectors. The large surface-to-volume ratio of a nanowire significantly increases the number of surface trap states, and the reduced dimensionality effectively confines the active area of the charge carrier and shortens the transit time, which results in an enhanced photoconductivity and response speed. In this paper, the zero-biased UV photodetectors based on nitrogen-incorporated ultrananocrystalline diamond nanowire arrays have been demonstrated and characterized. The estimated responsivity was 2.0 A/W for 350 nm incident light when the device operated at room temperature. The UVA and UVB photocurrent signals from this visible blind photodetector were well defined with a rise and decay time of less than 1 s.     

In-situ generation Cu2O/CuO core-shell heterostructure based on copper oxide nanowires with enhanced visible-light photocatalytic antibacterial activity

As visible light-driven photocatalysts in wastewater treatment, Cu₂O/CuO composites have garnered considerable attention. Herein, Cu₂O/CuO core–shell nanowires were fabricated directly on a Cu mesh using a simple two-step synthesis process involving a wet chemical method and rapid annealing. Unlike conventional composite nanowires, controllable core–shell nanowires exhibit high photoelectrochemical properties and overcome the problems associated with the recovery of powder-based photocatalysts. The presence and structural distribution of the Cu₂O/CuO core–shell nanowires were confirmed using X-ray diffraction, X-ray photoelectron spectroscopy and transmission electron microscopy. Among the samples subjected to different rapid annealing temperatures for 180 s, the sample exposed to rapid annealing at 350 °C achieved the highest photocurrent density of ?6.96 mA cm^?2. In the core–shell nanowires fabricated on the samples, the ratio of Cu₂O/CuO was 1:1. The photocatalytic activity of the Cu₂O/CuO nanowire samples was also determined by measuring methyl blue degradation to determine their applicability in wastewater treatment. A remarkable photocatalytic degradation rate of 91.6% was achieved at a loading bias voltage of ?0.5 V. The Cu₂O/CuO heterojunction enhanced the photodegradation of the samples because the different bandgaps improved the dissociation of the photogenerated electron–hole pairs. Furthermore, the antibacterial activity of the Cu₂O/CuO nanowires exhibited considerable resistance against Escherichia coli and photocatalytic antibacterial treatment for only 20 min under visible light killed 10⁶ CFU/mL of E. coli. Therefore, the Cu₂O/CuO controllable core–shell nanowires with a high photodegradation performance and excellent antibacterial activity under general illumination show diverse applications in water treatment.     

基于GPS/GIS的公共安全监测系统的研究

GPS技术在公共安全监测领域中的应用正向高精度、实时化和自动化的方向发展。GPS公共安全监测不仅注重结果,还要分析产生变形的原因。要实现这一目标,就需要借助于G IS的空间分析功能,即需要建立一种基于GPS/G IS的公共安全监测系统。研究了GPS监测的似单差数学模型和算法流程,由于该模型只用到L1载波相位观测值及伪距,因此可适用于单频接收机,从而降低监测成本。同时,结合GPS公共安全监测的特点,提出了GPS/G IS公共安全监测系统的技术路线。     

基于约翰逊转换的鲁棒化工过程监控方法

化工厂中一个小故障可能导致大事故,从而造成生命财产损失和环境破坏。为了防止小故障演变成大事故,化学工业需要有效的过程监控来及时检测故障和诊断故障原因。传统化工过程监控方法主元分析法(Principal Component Analysis, PCA)假设数据服从高斯分布,实践中有时并不满足该条件。此外,其使用方差、协方差捕捉数据非线性变化时,鲁棒性较差。本工作提出一种改进的主元分析法—基于约翰逊转换的鲁棒过程监控方法。首先引入约翰逊正态转换(Johnson Transformation)使过程数据服从高斯分布;其次使用鲁棒性强的斯皮尔曼相关系数(Spearman Correlation Coefficient)矩阵代替传统主元分析法的协方差矩阵提取特征向量,构造特征空间;最后将过程数据投影到特征空间,使用T2和SPE统计量实施过程监控。将此方法应用于TE过程故障案例,并与PCA和核主元分析法(Kernel Principal Component Analysis, KPCA)对比,验证了此方法的有效性。     

基于KECA的化工过程故障监测新方法

针对化工过程数据复杂、非线性的特点,提出一种基于核熵成分分析(KECA)的化工过程故障监测算法。首先,KECA算法按照Renyi熵值的大小选取特征值及特征向量,相比传统的KPCA监测算法,其保留主元个数更少,可以有效减少运算量。同时,仿真研究表明KECA算法选取的主元具有角度结构特性,据此,提出一种新的统计量--CS(Cauchy-Schwarz)统计量,其对应到核特征空间中即为向量间的角度余弦值,可以较好表述不同概率密度分布之间的相似度。最后,将KECA和KPCA算法分别应用于TE(Tennessee Eastman)过程,结果表明KECA在故障检测延迟与检出率相比KPCA都有很大的优势。     

基于支持向量数据描述的非高斯过程故障重构与诊断

提出一种基于支持向量描述（SVDD）的统计过程监控与故障重构及诊断算法,避免了PCA、PLS等传统统计过程监控方法假设过程数据服从高斯分布的不足。鲁棒故障重构算法通过迭代保证重构后的数据对应的SVDD监控统计量最小化。诊断算法根据故障集中的不同故障重构后监控统计量是否恢复正常,确定实际发生的过程故障。CSTR过程的仿真研究表明了所提出方法的有效性。     

Modeling and monitoring of multimode process based on subspace separation

In the paper, a new process monitoring approach is proposed for handling the multimode monitoring problem in the industrial batch processes. Compared to conventional method, the contributions are as follows: a new method of extracting the common subspace of different modes is proposed based on the subspace separation; after that the two different subspaces are separated, the kernel principal component models is built for the common and specific subspace respectively and the monitoring is carried out in subspace. The monitoring is carried out in the subspaces. The corresponding confidence regions are constructed according to their models respectively.     

聚合过程贝叶斯统计学习质量模式监测

聚合过程由于反应机理复杂、过程非线性强等特点,使得其质量监控问题成为过程控制领域的研究难点和热点。本文结合聚合反应过程的特点,利用聚合生产过程中与聚合物的物性参数相关的过程参数数据,引入质量模式监测的概念,创新性地提出了基于模式指标的贝叶斯统计学习方法,以解决聚合过程的质量模式监测问题。首先利用主成分分析将观测空间数据的本质特征投影到低维空间,得到模式指标;然后,基于贝叶斯统计学习算法,根据后验概率对构建的质量模式进行判别;最后利用某化工厂提供的聚合反应釜的生产数据对所提方法进行验证。由于模式指标相比于质量指标如转化率、聚合速率等,更能反映聚合反应过程的一致性和产品品质,因此基于模式指标的贝叶斯判别法较基于参数指标的贝叶斯判别法更加准确,更能实现对聚合过程的质量监控。     

Carbon nanotubes grown on glass fiber as a strain sensor for real time structural health monitoring

In order to more effectively monitor the health of composite structures, a fuzzy fiber sensor has been developed. The fuzzy fiber is a bundle of glass fibers with carbon nanotubes or nanofibers (CNTs or CNFs) grown on the surface. The nanotube coating makes the fiber bundle conductive while the small conductive path increases sensitivity. The fuzzy fiber sensor can replace conventional metal foil strain gauges in composite applications. The electrical response of the sensor is monitored in real time to measure strain, vibration, cracking and delamination. Continuous monitoring provides instant notification of any problems. Implementation of this sensor network in a composite can increase service life, decrease maintenance costs and greatly reduce inspection downtime.     

Quality-relevant and process-relevant fault monitoring based on GNPER and the fault quantification index for industrial processes

Traditional quality-relevant fault monitoring methods focus on extracting the relationship between the global structural features of the process and quality variables but ignore the local features. At the same time, they lack the quantification of quality-relevant faults. To solve these problems, a quality-relevant and process-relevant fault monitoring method and its fault quantification index based on global neighbourhood preserving embedding regression (GNPER) are proposed. First, by seeking the direction of maximum global variance, the global objective function is applied to neighbourhood preserving embedding algorithm, and the global neighbourhood preserving embedding (GNPE) model is established to fully extract the global and local information of process data. Second, on the basis of GNPE, through the idea of projection regression, the GNPER model is established to obtain mapping relationships among process variables and quality variables, and quality-relevant subspace and process-relevant subspace are extracted, the corresponding subspace statistics are established for fault monitoring. Finally, the fault quantification index is established for the faults in the two subspaces, which can provide more meaningful fault monitoring results. A numerical example, the hot rolling mill and the Tennessee Eastman (TE) process, verify the superiority and accuracy of the proposed method.     

Highly sensitive,flexible and transparent TiO2/nanocellulose humidity sensor for respiration and skin monitoring

Because of its biodegradability, biocompatibility, strong hydrophilicity, and high specific surface area, nanocellulose has been considered as a potential material for flexible humidity sensors. Herein, we developed a TiO₂/CNC humidity sensor with good flexibility based on the nanocellulose (CNC) prepared by the enzymatic method. Nano TiO₂ with a positive charge was adsorbed on the surface of CNC with a negative charge, and conductive fibers were obtained by an electrostatic self-assembly process. The TiO₂/CNC composite exhibits high optical transmittance (84.5% at 600 nm), flexibility (the tensile elongation reaches 57.82%), and robust mechanical property (the tensile strength reaches 44.66 MPa). The obtained TiO₂/CNC humidity sensor achieved high humidity response (R₀/R = 450.9), rapid response/recovery speed (22/13 s), folding durability (20 times), and long-term stability (40 days). TiO₂/CNC had outstanding performance in actual respiratory rate detection, and the assembled TiO₂/CNC skin moisture detector with flexibility and transparency could monitor human body moisture in real-time, showing its potential as a smart wearable device.     

Community detection based process decomposition and distributed monitoring for large-scale processes

Distributed architectures wherein multiple decision-making units are employed to coordinate their decision-making/actions based on real-time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distributed monitoring scheme involves two key steps, that is, the decomposition of the process into subsystems, and the design of local monitors based on the configured subsystem models. In this article, we propose a distributed process monitoring approach that tackles both steps for large-scale processes. A data-driven process decomposition approach is proposed by leveraging community structure detection to divide variables into subsystems optimally via finding a maximal value of the metric of modularity. A two-layer distributed monitoring scheme is developed where local monitors are designed based on the configured subsystems of variables using canonical correlation analysis. Inner-subsystem interactions and inter-subsystem interactions are tackled by the two layers separately, such that the sensitivity of this monitoring scheme to certain types of faults is improved. We utilize a numerical example to illustrate the effectiveness and superiority of the proposed method. It is then applied to a simulated wastewater treatment process.     

一种基于改进MPCA的间歇过程监控与故障诊断方法

针对基于不同展开方式的多向主元分析(MPCA)方法在线应用时各自存在的缺陷,提出一种改进的基于变量展开的MPCA方法,实现间歇过程的在线监控与故障诊断。该方法采用随时间更新的主元协方差代替固定的主元协方差进行T²统计量的计算,充分考虑了主元得分向量的动态特性;同时引入主元显著相关变量残差统计量,避免SPE统计量的保守性,且该统计量能提供更详细的过程变化信息,对正常工况改变或过程故障引起的T²监控图变化有一定的识别能力;最后提出一种随时间变化的贡献图计算方法用于在线故障诊断。该方法和MPCA方法的监控性能在一个青霉素发酵仿真系统上进行了比较。仿真结果表明：该方法具有较好的监控性能,能及时检测出过程存在的故障,且具有一定的故障识别和诊断能力。     

Supervised sparse preserving projections model based on distributed principal component analysis for chemical process monitoring

For the monitoring of large-scale chemical processes, the distributed method is often used to extract local feature information and model the extracted local feature information to obtain a process monitoring model. But the distributed process monitoring model often contains more process variables, which makes the local information of the process data flooded. To make up for the insufficient extraction of local information in traditional distributed process monitoring, supervised sparse preserving projections model based on distributed principal component analysis (DPCA-SSPP) is proposed in this paper. First, the process data are decomposed by the PCA algorithm, and the principal component space and residual space are obtained. Second, the variables of each sub-block are selected according to the maximum correlation criterion, and the SSPP process monitoring model is established for each sub-block. Finally, the monitoring results of each sub-block are combined together to form a global monitoring result through the Bayesian information fusion strategy. The proposed scheme can be proved to be effective through the simulation on a nonlinear numerical example and the Tennessee Eastman benchmark (TE) process.     

Dynamic stationary subspace analysis based on Gaussian mixture models for ironmaking process monitoring

Blast furnace ironmaking process monitoring is an important and challenging task. Due to the influence of hot blast stove switching and large fluctuations in the quality of raw materials, the measurements of ironmaking processes show obvious non-stationary characteristics, and in addition, the observed data are also characterized by time-series dynamic and non-Gaussian characteristics. In this paper, a dynamic stationary subspace analysis method based on the Gaussian mixture model (DSSA–GMM) is proposed to address the difficulties in blast furnace ironmaking process monitoring. The time-series dynamic relationship of the data is conducted by introducing a sliding time window. The Gaussian mixture model (GMM) is used to deal with the non-Gaussian characteristics of the data, and the parameters of the GMMs are estimated using the expectation–maximization algorithm. The stationary projection matrix is obtained by optimizing the Kullback–Leibler (K–L) divergence between GMMs of different periods to realize the stationary subspace separation. Finally, the convex hull of the stationary subspace is established for fault detection, thus realizing the monitoring for non-stationary and non-Gaussian dynamic processes. The effectiveness of the DSSA–GMM method is verified by a numerical simulation and a dataset collected from an actual blast furnace ironmaking process.     

基于仿射传播聚类子集主元分析的间歇过程监测方法

针对间歇过程固有的多阶段特性,也为了克服传统阶段划分方法严格按照物理时刻顺序将采样点硬性分割而不能使其寻找数据特征最为相近的聚类中心的严重缺陷,提出基于仿射传播聚类(AP)的子集多向主元分析(subset-MPCA)监测新方法:采用全新的乱序聚类思想,将时间片矩阵打乱用AP进行无约束乱序聚类,使样本突破时间顺序的约束自由找寻与其特征最为相近的聚类中心,获得聚类子集,建立精确的子集MPCA监控模型。在线监控时,引入信息度传递实现实时采样点的阶段归属判断,解决阶段不等长批次的最佳模型选择问题。对青霉素仿真数据的实验表明,该方法较传统方法可有效降低故障的漏报和误报,有着更加可靠的监控性能。     

High efficient UV-A photodetectors based on monodispersed ligand-capped TiO2 nanocrystals and polyfluorene hybrids

Monodispersed ligand-capped TiO₂ nanocrystals are synthesized by a low temperature solvothermal method using oleic acid (OA) as the capping agent. The single layer hybrid films based on the OA-capped TiO₂ nanocrystals and poly(9,9-dihexylfluorene) (PFH) are prepared by solution processing at room temperature and characterized by atomic force microscopy, UV-vis absorption and photoluminescence spectra. The hybrid film is applied in the fabrication of nanostructured UV-A photodetector (320-420 nm) by sandwiching it between two electrodes to form bulk heterojunction. The high ultraviolet signal-to-noise ratio of 3 orders of magnitude with short response time less than 200 ms can be achieved for the device. Furthermore, the device shows drastic changes in current under a wide range of UV irradiation with a linear relationship between them. The thermal behavior of the device is also discussed. The high photosensitivity in the UV-A range and the low-cost endow them with potential for environmental and biological uses.