388 research outputs found
Controllable Goos-Hanchen shifts and spin beam splitter for ballistic electrons in a parabolic quantum well under a uniform magnetic field
No full textThe quantum Goos-Hanchen shift for ballistic electrons is investigated in a parabolic potential well under a uniform vertical magnetic field. It is found that the Goos-Hanchen shift can be negative as well as positive, and becomes zero at transmission resonances. The beam shift depends not only on the incident energy and incidence angle, but also on the magnetic field and Landau quantum number. Based on these phenomena, we propose an alternative way to realize the spin beam splitter in the proposed spintronic device, which can completely separate spin-up and spin-down electron beams by negative and positive Goos-Hanchen shifts
Direct experimental observation of the single reflection optical Goos-Hanchen shift
No full textWe report a precise direct measurement of the Goos-Hanchen shift after one reflection off a dielectric interface coated with periodic metal stripes. The spatial displacement of the shift is determined by image analysis. A maximal absolute shift of 5.18 and 23.39 mu m for TE and TM polarized light, respectively, is determined. This technique is simple to implement and can be used for a large range of incident angles. (C) 2008 Optical Society of America
Prediction of simultaneously large and opposite generalized Goos-Hanchen shifts for TE and TM light beams in an asymmetric double-prism configuration
No full textIt is predicted that large and opposite generalized Goos-Hanchen (GGH) shifts may occur simultaneously for TE and TM light beams upon reflection from an asymmetric double-prism configuration when the angle of incidence is below but near the critical angle for total reflection, which may lead to interesting applications in optical devices and integrated optics. Numerical simulations show that the magnitude of the GGH shift can be of the order of beam's width
Control of the Goos-Hanchen shift of a light beam via a coherent driving field RID A-4660-2010
No full textJournals published by the American Physical Society can be found at http://publish.aps.org/We present a proposal to manipulate the Goos-Hanchen shift of a light beam via a coherent control field, which is injected into a cavity configuration containing the two-level atomic medium. It is found that the lateral shifts of the reflected and transmitted probe beams can be easily controlled by adjusting the intensity and detuning of the control field. Using this scheme, the lateral shift at the fixed incident angle can be enhanced (positive or negative) under the suitable conditions on the control field, without changing the structure of the cavity
Optimization of Hydrogenated Amorphous Si Layer for Si Heterojunction Solar Cells
No full textCrystalline silicon (c-Si) solar cells that are well-established technologies have already achieved a power conversion efficiency (PCE) as high as 26.7% by using the interdigitated-back-contacted (IBC) configuration. However, this efficiency is still limited by the spectral mismatch between t¬he absorption characteristics of c-Si and the AM 1.5 spectrum. To better utilize especially the low wavelength irradiance and to exceed the single-junction silicon solar cell theoretical PCE limit (29.43%), the perovskite/c-Si tandem solar cell concept was proposed. This thesis project aims to develop a high efficiency single-side textured front-back contacted (FBC) silicon heterojunction (SHJ) solar cell as the bottom cells for 2-terminal (2-T) perovskite/c-Si tandem solar cells. Therefore, we firstly focus on the optimizations of the deposition parameters (power and precursor gases) of hydrogenated intrinsic amorphous silicon ((i)a-Si:H) passivation layer and the subsequent application of hydrogen plasma treatment (HPT) on the (i)a-Si:H layer. An optimized 8.8 nm-thick single (i)a-Si:H together with hydrogen plasma treatment (HPT) achieve an effective lifetime (τeff) of 1.3 ms, implied open-circuit voltage (iVOC) of 704.3 mV on symmetrical passivated double-side-flat <100> oriented crystalline silicon (c-Si) wafer. To further improve the passivation, a bilayer structure of (i)a-Si:H with a total thickness of 10 nm is optimized to boost further the τeff to 8.3 ms and iVOC to 733.5 mV. Afterwards, we implement the optimized (i)a-Si:H layers into the front/back-contacted (FBC) rear junction solar cells with single-side textured morphology, which is designed to be used as the substrate for future integration of solution-processed perovskite top cell. For solar cells investigated, we keep the textured rear side always the same with optimized contact stacks consisting of p-type hydrogenated nanocrystalline silicon oxide ((p)-nc-SiOX:H) and p-type hydrogenated nanocrystalline silicon ((p)-nc-Si:H, while varying the layer stacks on the flat front side. Specifically cell performances are studied with either n-type hydrogenated nanocrystalline silicon ((n)-nc-Si:H) or n-type hydrogenated amorphous silicon ((n)-a-Si:H) with varied optimized (i)a-Si:H layers. We observe enhanced passivation qualities of solar cell precusors when adding the (n)-nc-Si:H/(n)-a-Si:H layer on top of the (i)a-Si:H, which enables the potential for realizing high-efficiency solar cells. During the fabrication of FBC-SHJ solar cells, we also find that a thinner (i)a-Si:H on the front side is critical to improve the device efficiency thanks to the effective reduction of both parasitic absorption and carrier transport losses. Accordingly the gain in JSC and FF dominate the improvement of PCE. Lastly, we present rear junction FBC-SHJ solar cell with n optimized stack of (n)nc-Si:H and (i)a-Si:H, with VOC of 711 mV, JSC of 34.82 mA/cm2, FF of 79.64% and PCE of 19.72%. This optimized solar cell is also considered to be ready for its application in tandem device fabrication.Electrical Engineering | Sustainable Energy Technolog
RETRACTED ARTICLE: Long non-coding RNA <i>SNHG4</i> promotes cervical cancer progression through regulating c-Met via targeting miR-148a-3p
No full textWe, the Editors and Publisher of the journal Cell Cycle, have retracted the following article: Hanchen Li, Jiang Hong and Walimuni Sandaroo Mendis Abeysekara Wijayakulathilaka. Long non-coding RNA SNHG4 promotes cervical cancer progression through regulating c-Met via targeting miR-148a-3p. Cell Cycle. 2019;18(23):3313-3324. doi: 10.1080/15384101.2019.1674071 Since publication, significant concerns have been raised about the integrity of the data and reported results in the article. When approached for an explanation, the authors did not provide their original data or any necessary supporting information. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The corresponding author listed in this publication has been informed. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.</p
Microseismic imaging using a source-independent full-waveform inversion method
No full textUsing full waveform inversion (FWI) to locate microseismic and image microseismic events allows for an automatic process (free of picking) that utilizes the full wavefield. However, waveform inversion of microseismic events faces incredible nonlinearity due to the unknown source location (space) and function (time). We develop a source independent FWI of microseismic events to invert for the source image, source function and the velocity model. It is based on convolving reference traces with the observed and modeled data to mitigate the effect of an unknown source ignition time. The adjoint-state method is used to derive the gradient for the source image, source function and velocity updates. The extended image for source wavelet in z axis is extracted to check the accuracy of the inverted source image and velocity model. Also the angle gather is calculated to see if the velocity model is correct. By inverting for all the source image, source wavelet and the velocity model, the proposed method produces good estimates of the source location, ignition time and the background velocity for part of the SEG overthrust model.We thank KAUST for sponsoring this research. We also thank to the team of SWAG for their help during the research
Designing Machine Learning Models for Graph Analytics
Get PDFUniversity of Technology Sydney. Faculty of Engineering and Information Technology.With growing popularity of the machine learning methods, there have been a great number of machine learning methods proposed for graph analytics. In this thesis, we design three machine learning based models for the popular graph analysis tasks such as node classification, graph interaction prediction and subgraph matching.
Firstly, we design a binarized graph neural network to efficiently obtain the vector representations for vertices and graphs. Recently, there have been some breakthroughs in graph analysis by applying the Graph Neural Networks (GNNs). However, the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based approaches which may limit the efficiency and scalability of these models. This motivates us to develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters following the GNN-based paradigm. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.
Secondly, we design a graph of graphs neural network for entity interaction prediction, and then extend the model to support the graph classification task with more expressive representations. Entity interaction prediction is essential in many important applications, which can be quite challenging when there are two types of graphs are involved: local graphs for structured entities and a global graph for the interactions between structured entities. We observe that existing works cannot properly exploit the unique graph of graphs structure. In this thesis, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features of the given graph in a hierarchical way. Based on GoGNN, we further propose a Powerful Graph Of graphs neural Network, namely PGON, which has 3-Weisfeiler-Lehman expressive power and can be used to handle the graph classification task.
Thirdly, we design a reinforcement learning based query vertex ordering model for subgraph matching. Subgraph matching is a fundamental problem in graph analytics. Instead generating the matching order with heuristics, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduce the number of redundant enumerations. With the help of the reinforcement learning framework, our model could consider the long-term benefits during order generation.
Extensive experiments on real-life datasets indicate the efficiency and effectiveness of our proposed models in the corresponding graph analytic tasks
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