Giant and tunable valley degeneracy splitting in MoTe2

Monolayer transition-metal dichalcogenides possess a pair of degenerate helical valleys in the band structure that exhibit fascinating optical valley polarization. Optical valley polarization, however, is limited by carrier lifetimes of these materials. Lifting the valley degeneracy is therefore an attractive route for achieving valley polarization. It is very challenging to achieve appreciable valley degeneracy splitting with applied magnetic field. We propose a strategy to create giant splitting of the valley degeneracy by proximity-induced Zeeman effect. As a demonstration, our first principles calculations of monolayer MoTe$_2$ on a EuO substrate show that valley splitting over 300 meV can be generated. The proximity coupling also makes interband transition energies valley dependent, enabling valley selection by optical frequency tuning in addition to circular polarization. The valley splitting in the heterostructure is also continuously tunable by rotating substrate magnetization. The giant and tunable valley splitting adds a readily accessible dimension to the valley-spin physics with rich and interesting experimental consequences, and offers a practical avenue for exploring device paradigms based on the intrinsic degrees of freedom of electrons.Comment: 8 pages, 5 figures, 1 tabl

Polar discontinuities and interfacial electronic properties of Bi2_2O2_2Se on SrTiO3_3

The layered oxychalcogenide semiconductor Bi$_2$O$_2$Se (BOS) hosts a multitude of unusual properties including high electron mobility. Owing to similar crystal symmetry and lattice constants, the perovskite oxide SrTiO$_3$ (STO) has been demonstrated to be an excellent substrate for wafer-scale growth of atomically thin BOS films. However, the structural and electronic properties of the BOS/STO interface remain poorly understood. Here, through first-principles study, we reveal that polar discontinuities and interfacial contact configurations have a strong impact on the electronic properties of ideal BOS/STO interfaces. The lowest-energy [Bi-TiO$_2$] contact type, which features the contact between a Bi$_2$O$_2$ layer of BOS with the TiO$_2$-terminated surface of STO, incurs significant interfacial charge transfer from BOS to STO, producing a BOS/STO-mixed, $n$-type metallic state at the interface. By contrast, the [Se-SrO] contact type, which is the most stable contact configuration between BOS and SrO-terminated STO substrate, has a much smaller interfacial charge transfer from STO to BOS and exhibits $p$-type electronic structure with much weaker interfacial hybridization between BOS and STO. These results indicate that BOS grown on TiO$_2$-terminated STO substrates could be a fruitful system for exploring emergent phenomena at the interface between an oxychalcogenide and an oxide, whereas BOS grown on SrO-terminated substrates may be more advantageous for preserving the excellent intrinsic transport properties of BOS.Comment: 9 pages, 4 figure

3D Model Retrieval Algorithm Based on Attention and Multi-view Fusion

With the rapid development of computer vision, 3D data is increasing rapidly. How to retrieve similar model from a large number of models has become a hot research topic. However, in order to meet people's demand, the retrieval accuracy need to be further improved. In terms of multi-view 3D model retrieval, how to effectively learn the information between views is the key to improving performance. In this paper, we propose a novel 3D model retrieval algorithm based on attention and multi-view fusion. Specifically, we mainly constructed two modules. First, dynamic attentive graph learning module is used to learn the intrinsic relationship between view blocks; Then we propose the Attention-NetVlad algorithm, which combines the channel attention algorithm and the NetVlad algorithm. It learns the information between feature channels to enhance the feature expression ability firstly, then uses the NetVlad algorithm to fuse multiple view features into a global feature according to the clustering information. Finally the global feature is used as the only feature of the model to retrieve according to Euclidean distance. In comparison with other state-of-the-art methods by utilizing ModelNet10 and ModelNet40 the proposed method has demonstrated significant improvement for retrieval mAP. Our experiments also demonstrate the effectiveness of the modules in the algorithm

Dopant-Free Donor (D)–p–D–p–D Conjugated Hole- Transport Materials for Efficient and Stable Perovskite Solar Cells

Three novel hole-transporting materials (HTMs) using the 4-methoxytriphenylamine (MeOTPA) core were designed and synthesized. The energy levels of the HTMs were tuned to match the perovskite energy levels by introducing symmetrical electron-donating groups linked with olefinic bonds as the bridge. The methylammonium lead triiodide (MAPbI(3)) perovskite solar cells based on the new HTM Z34 (see main text for structure) exhibited a remarkable overall power conversion efficiency (PCE) of 16.1% without any dopants or additives, which is comparable to 16.7% obtained by a p-doped 2,2,7,7-tetrakis-(N,N-di-4-methoxyphenylamino)-9,9-spirobifluorene (spiro-OMeTAD)-based device fabricated under the same conditions. Importantly, the devices based on the three new HTMs show relatively improved stability compared to devices based on spiro-OMeTAD when aged under ambient air containing 30% relative humidity in the dark

Over 20% PCE perovskite solar cells with superior stability achieved by novel and low-cost hole-transporting materials

The exploration of alternative low-cost molecular hole-transporting materials (HTMs) for both highly efficient and stable perovskite solar cells (PSCs) is a relatively new research area. Two novel HTMs using the thiophene core were designed and synthesized (Z25 and Z26). The perovskite solar cells based on Z26 exhibited a remarkable overall power conversion efficiency (PCE) of 20.1%, which is comparable to 20.6% obtained with spiroOMeTAD. Importantly, the devices based-on Z26 show better stability compared to devices based on Z25 and spiroOMeTAD when aged under ambient air of 30% or 85% relative humidity in the dark and under continuous full sun illumination at maximum power point tracking respectively. The presented results demonstrate a simple strategy by introducing double bonds to design hole-transporting materials for highly efficient and stable perovskite solar cells with low cost, which is important for commercial application

You may also like: Machine-learning algorithms for collection recommendations

Traditional collection development relies heavily on human input, with librarians relying on reviews and subject selection lists, and through user requests. With the development of machine learning, more and more businesses seek automated methods to deliver results relevant to users. The Recommender system, a subclass of information filtering that seeks to predict the "rating" or "preference" of a user, is among the most successful systems of machine learning in action. It has been adopted by many major e-commerce businesses such as Amazon, Netflix, and Expedia, and has been widely implemented to predict product and media recommendations, making it a key factor in increasing product average order value and the number of items per order. Drawing inspiration from the benefits of a recommender system to business and its success in heightening the reliability of recommendations, we attempted to build optimal collection recommendations with machine-learning algorithms using Python. The purpose of this project is to help librarians make collection decisions using the recommender system, and in this presentation we will illustrate several examples that will appeal to both public and academic librarians. One example involves the merging of popular titles with reader rating data. We found that while The New York Times publishes best seller titles based on the rates of sales, they do not have any connection to user ratings. By leveraging data from Goodreads, the world’s largest site for readers and book recommendations, we will build a simple recommender system that produces The New York Times best seller titles that have higher user rating using a matrix factorization based method. Librarians in turn can purchase best seller titles with good reader ratings. Moreover, the recommender system will also enable librarians to recommend books that are similar to a particular title based on pairwise similarity scores. Therefore, if a user enjoys reading a book, the recommender system will pull titles with similar features. The recommender system can be used in other settings. Drawing on bibliographic data from highly circulated items, or frequently requested interlibrary loan items, the recommender system will suggest items with similar features using similarity metrics. As a result, librarians can acquire relevant materials based on users’ previous reading patterns. The recommender system will use machine-learning algorithms not only to simplify collection development for librarians, but will also help end users discover more items relevant to their interests.Librarie

Connecting the dots: Reader ratings, bibliographic data, and machine-learning algorithms for monograph selection

Traditional collection development relies heavily on human input, with librarians relying on reviews and subject selection lists, and through user requests. With the development of machine learning, more and more businesses seek automated methods to deliver results relevant to users. The Recommender system, a subclass of information filtering that seeks to predict the "rating" or "preference" of a user, is among the most successful systems of machine learning in action. It has been adopted by many major e-commerce businesses such as Amazon, Netflix, and Expedia, and has been widely implemented to predict product and media recommendations, making it a key factor in increasing product average order value and the number of items per order. Drawing inspiration from the benefits of a recommender system to business and its success in heightening the reliability of recommendations, we attempted to build optimal collection recommendations with machine-learning algorithms using Python. The purpose of this project is to help librarians make collection decisions using the recommender system, and in this presentation we will illustrate several examples of building this system to aid in the selection of monographs. One example involves the merging of popular titles with reader rating data. We found that while The New York Times publishes best seller titles based on the rates of sales, they do not have any connection to user ratings. By leveraging data from Goodreads, the world’s largest site for readers and book recommendations, we will build a simple recommender system that produces The New York Times best seller titles that have higher user rating using a matrix factorization based method. Another example of using a recommender system is to have the ability to refer selectors to books that are similar to a particular title based on pairwise similarity scores. News services are already able to identify related articles of interest to readers based on the articles that they have read in the past, so applying this system to libraries is an exciting prospect. Drawing on bibliographic data from highly circulated items, the recommender system will suggest items with similar features using similarity metrics. The recommender system will use machine-learning algorithms not only to simplify collection development for librarians, but will also help end users discover more items relevant to their interests.Librarie