97 percent light absorption in an ultrabroadband frequency range utilizing an ultrathin metal layer: Randomly oriented, densely packed dielectric nanowires as an excellent light trapping scaffold

In this paper, we propose a facile and large scale compatible design to obtain perfect ultrabroadband light absorption using metal-dielectric core-shell nanowires. The design consists of atomic layer deposited (ALD) Pt metal uniformly wrapped around hydrothermally grown titanium dioxide (TiO2) nanowires. It is found that the randomly oriented dense TiO2 nanowires can impose excellent light trapping properties where the existence of an ultrathin Pt layer (with a thickness of 10 nm) can absorb the light in an ultrabroadband frequency range with an amount near unity. Throughout this study, we first investigate the formation of resonant modes in the metallic nanowires. Our findings prove that a nanowire structure can support multiple longitudinal localized surface plasmons (LSPs) along its axis together with transverse resonance modes. Our investigations showed that the spectral position of these resonance peaks can be tuned with the length, radius, and orientation of the nanowire. Therefore, TiO2 random nanowires can contain all of these features simultaneously in which the superposition of responses for these different geometries leads to a flat perfect light absorption. The obtained results demonstrate that taking unique advantages of the ALD method, together with excellent light trapping of chemically synthesized nanowires, a perfect, bifacial, wide angle, and large scale compatible absorber can be made where an excellent performance is achieved while using less materials. © 2017 The Royal Society of Chemistry

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory

Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data

Clustering geosensor data is a problem that has recently attracted a large amount of research. In this paper, we focus on clustering geophysical time series data measured by a geo-sensor network. Clusters are built by accounting for both spatial and temporal information of data. We use clusters to produce globally meaningful information from time series obtained by individual sensors. The cluster information is integrated to the ARIMA model, in order to yield accurate forecasting results. Experiments investigate the trade-off between accuracy and efficiency of the proposed algorithm

Comparative study of Al2O3 and HfO2 for surface passivation of Cu(In,Ga)Se2 thin-films: An innovative Al2O3/HfO2 multi-stack design

In Cu(In,Ga)Se2 (CIGS) thin-film solar cells, interface recombination is one of the most important limiting factors with respect to device performance. Therefore, in this study, Metal-Insulator- Semiconductor samples are used to investigate and compare the passivation effects of Al2O3 and HfO2 at the interface with CIGS. Capacitance-Voltage-Frequency measurements allow to qualitatively and quantitatively assess the existence of high negative charge density (Qf ~ -1012 cm- 2) and low interface-trap density (Dit ~1011 cm-2 eV-1). At the rear interface of CIGS solar cells, these respectively induce field-effect and chemical passivation. A trade-off is highlighted between stronger field-effect for HfO2 and lower interface-trap density for Al2O3. This motivates the usage of Al2O3 to induce chemical passivation at the front interface of CIGS solar cells but raises the issue of its processing compatibility with the buffer layer. Therefore, an innovative Al2O3/HfO2 multistack design is proposed and investigated for the first time. Effective chemical passivation is similarly demonstrated for this novel design, suggesting potential decrease in recombination rate at the front interface in CIGS solar cells and increased efficiency. 300°C annealing in N2 environment enable to enhance passivation effectiveness by reducing Dit while surface cleaning may reveal useful for alternative CIGS processing methods

KF Postdeposition Treatment in N-2 of Single-Stage Thin Cu(In,Ga)Se-2 Absorber Layers

status: publishe

A fuzzy index for detecting spatiotemporal outliers

The detection of spatial outliers helps extract important and valuable information from large spatial datasets. Most of the existing work in outlier detection views the condition of being an outlier as a binary property. However, for many scenarios, it is more meaningful to assign a degree of being an outlier to each object. The temporal dimension should also be taken into consideration. In this paper, we formally introduce a new notion of spatial outliers. We discuss the spatiotemporal outlier detection problem, and we design a methodology to discover these outliers effectively. We introduce a new index called the fuzzy outlier index, FoI, which expresses the degree to which a spatial object belongs to a spatiotemporal neighbourhood. The proposed outlier detection method can be applied to phenomena evolving over time, such as moving objects, pedestrian modelling or credit card fraud

The application of cluster analysis in geophysical data interpretation

A clustering algorithm which is based on density and adaptive density-reachable is developed and presented for arbitrary data point distributions in some real world applications, especially in geophysical data interpretation. Through comparisons of the new algorithm and other algorithms, it is shown that the new algorithm can reduce the dependency of domain knowledge and the sensitivity of abnormal data points, that it can improve the effectiveness of clustering results in which data are distributed in different shapes and different density, and that it can get a better clustering efficiency. The application of the new clustering algorithm demonstrates that data mining techniques can be used in geophysical data interpretation and can get meaningful and useful results, and that the new clustering algorithm can be used in other real world applications.Science Foundation Irelan

A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data

Publisher Copyright: © 2020, Springer Nature Switzerland AG.Clustering methods have become popular in the last years due to the need of analyzing the high amount of collected data from different fields of knowledge. Nevertheless, the main drawback of clustering is the selection of the optimal method along with its internal parameters in an unsupervised environment. In the present paper, a novel heuristic approach based on the Harmony Search algorithm aided with a local search procedure is presented for simultaneously optimizing the best clustering algorithm (K-means, DBSCAN and Hierarchical clustering) and its optimal internal parameters based on the Silhouette index. Extensive simulation results show that the presented approach outperforms the standard clustering configurations and also other works in the literature in different Time Series and synthetic databases.Acknowledgments. This research has been supported by a TECNALIA Research and Innovation PhD Scholarship, ELKARTEK program (SENDANEU KK-2018/00032) and the HAZITEK program (DATALYSE ZL-2018/00765) of the Basque Government.Peer reviewe