Electronic structure and absolute band edge position of tetragonal AgInS2 photocatalyst: A hybrid density functional study

Energy bands, effective mass of carriers, absolute band edge positions and optical properties of tetragonal AgInS₂ were calculated using a first-principles approach with the exchange correlation described by B3LYP hybrid functional. The results indicate that tetragonal AgInS₂ has a direct band gap of 1.93 eV, which reproduce well experimental value. Calculated effective masses of electrons and holes are both small which are beneficial to separation and migration of electron and hole pairs. This implies that AgInS₂ has good photocatalytic performance. The calculated optical characteristics indicate that AgInS₂ has a slight anisotropy for both the real and imaginary parts of the dielectric function and exhibits large optical absorption in the visible light region. Furthermore, the calculated band edge positions in (100), (010) and (001) surfaces indicate that tetragonal AgInS₂ is beneficial to the reduction and oxidation of water to hydrogen and oxygen under visible light irradiation.     

Fabrication,structural and vibrational properties,and physical and optical properties tailoring of nanocrystalline MoS2 films

The modification and tuning features of nanostructured films are of great interest because of controllable and distinctive inherent properties in these materials. Here, nanocrystalline MoS₂ films were fabricated on the stainless steels by a radio frequency magnetron sputtering at ambient temperature. X-ray photoelectron spectroscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction and Raman scattering spectroscopy were used to study the chemical state, chemical composition, crystal structure and vibrational properties of the fabricated MoS₂ films. The bias voltage dependent structural evolution and its influence on the optical properties of MoS₂ nanocrystalline films were systematically investigated. Besides, the residual stresses of MoS₂ nanocrystalline films were explored by employing sin²ψ approach. X-ray diffraction demonstrates that the nanocrystalline MoS₂ films have single-phase hexagonal crystal structure. All MoS₂ films are polycrystalline in nature. The bandgap values are found to be intensively dependent on bias voltage. Our findings show that the nanocrystalline MoS₂ films with different physical properties and intense quantum confinement effect can be realized through adjusting bias voltages. This work may provide deep insight for realizing transitional metal dichalcogenide-based nanostructured film optoelectronic devices with tunable physical properties through a traditional, very cost-effective, and large-scale fabrication method.     

Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

“The innovation governance dilemma: Alternatives to the precautionary principle”

This article reviews the four innovation governance approaches (the precautionary principle, responsible innovation, permissionless innovation, and the innovation principle), including definitions, important attributes, and weaknesses found in each approach, and when utilizing an affinity diagram as a tool of analysis, identifies their distinctive characteristics and common relationships. A discussion section summarizes the paper’s findings and offers insights into where there is common relationships for further possible convergence between two innovation governance approaches – responsible innovation and permissionless innovation – that conceptually share substantially more in common than they contrast with each other. For addressing this challenge, the study recommends the following policy proposals: embrace artificial intelligence/machine learning/data analytics for risk management and regulatory adaptability; consider “soft law”as an option to public regulation; and substitute corporate citizenship for corporate social responsibility.     

Enhanced thermal stability of Bi2Te3-based alloys via interface engineering with atomic layer deposition

The ease of Te sublimation from Bi₂Te₃-based alloys significantly deteriorates thermoelectric and mechanical properties via the formation of voids. We propose a novel strategy based on atomic layer deposition (ALD) to improve the thermal stability of Bi₂Te₃-based alloys via the encapsulation of grains with a ZnO layer. Only a few cycles of ZnO ALD over the Bi₂Te_2.7Se_0.3 powders resulted in significant suppression of the generation of pores in Bi₂Te_2.7Se_0.3 extrudates and increased the density even after post-annealing at 500 °C. This is attributed to the suppression of Te sublimation from the extrudates. The ALD coating also enhanced grain refinement in Bi₂Te_2.7Se_0.3 extrudates. Consequently, their mechanical properties were significantly improved by the encapsulation approach. Furthermore, the ALD approach yields a substantial improvement in the figure-of-merit after the post-annealing. Therefore, we believe the proposed approach using ALD will be useful for enhancing the mechanical properties of Bi₂Te₃-based alloys without sacrificing thermoelectric performance.     

Electrochemical generation of hydrogenated graphene flakes

A cathodic electrochemical method for the exfoliation of graphite to produce hydrogenated graphenic flakes is introduced. The resulting solutions consist of micrometer-sized and predominantly 1–4 layers thick hydrogenated graphenic flakes. In contrast to oxygenation, chemisorption of hydrogen avoids the formation of structural vacancy defects in the exfoliated flakes. Thermal desorption of hydrogen therefore results in graphenic flakes with a low defect density and consequently good electrical conductivity. Cathodic electrochemical exfoliation offers a remarkably simple and effective technique for the production of high quality graphene flakes and their hydrogenated relatives.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.