Inducing imperfections in germanium nanowires

Nanowires with inhomogeneous heterostructures such as polytypes and periodic twin boundaries are interesting due to their potential use as components for optical,electrical,and thermophysical applications.Additionally,the incorporation of metal impurities in semiconductor nanowires could substantially alter their electronic and optical properties.In this highlight article,we review our recent progress and understanding in the deliberate induction of imperfections,in terms of both twin boundaries and additional impurities in germanium nanowires for new/enhanced functionalities.The role of catalysts and catalyst-nanowire interfaces for the growth of engineered nanowires via a three-phase paradigm is explored.Three-phase bottom-up growth is a feasible way to incorporate and engineer imperfections such as crystal defects and impurities in semiconductor nanowires via catalyst and/or interfacial manipulation."Epitaxial defect transfer"process and catalyst-nanowire interfacial engineering are employed to induce twin defects parallel and perpendicular to the nanowire growth axis.By inducing and manipulating twin boundaries in the metal catalysts,twin formation and density are controlled in Ge nanowires.The formation of Ge polytypes is also observed in nanowires for the growth of highly dense lateral twin boundaries.Additionally,metal impurity in the form of Sn is injected and engineered via third-party metal catalysts resulting in above-equilibrium incorporation of Sn adatoms in Ge nanowires.Sn impurities are precipitated into Ge bi-layers during Ge nanowire growth,where the impurity Sn atoms become trapped with the deposition of successive layers,thus giving an extraordinary Sn content (＞6 at.％) in Ge nanowires.A larger amount of Sn impingement (＞9 at.％) is further encouraged by utilizing the eutectic solubility of Sn in Ge along with impurity trapping.     

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.     

Experimental study of paddy drying in a vortex chamber

The intensification of interfacial mass, heat, and momentum transfer makes vortex chambers potentially interesting for the efficient drying of paddy, allowing shorter drying times and/or more compact equipment. The presence of a shell introduces particular challenges. Intraparticle diffusion limitations are strong and may reduce the advantage from intensified interfacial mass and heat transfer and the efficiency of air usage. Furthermore, high shear and normal stresses in the fast rotating particle bed may cause damage to the paddy shell, posing problems for transport and storage. With these specific aspects in mind, the use of vortex chambers for paddy drying is experimentally evaluated.     

Precipitation phase transformation in nanocrystalline Fe-Mo alloys

Precipitation phase transformation was studied in nanocrystalline Fe-rich Fe-Mo alloys with the use of X-ray diffraction and M?ssbauer spectroscopy. Alloys up to 5 at% Mo in Fe were synthesized by mechanical alloying and formed in alpha phase bcc solid solutions with average grain sizes in the range of 10-13 nm. The precipitation transformation (alpha-->alpha + lambda) was found to proceed via a Mo clustering that was correlated with the size of the nanograins. This was understood in terms of the Gibbs Thomson effect with a concept of negative surface energy contribution to the Gibbs free energy of mixing in a nanocrystalline alloy with positive internal energy of mixing. This contribution increased the stability of the solid solution for nanosized grains, and the Mo precipitation started once the grains grew beyond a critical size. We argue that the Mo precipitation takes place in the grain boundary regions, and the Mo-rich lambda phase also precipitates directly in the grain boundary regions, in contrast to the microcrystalline alloys, where the Mo clusters formed within the grains and were first dissolved in the Fe matrix before the lambda phase was formed.     

Copper(II)‐Catalyzed Aerobic Oxidative Coupling between Chalcone and 2‐Aminopyridine via C?H Amination: An Expedient Synthesis of 3‐Aroylimidazo[1,2‐a]pyridines

A simple and efficient protocol has been developed for the synthesis of 3‐aroylimidazopyridines via copper(II) acetate‐catalyzed aerobic oxidative amination. A library of 3‐aroylimidazopyridines was synthesized from readily accessible chalcones and 2‐aminopyridines with high yields and regioselectivity. The reaction proceeds through a tandem Michael addition followed by an intramolecular oxidative amination. The successful application of this methodology for a gram‐scale reaction indicates its potential for bulk synthesis.

     

In‐situ Observations of Nanoscale Effects in Germanium Nanowire Growth with Ternary Eutectic Alloys

Vapour‐liquid‐solid (VLS) techniques are popular routes for the scalable synthesis of semiconductor nanowires. In this article, in‐situ electron microscopy is used to correlate the equilibrium content of ternary (Au_0.75Ag_0.25–Ge and Au_0.65Ag_0.35–Ge) metastable alloys with the kinetics, thermodynamics and diameter of Ge nanowires grown via a VLS mechanism. The shape and geometry of the heterogeneous interfaces between the liquid eutectic and solid Ge nanowires varies as a function of nanowire diameter and eutectic alloy composition. The behaviour of the faceted heterogeneous liquid–solid interface correlates with the growth kinetics of the nanowires, where the main growth facet at the solid nanowire–liquid catalyst drop contact line lengthens for faster nanowire growth kinetics. Pronounced diameter dependent growth kinetics, as inferred from liquid–solid interfacial behaviour, is apparent for the synthesised nanowires. Direct in‐situ microscopy observations facilitates the comparison between the nanowire growth behaviour from ternary (Au–Ag–Ge) and binary (Au–Ge) eutectic systems.     

A three-stage graphics processing unit-based finite element analyses matrix generation strategy for unstructured meshes

With the development of parallel computing architectures, larger and more complex finite element analyses (FEA) are being performed with higher accuracy and smaller execution times. Graphics processing units (GPUs) are one of the major contributors of this computational breakthrough. This work presents a three-stage GPU-based FEA matrix generation strategy with the key idea of decoupling the computation of global matrix indices and values by use of a novel data structure referred to as the neighbor matrix. The first stage computes the neighbor matrix on the GPU based on the unstructured mesh. Using this neighbor matrix, the indices and values of the global matrix are computed separately in the second and third stages. The neighbor matrix is computed for three different element types. Two versions for performing numerical integration and assembly in the same or separate kernels are implemented and simulations are run for different mesh sizes having up to three million degrees of freedom on a single GPU. Comparison with GPU-based parallel implementation from the literature reveals speedup ranging from 4× to 6× for the proposed workload division strategy. Furthermore, the same kernel implementation is found to outperform the separate kernel implementation by 70% to 150% for different element types.     

Engineering Catalytically Active Sites by Sculpting Artificial Edges on MoS2 Basal Plane for Dinitrogen Reduction at a Low Overpotential

Engineering catalytically active sites have been a challenge so far and often relies on optimization of synthesis routes, which can at most provide quantitative enhancement of active facets, however, cannot provide control over choosing orientation, geometry and spatial distribution of the active sites. Artificially sculpting catalytically active sites via laser-etching technique can provide a new prospect in this field and offer a new species of nanocatalyst for achieving superior selectivity and attaining maximum yield via absolute control over defining their location and geometry of every active site at a nanoscale precision. In this work, a controlled protocol of artificial surface engineering is shown by focused laser irradiation on pristine MoS₂ flakes, which are confirmed as catalytic sites by electrodeposition of AuNPs. The preferential Au deposited catalytic sites are found to be electrochemically active for nitrogen adsorption and its subsequent reduction due to the S-vacancies rather than Mo-vacancy, as advocated by DFT analysis. The catalytic performance of Au-NR/MoS₂ shows a high yield rate of ammonia (11.43 × 10⁻⁸ mol s⁻¹ cm⁻²) at a potential as low as −0.1 V versus RHE and a notable Faradaic efficiency of 13.79% during the electrochemical nitrogen reduction in 0.1 m HCl.     

Legal framework for small autonomous agricultural robots

Legal structures may form barriers to, or enablers of, adoption of precision agriculture management with small autonomous agricultural robots. This article develops a conceptual regulatory framework for small autonomous agricultural robots, from a practical, self-contained engineering guide perspective, sufficient to get working research and commercial agricultural roboticists quickly and easily up and running within the law. The article examines the liability framework, or rather lack of it, for agricultural robotics in EU, and their transpositions to UK law, as a case study illustrating general international legal concepts and issues. It examines how the law may provide mitigating effects on the liability regime, and how contracts can be developed between agents within it to enable smooth operation. It covers other legal aspects of operation such as the use of shared communications resources and privacy in the reuse of robot-collected data. Where there are some grey areas in current law, it argues that new proposals could be developed to reform these to promote further innovation and investment in agricultural robots.