Transmission electron microscopy study of Ni–Si nanocomposite films

Nickel induced crystallization of amorphous Si (a-Si) films is investigated using transmission electron microscopy. Metal-induced crystallization was achieved on layered films deposited onto thermally oxidized Si(3 1 1) substrates by electron beam evaporation of a-Si (400 nm) over Ni (50 nm). The multi-layer stack was subjected to post-deposition annealing at 200 and 600 °C for 1 h after the deposition. Microstructural studies reveal the formation of nanosized grains separated by dendritic channels of 5 nm width and 400 nm length. Electron diffraction on selected points within these nanostructured regions shows the presence of face centered cubic NiSi₂ and diamond cubic structured Si. Z-contrast scanning transmission electron microscopy images reveal that the crystallization of Si occurs at the interface between the grains of NiSi₂ and a-Si. X-ray absorption fine structure spectroscopy analysis has been carried out to understand the nature of Ni in the Ni–Si nanocomposite film. The results of the present study indicate that the metal induced crystallization is due to the diffusion of Ni into the a-Si matrix, which then reacts to form nickel silicide at temperatures of the order of 600 °C leading to crystallization of a-Si at the silicide–silicon interface.     

Manufacture of Syntactic Foams: Pre-Mold Processing

Manufacturing processes for syntactic foams made of hollow microspheres and starch were studied. Various manufacturing parameters in relation to the “buoyancy method” were identified and inter-related. An equation based on unit-cell models with the minimum inter-microsphere distance concept for a relation between volume expansion rate of bulk microspheres in aqueous starch and microsphere size was derived and successfully used to predict experimental data. It was demonstrated that the inter-microsphere distance can be calculated numerically for microspheres with known statistical data. The equation relating between volume expansion rate and microsphere size was further extended to accommodate a relation between inter-microsphere distance and microsphere size but with limited accuracy for binders of low starch content. An alternative empirical linear equation for the relation between inter-microsphere distance and microsphere size is proposed for wider applications. A simple method for estimation of syntactic foam density prior to completion of manufacture is suggested. Shrinkage after molding of syntactic foam is discussed in relation with different stages such as slurry, dough and solid. A two-step manufacturing process involving molding and then forming is suggested for syntactic foam dimensional control.     

Enhanced Solar‐to‐Hydrogen Generation with Broadband Epsilon‐Near‐Zero Nanostructured Photocatalysts

The direct conversion of solar energy into fuels or feedstock is an attractive approach to address increasing demand of renewable energy sources. Photocatalytic systems relying on the direct photoexcitation of metals have been explored to this end, a strategy that exploits the decay of plasmonic resonances into hot carriers. An efficient hot carrier generation and collection requires, ideally, their generation to be enclosed within few tens of nanometers at the metal interface, but it is challenging to achieve this across the broadband solar spectrum. Here the authors demonstrate a new photocatalyst for hydrogen evolution based on metal epsilon‐near‐zero metamaterials. The authors have designed these to achieve broadband strong light confinement at the metal interface across the entire solar spectrum. Using electron energy loss spectroscopy, the authors prove that hot carriers are generated in a broadband fashion within 10 nm in this system. The resulting photocatalyst achieves a hydrogen production rate of 9.5 µmol h^?1 cm^?2 that exceeds, by a factor of 3.2, that of the best previously reported plasmonic‐based photocatalysts for the dissociation of H₂ with 50 h stable operation.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Chemical Coupled PEDOT:PSS/Si Electrode: Suppressed Electrolyte Consumption Enables Long‑Term Stability

Huge volume changes of Si during lithiation/delithiation lead to regeneration of solid-electrolyte interphase(SEI)and consume electrolyte.In this article,γ-glycidoxypropyl trimethoxysilane(GOPS)was incorporated in Si/PEDOT:PSS electrodes to construct a flexible and conductive artificial SEI,effectively suppressing the consumption of electrolyte.The optimized electrode can maintain 1000 mAh g^−1 for nearly 800 cycles under limited electrolyte compared with 40 cycles of the electrodes without GOPS.Also,the optimized electrode exhibits excellent rate capability.The use of GOPS greatly improves the interface compatibility between Si and PEDOT:PSS.XPS Ar+etching depth analysis proved that the addition of GOPS is conducive to forming a more stable SEI.A full battery assembled with NCM 523 cathode delivers a high energy density of 520 Wh kg^−1,offering good stability.     

Gene-expression programming to predict friction factor for Southern Italian rivers

This briefing article presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to predict friction factor for Southern Italian rivers. Published data were compiled for the friction for 43 gravel-bed rivers of Calabria. The proposed GEP approach produces satisfactory results (R ² = 0.958 and RMSE = 0.079) compared with existing predictors.     

Tree colonization trends on a sediment bar after a major flood

Tree mortality and regeneration in riparian areas are greatly influenced by flooding. The elevational distribution of Salix spp. and Robinia pseudoacacia were investigated by observing densities and standing conditions before and after a major flood on a sediment bar in the middle reaches of the Arakawa River in Kumagaya, Japan. The subsequent tree recruitment was also examined. R. pseudoacacia was easily washed away with the eroded sediment, whereas Salix spp. was found to be more tolerant. Both species were able to survive even after collapsing, provided that their roots were fully or partially embedded in the sediment. Re‐shooting of collapsed trees, rather than emergence from saplings (either by seeds or vegetative fragments), was observed to be the major method of recruitment after the flood. Therefore, tree density before the flood was unchanged, unless the trees were subject to washing away. Salix spp. recruitment was mainly observed in low‐lying areas and R. pseudoacacia in elevated areas. Recruitment from saplings was species‐specific. Salix spp. at high elevations originated mainly from shoot fragments as they need high levels of moisture for seed germination and at higher elevations, sediment moisture content is very low. R. pseudoacacia, on other hand, originated mainly from roots and seeds. At a given elevation, past recruitment patterns indicated that the annual recruitment of trees increased with tree density up to a particular threshold of recruitment density. Further increases in tree density beyond that optimum value resulted in a decline in recruitment. Furthermore, threshold density was observed to increase along with elevation for R. pseudoacacia while declining with Salix spp. Copyright © 2010 John Wiley & Sons, Ltd.     

A variational multiscale method for incompressible turbulent flows: Bubble functions and fine scale fields

This paper presents a residual-based turbulence model for the incompressible Navier–Stokes equations. The method is derived employing the variational multiscale (VMS) framework. A multiscale decomposition of the continuous solution and a priori unique decomposition of the admissible spaces of functions lead to two coupled nonlinear problems termed as the coarse-scale and the fine-scale sub-problems. The fine-scale velocity field is assumed to be nonlinear and time-dependent and is modeled via the bubble functions approach applied directly to the fine-scale sub-problem. A significant contribution in this paper is a systematic and consistent derivation of the fine-scale variational operator, commonly termed as the stabilization tensor that possesses the right order in the advective and diffusive limits, and variationally projects the fine-scale solution onto the coarse-scale space. A direct treatment of the fine-scale problem via bubble functions offers several fine-scale approximation options with varying degrees of mathematical sophistication that are investigated via benchmark problems. Numerical accuracy of the proposed method is shown on a forced-isotropic turbulence problem, statistically stationary turbulent channel flow problems at Re_T = 395 and 590, and non-equilibrium turbulent flow around a cylinder at Re = 3,900.