Modeling the effect of statistical variations in length and diameter of randomly oriented CNTs on the properties of CNT reinforced nanocomposites

A nano-mechanical model has been developed to calculate the tensile modulus and the tensile strength of randomly oriented short carbon nanotubes (CNTs) reinforced nanocomposites, considering the statistical variations of diameter and length of the CNTs. According to this model, the entire composite is divided into several composite segments which contain CNTs of almost the same diameter and length. The tensile modulus and tensile strength of the composite are then calculated by the weighted sum of the corresponding modulus and strength of each composite segment. The existing micro-mechanical approach for modeling the short fiber composites is modified to account for the structure of the CNTs, to calculate the modulus and the strength of each segmented CNT reinforced composites. Multi-walled CNTs with and without inter-tube bridging have been considered. Statistical variations of the diameter and length of the CNTs are modeled by a normal distribution. Simulation results show that CNTs inter-tube bridging, length and diameter affect the nanocomposites modulus and strength. Simulation results have been compared with the available experimental results and the comparison concludes that the developed model can be effectively used to predict tensile modulus and tensile strength of CNTs reinforced composites.     

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.     

中孟在校青年男子体型对比分析

为分析中孟在校青年男子体型区别,对孟加拉和中国在校青年男子的整体和局部体型进行对比和分类分析。运用手工测量及三维测量法获取中国与孟加拉各200名在校青年男子的相关体型参数,对比分析孟加拉与中国在校青年男子的整体和局部体型。在整体体型方面,分析了两国在校青年男子于国家标准及形态整体特征上的异同点;在局部体型方面,针对肩部、胸部和腰臀部3个人体主要特征部位进行分类,分别将各个部位的体型聚类成3类进行对比分析。研究表明:中孟在校青年男子体型差异较大,中国在校青年男子整体瘦长、整体曲线明显、肩部较宽、胸部线条弧度较大、臀部挺翘;孟加拉在校青年男子大部分属于均匀凸腹体型,整体轮廓线条平缓、肩部较窄、胸围较大且平坦、腰臀围较大、臀部较扁平且腹部凸出。该研究结果可为中孟在校青年男子版型制作提供体型依据。     

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.     

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.     

Analysis of phase transition,structural and dynamical properties of R290 using molecular dynamics simulation

Propane (R290), a hydrocarbon refrigerant, is an excellent choice of cooling fluids for use in refrigeration and air conditioning systems considering the environmental point of view and system performance. The phase transition phenomenon and structural and dynamic properties of R290 were analyzed through a molecular dynamics (MD) simulation. The densities, isobaric heat capacities and viscosities were computed and the variations of density, volume, potential energy and the nucleation process were examined to investigate the effects of condensation temperature on the phase transition rate. The mean square displacement and velocity autocorrelation function for different temperatures were simulated for dynamical analysis. Radial distribution functions were investigated to get insight into the structural analysis at the atomic level. Shear viscosity and isobaric heat capacity obtained by the present simulation showed a good agreement with the REFPROP data. The structural analysis revealed that the phase transition of R290 did not affect its intramolecular structure.

     

Destructive and non-destructive evaluation of concrete for optimum sand to aggregate volume ratio

Aggregates are the biggest contributor to concrete volume and are a crucial parameter in dictating its mechanical properties. As such, a detailed experimental investigation was carried out to evaluate the effect of sand-to-aggregate volume ratio (s/a) on the mechanical properties of concrete utilizing both destructive and non-destructive testing (employing UPV (ultrasonic pulse velocity) measurements). For investigation, standard cylindrical concrete samples were made with different s/a (0.36, 0.40, 0.44, 0.48, 0.52, and 0.56), cement content (340 and 450 kg/m³), water-to-cement ratio (0.45 and 0.50), and maximum aggregate size (12 and 19 mm). The effect of these design parameters on the 7, 14, and 28 d compressive strength, tensile strength, elastic modulus, and UPV of concrete were assessed. The careful analysis demonstrates that aggregate proportions and size need to be optimized for formulating mix designs; optimum ratios of s/a were found to be 0.40 and 0.44 for the maximum aggregate size of 12 and 19 mm, respectively, irrespective of the W/C (water-to-cement) and cement content.