Results 1 - 10 of 4180
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[en] High purity boron nitride, without Si and a low carbon content, is prepared by pyrolysis, under an ammoniac atmosphere, of the reaction product between a B-trihalogenoborazole and a primary amine RNH2 when R is a hydrocarbon radical eventually substituted containing from 1 to 6 carbon atoms inclusively
[fr]Du nitrure de bore de haute purete, exempt de silicium et a faible teneur en carbone, est prepare par pyrolyse sous une atmosphere d'ammoniac du produit de reaction entre un B-trihalogenoborazole et une amine primaire: H2 N - R ou R est un radical hydrocarbone, eventuellement substitue, contenant inclusivement entre 1 et 6 atomes de carbone
[en] Present work deals with prediction of surface roughness using cutting parameters along with in-process measured cutting force and tool vibration (acceleration) during turning of Ti-6Al-4V with cubic boron nitride (CBN) inserts. Full factorial design is used for design of experiments using cutting speed, feed rate and depth of cut as design variables. Prediction model for surface roughness is developed using response surface methodology with cutting speed, feed rate, depth of cut, resultant cutting force and acceleration as control variables. Analysis of variance (ANOVA) is performed to find out significant terms in the model. Insignificant terms are removed after performing statistical test using backward elimination approach. Effect of each control variables on surface roughness is also studied. Correlation coefficient (R2 pred) of 99.4% shows that model correctly explains the experiment results and it behaves well even when adjustment is made in factors or new factors are added or eliminated. Validation of model is done with five fresh experiments and measured forces and acceleration values. Average absolute error between RSM model and experimental measured surface roughness is found to be 10.2%. Additionally, an artificial neural network model is also developed for prediction of surface roughness. The prediction results of modified regression model are compared with ANN. It is found that RSM model and ANN (average absolute error 7.5%) are predicting roughness with more than 90% accuracy. From the results obtained it is found that including cutting force and vibration for prediction of surface roughness gives better prediction than considering only cutting parameters. Also, ANN gives better prediction over RSM models. (paper)
[en] Highlights: • Investigated keff of PBT composites contain spherical and platelet-shaped hBN. • PMC filled with spherical hBN of relatively small size possessed the highest keff. • Hybrid hBN fillers showed synergistic effect on PMC's keff. • Platelet-shaped hBN of significant size difference exhibited most increase in keff.
[en] Inferring causality has long been a challenging task in environmental impact studies and monitoring programs, mostly because of the problem of confounding bias, i.e. the difficulty of separating impact from natural variation. Traditional approaches for dealing with confounding, despite improvements in study design and statistical analysis, are inadequate. Using aquatic biota as a case study, this review explains the limitations of traditional methods used to separate the impact of human-made pollution from natural variation in the environment. Advantages and disadvantages of the traditional and novel techniques are enumerated. Bayesian networks (BNs) and structural equation modelling (SEM) as causal modelling techniques are introduced as approaches to improve environmental impact monitoring.
[en] The writings of the text on the last line, left column on the 4th page and the text on lines 8th, 10th, 11th and 16th in the 4th paragraph, left column and on lines from 1st to 8th in the 1st paragraph, right column on the 5th page, and the text on line 4th in the 1st paragraph, left column on the 9th page, and Figure 3 and its caption on the 5th page in the original version of this article were unfortunately incorrect.
[en] We study thermoelectric properties of zigzag graphene nanoribbon (ZGNR)–boron nitride (BN) junctions coupled to square electrodes using nonequilibrium Green function formalism in the linear response regime. The embedding of hexagonal BN cells into the ZGNR results in the change of the thermoelectric properties with the length and position of BN cells. The influence of the width variation on the electrical conductance and the Seebeck coefficient of the ZGNR–BN junctions is examined. Also, the coupling of asymmetric electrodes to the ZGNR–BN junctions and the pristine ZGNR is considered. It is observed that the asymmetric electrodes lead to the increase of the Seebeck coefficients of both structures, while the phonon thermal conductance is decreased because of the reduction of the phonon transport in inhomogeneous structures. Our results predict that the thermoelectric efficiency of the system is increased by embedding the hexagonal BN cells, as well as coupling to the asymmetric electrodes. (paper)
[en] Highlights: • Piezopotential of boron nitride honeycombs (BNHCs) is studied by FE-MD simulations. • BNHCs possess a combination of tensile and shear piezoelectricity. • Piezopotential properties of BNHCs can be tailored by adjusting their cell length. • BNHCs possess extremely high specific piezopotential coefficients. Exploring new piezoelectric nanomaterials (PNMs) with unique piezoelectric and piezopotential properties plays a crucial role in designing novel piezotronics nanodevices. In this paper, using hybrid finite element-molecular dynamics simulations, we find a remarkable piezopotential property in the recently proposed boron nitride honeycomb (BNHC) structures. Our results show that, due to their unique polarization distribution BNHCs possess a tensile piezoelectricity in the armchair direction and a shear piezoelectricity in the zigzag direction. It is expected that such a combination of tensile and shear piezoelectricity in BNHCs render them have the ability to harvest almost all types of mechanical energies in the ambient environment. Moreover, the elastic constant, the piezoelectric coefficient and the dielectric constant of BNHCs are found to decrease when their cell length increases, which makes the piezopotential coefficients of BNHCs significantly increase in this process. This observation indicates that the piezopotential properties of BNHCs can be efficiently tailored by adjusting their cell length. As for BNHCs with a proper cell length, we find that their specific piezopotential coefficients can become much larger than those of most existing PNMs. In addition to the remarkable piezopotential properties, a large failure strain is also observed in BNHCs. Such high specific piezopotential coefficients and failure strain in BNHCs render them appealing in the design of novel piezotronics nanodevices with ultralight weight and ultrahigh stretchability.