纳米氧化钛在皮革涂料中的应用

皮革涂饰在皮革制造中是非常重要的操作,其中要使用各种不同的化学品。纳米材料引入皮革涂饰使皮革行业有了革命性的进步,纳米材料被用于生产各种不同类型的皮革来满足消费者的各种需求。本研究的目的是结合皮革科学技术和纳米技术来获得拥有优良特性的更卓越品质的皮革。纳米氧化钛采用溶胶凝胶过程合成,作为一个组分用于皮革涂料混合物中。纳米氧化钛的物理化学特性使用TEM、FE-SEM、EDAX、FT-IR和XRD进行了表征,纳米氧化钛涂饰皮革的表面形态采用AFM进行了评价,涂层的粘接性通过皮革涂层与皮革表面之间的结合强度来评价。传统的涂饰系统经常碰到涂层粘结不牢的问题。纳米氧化钛涂饰过的皮革呈现出更好的粘着性能,它增加了皮革表面与涂层之间的分子间交联。

Dispersion and attenuation of torsional wave in a viscoelastic layer bonded between a layer and a half-space of dry sandy media

Propagation of a torsional wave in a doubly-layered half-space structure of an initially stressed heterogeneous viscoelastic layer sandwiched between a layer and a half-space of heterogeneous dry sandy media is studied. A closed form complex expression for the velocity profile is obtained under effective boundary conditions. The real part of the complex expression provides a dispersion equation, and the imaginary part yields a damping equation. The derived dispersion and damped equations are in well agreement with the classical Love wave condition. In addition, to study the effect of the dissipation factor, the attenuation coefficient, the sandy parameters, the initial stress, the hetero- geneity parameters, and the thickness ratio parameter, some noteworthy contemplations are made by numerical calculations and graphical visuals. The results of this paper may present a deeper insight into the behaviour of propagation phenomena in heterogeneous viscoelastic and heterogeneous dry sandy materials that can provide a theoretical guide for the design and optimization in the field of earthquake engineering. The study also reveals that the presence of a damping part due to viscoelasticity affects the torsional wave propagation significantly.

Dispersion equation of magnetoelastic shear waves in irregular monoclinic layer

This paper studies the propagation of horizontally polarized shear waves in an internal magnetoelastic monoclinic stratum with irregularity in lower interface. The stratum is sandwiched between two magnetoelastic monoclinic semi-infinite media. Dispersion equation is obtained in a closed form. In the absence of magnetic field and irregularity of the medium, the dispersion equation agrees with the equation of classical case in three layered media. The effects of magnetic field and size of irregularity on the phase velocity are depicted by means of graphs.

晚期折返过程含石墨流体包裹体及其意义：以印度两个不同地体为例

Archean greenstone belts and Proterozoic granulite mobile belts are products of fundamentally different tectonic processes that culminated in different levels of crustal incision.The present study focuses on graphite-bearing fluid inclusions from two such terrains in India,the Angul domain of Eastern Ghats Mobile Belt and Hutti-Maski schist belt of the eastern Dharwar greenstone-granite belt.In beth cases,a high population of such inclusions within the fluid inclusion assemblage rules out the possibility of graphite being a captive phase,and instead confirms that it was deposited by the fluid within the inclusion cavity.Graphite is usually observed to be occurring with either pure water or a pure carbonic( CO_2 only)liquid,or with a CH_4 dominated carbonic liquid without vapor at room temperature.Graphite precipitation in inclusions is brought about by reaction of the CO2 and CH4 trapped as a homogeneous fluid to give rise to H_2O and C(graphite).Molar volume calculations for the CO_2-CH_4 mixture assuming an appropriate PVTX relationship indicates that there is a substantial increase in volume with decreasing pressure at a given temperature.The reaction producing graphite and H_2O from CH_4 and CO_2 involves substantial volume reduction,and hence would be favored when the rock undergoes rapid exhumation.Graphite-beating inclusions in quartz in a late-stage leucosome from migmatites in the Angul domain of the EGMB are accompanied by other fluid inclusion evidence for isothermal decompression.In the Hutti-Maski schist belt of the eastern Dharwar Craton,graphite-bearing inclusions occur in structurally controlled quartz veins(often auriferous)within metamorphosed mafic volcanics(schists and amphibolites).The Raman spectra indicate that graphites in fluid inclusions from the Hutti-Maski schist belt have both ordered(O)and the disordered(D)peaks,whereas those from the Angul area of EGMB lack the disordered(D)peaks, with both having perfectly symmetrical‘S’peak.This implies that in both belts,exhumation from the burial depth maxima was a rapid process.However,the Hutti-Maski schist belt experienced a lower amount of uplift than the Angul domain,where the driving mechanism led to a deeper level of incision.This difference in the extent and rate of exhumation is speculated to be related to a fundamental difference in the nature of tectonism.A more detailed comparative study of the fluid inclusion characteristics would possibly throw more light on the changing tectonic style from the Archean to the Proterozoic,a topic that is extensively debated.

Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

Propagation of Love waves in non-homogeneous substratum over initially stressed heterogeneous half-space

The paper studies the propagation of Love waves in a non-homogeneous substratum over an initially stressed heterogeneous half-space. The dispersion equation of phase velocity is derived. The velocities of Love waves are calculated numerically as a function of kH and presented in a number of graphs, where k is the wave number, and H is the thickness of the layer. The case of Gibson＇s half-space is also considered. It is observed that the speed of Love waves is finite in the vicinity of the surface of the half-space and vanishes as the depth increases for a particular wave number. It is also observed that an increase in compressive initial stresses causes decreases of Love waves velocity for the same frequency, and the tensile initial stress of small magnitude in the half-space causes increase of the velocity.

Influence of linearly varying density and rigidity on torsional surface waves in inhomogeneous crustal layer

The present work deals with the possibility of propagation of torsional surface wave in an inhomogeneous crustal layer over an inhomogeneous half space. The layer has inhomogeneity which varies linearly with depth whereas the inhomogeneous half space exhibits inhomogeneity of three types, namely, exponential, quadratic, and hyperbolic discussed separately. The dispersion equation is deduced for each case in a closed form. For a layer over a homogeneous half space, the dispersion equation agrees with the equa- tion of the classical case. It is observed that the inhomogeneity factor due to linear variation in density in the inhomogeneous crustal layer decreases as the phase velocity increases, while the inhomogeneity factor in rigidity has the reverse effect on the phase velocity.

Effect of irregularity on the propagation of torsional surface waves in an initially stressed anisotropic poro-elastic layer

The paper studies the propagation of torsional surface waves in an initially stressed anisotropic poro-elastic layer over a semi-infinite heterogeneous half space with linearly varying rigidity and density due to irregularity at the interface. The irregularity is taken in the half space in the form of a rectangle. It is observed that torsional surface waves propagate in this assumed medium. In the absence of the irregularity, the velocity equation of the torsional surface wave is also obtained. For a layer over a homogeneous half space, the velocity of torsional surface waves coincides with that of the Love waves.

印度选择食品和牙膏中的氟含量

在印度氟中毒是一个重要的公共卫生问题,它是印度许多地区的地方病。地方性氟中毒的主要原因是过量摄入微量元素(F-)。目前人们相信改食对摄氟具有很大影响;饮水含氟量高以及饮用这种高氟水的时间是氟中毒发生的关键因素。

An Interacting Gauge Field Theoretic Model for Hodge Theory: Basic Canonical Brackets

We derive the basic canonical brackets amongst the creation and annihilation operators for a two(1 + 1)-dimensional(2D) gauge field theoretic model of an interacting Hodge theory where a U(1) gauge field(Aμ) is coupled with the fermionic Dirac fields(ψ andˉψ). In this derivation, we exploit the spin-statistics theorem, normal ordering and the strength of the underlying six infinitesimal continuous symmetries(and the concept of their generators) that are present in the theory. We do not use the definition of the canonical conjugate momenta(corresponding to the basic fields of the theory) anywhere in our whole discussion. Thus, we conjecture that our present approach provides an alternative to the canonical method of quantization for a class of gauge field theories that are physical examples of Hodge theory where the continuous symmetries(and corresponding generators) provide the physical realizations of the de Rham cohomological operators of differential geometry at the algebraic level.

Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models

Despite a plethora of data being generated on the mechanical behavior of multi-principal element alloys,a systematic assessment remains inaccessible via Edisonian approaches.We approach this challenge by considering the specific case of alloy hardness,and present a machine-learning framework that captures the essential physical features contributing to hardness and allows high-throughput exploration of multi-dimensional compositional space.The model,tested on diverse datasets,was used to explore and successfully predict hardness in Al_(x)Ti_(y)(CrFeNi)_(1-x-y),Hf_(x)Co_(y)(CrFeNi)_(1-x-y)and Al_(x)(TiZrHf)_(1-x)systems supported by data from density-functional theory predicted phase stability and ordering behavior.The experimental validation of hardness was done on TiZrHfAlx.The selected systems pose diverse challenges due to the presence of ordering and clustering pairs,as well as vacancy-stabilized novel structures.We also present a detailed model analysis that integrates local partial-dependencies with a compositional-stimulus and model-response study to derive material-specific insights from the decision-making process.