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This paper aims to investigate the thermal stability and hydrophobicity of difference alkyl chain of silanes with silicon (Si) micro- and nanoparticles.

Sol-gel methods have been used to design superhydrophobic glass substrates through surface modification by using low-surface-energy Isooctyl trimethoxysilane (ITMS) and Ethyl trimethoxysilane (ETMS) solution. Hierarchical double-rough scale solid surface was built by Si micro- and nanoparticles to enhance the surface roughness. The prepared sol was applied onto glass substrate using dip-coating method and was dried at control temperature of 400°C inside the tube furnace.

The glass substrate achieved the water contact angle as high as 154 ± 2° and 150.4 ± 2° for Si/ITMS and Si/ETMS films, respectively. The Si/ITMS and Si/ETMS also were equipped with low sliding angle as low as 3° and 5°, respectively. The Si micro- and nanoparticles in the coating system have created nanopillars between them, which will suspend the water droplets. Both superhydrophobic coatings have showed good stability against high temperature up to 200°C as there are no changes in WCA shown by both coatings. Si/ITMS film sustains its superhydrophobicity after impacting with further temperature up to 400°C and turns hydrophobic state at 450°C.

Findings will be useful to develop superhydrophobic coatings with high thermal stability.

Sol method provides a suitable medium for the combination of organic-inorganic network to achieve high hydrophobicity with optimum surface roughness.

Application of different alkyl chain groups of silane resin blending with micro- and nanoparticles of Si pigments develops superhydrophobic coatings with high thermal stability.

The purpose of this paper is to construct a continuous time series model to study the thermal creep of rough surfaces in contact.

For normal loading, the contact between rough surfaces can often be modeled as the contact of an effective surface with a rigid fiat surface. A solution for the deformation of such equivalent surface, generated using fractal geometry, can be modified. However, in this study only the case of a single rough surface in contact with a rigid flat surface is considered. In the interface, the material is assumed to follow the idealized constitutive viscoelastic standard linear solid (SLS) model. Fractal geometry, through Cantor set theory, is utilized to model the roughness of the surface.

An asymptotic time series power law is obtained, which associates the creep load, the buck temperature and the creep of the fractal surface.

This law is only valid as long as the creep is of the size of the surface roughness. The modified model admits an analytical solution for the case when the behavior is linear viscoelastic. The proposed model shows a good agreement when compared with experimental results available in the literature.

The textile industry is evolving toward nanotechnology, which provides materials with self-cleaning properties. This paper aims to provide a thorough explanation of the green synthesis and mechanism of ZnO nanoparticles, with prospective applications of zinc oxide nanoparticles (ZnO NPs) in self-cleaning textiles.

This review introduces a green mechanism for the synthesis of ZnO NPs using plant extracts, their self-cleaning properties and the mechanisms of physical, chemical and biological self-cleaning actions for textile applications.

ZnO NPs are among the several nanoparticles that are beneficial for self-cleaning textiles because of their exceptional physical and chemical properties, although review publications addressing the use of ZnO NPs in textiles for self-cleaning are uncommon. These results indicate that the plant-synthesized ZnO NPs display excellent biological, physical and chemical self-cleaning properties, the mechanism of which involves photocatalysis, surface roughness and interactions between ZnO NPs and bacterial surfaces.

Nanoformulations of plant-synthesized ZnO have been reviewed to achieve promising self-cleaning textile properties and have not been reviewed earlier.

This paper aims to find an optimal surface treatment of commonly used polymeric substrates for achieve the high adhesion of printed structures. For this reason, the investigation of substrates surfaces from different perspectives is presented in this paper.

The contact angle measurements as well as the roughness measurements were realised for the analysis of surface properties of investigated substrates. The impact of applied chemical agents for surface treatment onto the wettability is analysed for polyimide, polyethylene terephthalate and polyethylene naphthalene substrates.

The results prove the correlation among wettability, surface energy and work of adhesion with respect to the theoretical background. The surface treatment of polymeric substrates by chemical agents, such as acetone, toluene, ethanol, isopropyl and fluor silane polymer, has a significant impact onto the wettability of substrates which affects the final deposition process of nanoinks.

The main benefit of the surfaces’ investigation presented in this paper lays in surface modification by readily available chemical agents for optimising the deposition process nanoinks used in inkjet printing technology.

The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.

Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.

According to the study results, several regression models were developed with a big variation in the coefficients of determination (R²). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R² equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.

The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.

To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

This paper aims to study the comparison between a response surface methodology (RSM) and artificial neural network (ANN) in the modelling and prediction of surface roughness during endmilling of glass-fibre-reinforced polymer composites.

Aiming to achieve this goal, several milling experiments were performed with polycrystalline diamond inserts at different machining parameters, namely, feed rate, cutting speed, depth of cut and fibre orientation angle. Mathematical model is created using central composite face-centred second-order in RSM and the adequacy of the model was verified using analysis of variance. ANN model is created using the back propagation algorithm.

With regard to the machining test, it was observed that feed rate is the dominant parameter that affects the surface roughness, followed by the fibre orientation. The comparison results show that models provide accurate prediction of surface roughness in which ANN performs better than RSM.

The data predicted from ANN are very nearer to experimental results compared to RSM; therefore, this ANN model can be used to determine the surface roughness for various fibre-reinforced polymer composites and also for various machining parameters.

Nacre is a biological material constituting the innermost layer of the shells of gastropods and bivalves. It consists of polygonal tablets of aragonite, tessellated to form individual layers and having the adjacent layers as well as the tablets within a layer bonded by a biopolymer. Due to its highly complex hierarchical microstructure, nacre possesses an outstanding combination of mechanical properties, the properties which are far superior to the ones that are predicted using techniques such as the rule of mixtures. Given these properties, a composite armor the structure of which mimics that of nacre may have improved performance over a monolithic armor having a similar composition and an identical areal density. The paper aims to discuss these issues.

In the present work, an attempt is made to model a nacre-like composite armor consisting of B₄C tablets and polyurea tablet/tablet interfaces. The armor is next tested with respect to impact by a solid right circular cylindrical (SRCC) rigid projectile, using a transient non-linear dynamics finite-element analysis. The ballistic-impact response and the penetration resistance of the armor are then compared with that of the B₄C monolithic armor having an identical areal density. Furthermore, the effect of various nacre microstructural features (e.g. surface profiling, micron-scale asperities, mineral bridges between the overlapping tablets lying in adjacent layers, and B₄C nano-crystallinity) on the ballistic-penetration resistance of the composite armor is investigated in order to identify an optimal nacre-like composite armor architecture having the largest penetration resistance.

The results obtained clearly show that a nacre-like armor possesses a superior penetration resistance relative to its monolithic counterpart, and that the nacre microstructural features considered play a critical role in the armor-penetration resistance.

The present work indicates that for a given choice of armor material, penetration resistance may be improved by choosing a structure resembling that of nacre.

The purpose of this paper is to develop a general mathematic approach to model the microstructures of porous structures produced by additive manufacturing (AM), which will result in fractal surface topography and higher roughness that have greater influence on the performance of porous structures.

The overall shapes of pores were modeled by triply periodic minimal surface (TPMS), and the micro-roughness details attached to the overall pore shapes were represented by Weierstrass–Mandelbrot (W-M) fractal representation, which was integrated with TPMS along its normal vectors. An index roughly reflecting the irregularity of fractal TPMS was proposed, based on which the influence of the fractal parameters on the fractal TPMS was qualitatively analyzed. Two complex samples of real porous structures were given to demonstrate the feasibility of the model.

The fractal surface topography should not be neglected at a micro-scale level. In addition, a decrease in the fractal dimension D_s may exponentially make the topography rougher; an increase in the height-scaling parameter G may linearly increase the roughness; and the number of the superposed ridges has no distinct influence on the topography. Furthermore, the synthesis method is general for all implicit surfaces.

The method provides an alternative way to shift the posteriori design paradigm of porous media to priori design mode through numeric simulation. Therefore, the optimization of AM process parameters, as well as the porous structure, can be potentially realized according to specific functional requirement.

The synthesis of TPMS and W-M fractal geometry was accomplished efficiently and was general for all implicit freeform surfaces, and the influence of the fractal parameters on the fractal TPMS was analyzed more systematically.

The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision, in gathering knowledge about the structure, content and the surrounding environment of a real-world natural scene, at a quick glance accurately. This paper proposes a set of novel features to determine the gist of a given scene based on dominant color, dominant direction, openness and roughness features.

The classification system is designed at two different levels. At the first level, a set of low level features are extracted for each semantic feature. At the second level the extracted features are subjected to the process of feature evaluation, based on inter-class and intra-class distances. The most discriminating features are retained and used for training the support vector machine (SVM) classifier for two different data sets.

Accuracy of the proposed system has been evaluated on two data sets: the well-known Oliva-Torralba data set and the customized image data set comprising of high-resolution images of natural landscapes. The experimentation on these two data sets with the proposed novel feature set and SVM classifier has provided 92.68 percent average classification accuracy, using ten-fold cross validation approach. The set of proposed features efficiently represent visual information and are therefore capable of narrowing the semantic gap between low-level image representation and high-level human perception.

The method presented in this paper represents a new approach for extracting low-level features of reduced dimensionality that is able to model human perception for the task of scene classification. The methods of mapping primitive features to high-level features are intuitive to the user and are capable of reducing the semantic gap. The proposed feature evaluation technique is general and can be applied across any domain.

This study aims to investigate the contact behavior of nominal curved surfaces with random roughness.

A deterministic model was applied to investigate the contact behavior. Numerical calculations were conducted on Gaussian and fractal profiles under a range of loading conditions. The deformation behavior is characterized in terms of three regimes including the elastic, elastoplastic and plastic regimes.

A linear relationship was observed between the real contact areas and normal loads, which is mainly governed by the plastic deformation. Surface roughness changes contact behavior by influence the transition of deformation regimes. Rougher surfaces generally demonstrate higher saturated plastic ratios.

The contact behavior of nominally curved surfaces with random roughness is understood in terms of the evolution of real contact areas and plastic ratios.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2019-0190.