Multiscale Morphology Research Articles

Improvement of the self-cleaning capabilities and transparency of cover glasses for solar cell applications by modification with atmospheric pressure plasma

Using a cover glass is indispensable for protecting solar cells in photovoltaic systems. Herein, the surface of the cover glass was modified by atmospheric pressure plasma to enhance the self-cleaning effect without degrading the transmittance. A lower surface energy was achieved by depositing fluorocarbon polymers, and a micro-nano multi-scale morphology was built on the cover glass within 50 s. These two properties led to an increase in the hydrophobicity, which enhanced the self-cleaning effect of the surface. The morphology of the surface also helped to improve the transparency by reducing reflections. Both the enhanced self-cleaning effect and the improved transparency induced by the atmospheric pressure plasma treatment were confirmed by analyzing the total conversion efficiency of a solar cell by outdoor field testing.

A Novel Multi-scale 3D Area Morphological Filtering Method for Airborne LiDAR Building Extraction

Automatic building extraction is one of the most important issues in the fields of geoscience and remote sensing. In this letter, by introducing the idea of area morphology to the analysis of 3-D point clouds, a novel approach for automatic building extraction from airborne LiDAR data was proposed. At first, single scale area opening and area closing operator was used to produce normalized point clouds. With the normalized point clouds as input, multi-scale area morphology was employed to obtain connected regions, and then tree points were removed by PCA based local structural analyzing technique. Finally, building regions were extracted by analyzing geometry properties of the obtained connected regions without tree points. Experiments for different terrains were conducted. And the corresponding experimental results are very promising.

Multiscale morphology based illumination normalization with enhanced local textures for face recognition

A key challenge of face recognition is to obtain illumination invariant face images while preserving the discriminative features. The locations and shapes of small-scale features (e.g. eyebrows, eyes, nostrils, a mouth, etc.) are usually treated as key features for face recognition. However, it has also been observed that the local texture information of facial regions contains intrinsic facial features and needs to be enhanced to improve performance. To compensate for the illumination effects that appeared while extracting both the small-scale features and the texture information, we used multiscale morphological techniques. We used a generalized dynamic morphological quotient image (GDMQI) method based on Retinex theory and multiscale morphological closing to solve the artifact problem discussed in previous works. The proposed method consisted of two main steps: (i) illumination estimation and (ii) texture enhancement. The proposed method showed improved performance when using the CMU PIE, AR and Extended Yale-B databases.

Multiscale morphological manifold for rolling bearing fault diagnosis

The vibration signals of fault rolling bearing are high-dimensional information with complex components. In order to identify different classes of bearing fault, a new multiscale morphological manifold method based on multiscale morphology and manifold learning is proposed. The multiscale morphological manifold method consists of three main steps. Firstly, multiscale difference filter based on multiscale morphological transformation is applied to obtain multiscale observation results of each signal sample. Secondly, the nonlinear feature vectors of each signal sample are constructed according to the observation approach. Finally, manifold learning is introduced to extract the low-dimensional multiscale morphological manifold features through reducing the dimension of nonlinear features. The low-dimensional multiscale morphological manifold features can reveal the differences of signal classes, which are applicable for fault diagnosis. The performance of proposed method is tested by experimental data from bearings with different types of defects. Experimental verifications confirm that the proposed method is applicable and effective for rolling bearing fault diagnosis.

Erratum to: Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

Erratum to: Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

Active multi-scale modeling and gas permeability study of porous metal fiber sintered felt for proton exchange membrane fuel cells

Porous metal fiber sintered felt (PMFSF) is a promising critical component in proton exchange membrane fuel cells, having the ability of simultaneously acting as the flow field plate, gas diffusion layers and also the catalyst layers support, and owing the property of multi-scale surface morphology. A simple multi-scale mathematical method was proposed to actively construct three dimensional models of PMFSF’s microstructure by synthesizing the implicit periodic surface (PS) model and the Weierstrass-Mandelbrot (W-M) fractal geometry. In this method, the PS model described the macro overall fiber shape, and the W-M fractal geometry modeled the micro fractal roughness topography attached. Based on the method, multi-scale fractal PMFSF models were reconstructed according to morphology parameters of physical PMFSFs, and were discretized in ANSYS/ICEM to generate refined mesh for computational fluid dynamics analysis. To verify the validity of the proposed modeling approach, PMFSFs with different porosity and fiber orientation are generated, and then the effects of the fractal surface topography and the fractal parameters such as fractal dimension and height scaling parameter on the gas permeability of PMFSF were investigated. The numeric simulation results show that the influence of the fractal topography on the in-plane and through-plane permeability of PMFSF cannot be ignored, and the permeability of fractal PMFSF models agrees with experimental measurements better. Especially, the results imply that the fractal morphology may have the potential to adjust the anisotropic properties of PMFSFs’ permeability. It is further found that the larger the fractal dimension is and the lower the height scaling parameter is, the better the permeability of PMFSF will be. The synthesis approach and numerical simulation method may facilitate the development of active functional design mode to predict and optimize key characteristics of PMFSF ahead of manufacture.

Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains. One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment. In this work, we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum (MPS) through a multi-scale morphology analysis procedure. The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves. Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

Superhydrophobic carbon nanotube/polydimethylsiloxane composite coatings

This paper reports a method for fabricating carbon nanotube (CNT)/polydimethylsiloxane (PDMS) composite superhydrophobic coatings. With toluene as a solvent, the coating is obtained directly by spray of CNT/PDMS/toluene suspension. The hydrophobicity and micro-/nanostructure of the coatings are studied with respect to the mass ratio (MR) of CNT to PDMS. Based on the multiscale morphology analysis, it is shown that the nanoscale roughness is essential for achieving superhydrophobicity. To form nanoscale rough surface and obtain a stable superhydrophobic coating, MR>0·3 is recommended. In addition, such coatings also show small slide angle, low adhesion strength and long term stability of the coated surface. The method reported in this study is low cost and especially suitable for engineering applications.

Characterization of Multi-scale Morphology and Superhydrophobicity of Water Bamboo Leaves and Biomimetic Polydimethylsiloxane (PDMS) Replicas

The morphology and wettability of Water Bamboo Leaves (WBL) and their biomimetic replicas were investigated. The particular morphology structures of samples were characterized by Scanning Electron Microscopy (SEM) and Confocal Laser Scanning Microscopy (CLSM). The static wettability of samples was assessed by contact angle measurements, while the dynamic wettability was analyzed by high speed camera system. The wettability mechanism of WBL was also explained by Cassie model. Artificial surfaces were fabricated by duplicating WBL surface microstructures using PDMS in large area (5 cm (3 cm). The results show the main structure characteristics of this leaf surface are sub-millimeter groove arrays, micron-scale papillae and a superimposed layer with 3D epicuticular wax sculptures hierarchical structure, and the static Water Contact Angle (WCA) of 151°±2° and Water Sliding Angle (WSA) of 4°-6° indicate that WBL surface is superhydrophobic. The combination of wax film and microstructure of WBL surface gives its surface excellent superhydrophobic property. Complex hierarchical patterns with features from sub-millimeter to micron-scale range are well reproduced. The reason for the absence of nanostructures is melting of plant epidermal wax during the curing process. The WCA values on artificial WBL and negative PDMS replica are 146° ± 3° and 137° ± 2°, respectively, demonstrating preferable hydrophobicity. Differences in wetting behavior between natural leaves and artificial leaves originate from an inaccurate replication of the chemistry and structures of the three-dimensional wax projections on the leaf surface. Nevertheless, the morphological features of the leaf transferred to the replica improve significantly the hydrophobic properties of the replica when compared with the smooth PDMS reference. This study may provide an inspiration for the biomimetic design and construction of large area roughness-induced hydrophobic and anti-sticking material surface.

Plant Identification Based on Leaf Midrib Cross-Section Images Using Fractal Descriptors.

The correct identification of plants is a common necessity not only to researchers but also to the lay public. Recently, computational methods have been employed to facilitate this task, however, there are few studies front of the wide diversity of plants occurring in the world. This study proposes to analyse images obtained from cross-sections of leaf midrib using fractal descriptors. These descriptors are obtained from the fractal dimension of the object computed at a range of scales. In this way, they provide rich information regarding the spatial distribution of the analysed structure and, as a consequence, they measure the multiscale morphology of the object of interest. In Biology, such morphology is of great importance because it is related to evolutionary aspects and is successfully employed to characterize and discriminate among different biological structures. Here, the fractal descriptors are used to identify the species of plants based on the image of their leaves. A large number of samples are examined, being 606 leaf samples of 50 species from Brazilian flora. The results are compared to other imaging methods in the literature and demonstrate that fractal descriptors are precise and reliable in the taxonomic process of plant species identification.

Topological modeling and classification of mammographic microcalcification clusters.

The presence of microcalcification clusters is a primary sign of breast cancer; however, it is difficult and time consuming for radiologists to classify microcalcifications as malignant or benign. In this paper, a novel method for the classification of microcalcification clusters in mammograms is proposed. The topology/connectivity of individual microcalcifications is analyzed within a cluster using multiscale morphology. This is distinct from existing approaches that tend to concentrate on the morphology of individual microcalcifications and/or global (statistical) cluster features. A set of microcalcification graphs are generated to represent the topological structure of microcalcification clusters at different scales. Subsequently, graph theoretical features are extracted, which constitute the topological feature space for modeling and classifying microcalcification clusters. k-nearest-neighbors-based classifiers are employed for classifying microcalcification clusters. The validity of the proposed method is evaluated using two well-known digitized datasets (MIAS and DDSM) and a full-field digital dataset. High classification accuracies (up to 96%) and good ROC results (area under the ROC curve up to 0.96) are achieved. A full comparison with related publications is provided, which includes a direct comparison. The results indicate that the proposed approach is able to outperform the current state-of-the-art methods. Significance: This study shows that topology modeling is an important tool for microcalcification analysis not only because of the improved classification accuracy but also because the topological measures can be linked to clinical understanding.

Multi-fractal hierarchy of single-walled carbon nanotube hydrophobic coatings.

A hierarchical structure is an assembly with a multi-scale morphology and with a large and accessible surface area. Recent advances in nanomaterial science have made increasingly possible the design of hierarchical surfaces with specific and tunable properties. Here, we report the fractal analysis of hierarchical single-walled carbon nanotube (SWCNT) films realized by a simple, rapid, reproducible, and inexpensive filtration process from an aqueous dispersion, then deposited by drytransfer printing method on several substrates, at room temperature. Furthermore, by varying the thickness of carbon nanotube random networks, it is possible tailoring their wettability due to capillary phenomena in the porous films. Moreover, in order to describe the wetting properties of such surfaces, we introduce a two-dimensional extension of the Wenzel-Cassie-Baxter theory. The hierarchical surface roughness of SWCNT coatings coupled with their exceptional and tunable optical and electrical properties provide an ideal hydrophobic composite surface for a new class of optoelectronic and nanofluidic devices.

Exploiting the hierarchical morphology of single-walled and multi-walled carbon nanotube films for highly hydrophobic coatings.

Self-assembled hierarchical solid surfaces are very interesting for wetting phenomena, as observed in a variety of natural and artificial surfaces. Here, we report single-walled (SWCNT) and multi-walled carbon nanotube (MWCNT) thin films realized by a simple, rapid, reproducible, and inexpensive filtration process from an aqueous dispersion, that was deposited at room temperature by a dry-transfer printing method on glass. Furthermore, the investigation of carbon nanotube films through scanning electron microscopy (SEM) reveals the multi-scale hierarchical morphology of the self-assembled carbon nanotube random networks. Moreover, contact angle measurements show that hierarchical SWCNT/MWCNT composite surfaces exhibit a higher hydrophobicity (contact angles of up to 137°) than bare SWCNT (110°) and MWCNT (97°) coatings, thereby confirming the enhancement produced by the surface hierarchical morphology.

Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis

Scales of IAMMA are adaptively determined by morphological features of signal, thus fault features of a hydraulic pump fault signal presented in multi-scales can be adaptively demodulated. In some coefficient range, IAMMA outperforms AMMA in demodulation ability based on the same SE, and it is less susceptible to noises than AMMA. The best performance of IAMMA with triangle SE is stronger than that of IAMMA with plat and semi-circle SE when they demodulate the same fault signal of hydraulic pump. Compared with traditional demodulation methods of HT and TKEO, IAMMA is adaptive and has stronger demodulation ability. An evaluation method based on kurtosis, power and standard deviation is proposed, by which some PFs which are rich in fault features can be selected as data source.

Theoretical description of structural and electronic properties of organic photovoltaic materials.

We review recent progress in the modeling of organic solar cells and photovoltaic materials, as well as discuss the underlying theoretical methods with an emphasis on dynamical electronic processes occurring in organic semiconductors. The key feature of the latter is a strong electron-phonon interaction, making the evolution of electronic and structural degrees of freedom inseparable. We discuss commonly used approaches for first-principles modeling of this evolution, focusing on a multiscale framework based on the Holstein-Peierls Hamiltonian solved via polaron transformation. A challenge for both theoretical and experimental investigations of organic solar cells is the complex multiscale morphology of these devices. Nevertheless, predictive modeling of photovoltaic materials and devices is attainable and is rapidly developing, as reviewed here.

Superhydrophobic and anti-icing properties at overcooled temperature of a fluorinated hybrid surface prepared via a sol-gel process.

A superhydrophobic surface with anti-icing performance has been the focus of research, but few studies have reported the effective and low cost strategy that met the requirements under overcooled conditions. In this article, the fluorinated sol-gel colloid coatings were simply prepared via hydrolytic condensation of nanosilica sol, methyltriethoxysilane (MTES) and 3-[(perfluorohexylsulfonyl)amino]propyltriethoxysilane (HFTES). The multi scale morphology and chemical composition of the artificial surfaces were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS). The influence of the surface roughness structure and fluorinated groups on the wettability and freezing delay time of the colloid surface under overcooled conditions were explored. As the HFTES content was higher than 6 wt%, the prepared colloid surface showed excellent superhydropobicity with a contact angle (CA) of about 166° at room temperature. The CA gradually reduced with the decrease of the temperature. Only the samples with high HFTES contents (above 30 wt%) exhibited special superhydrophobic and anti-icing properties under freeze temperature. Besides the surface roughness structure, the high fluoride enrichment on the surface plays a major role in the superhydrophobic and anti-icing properties under overcooled conditions.

Concrete Image Segmentation Based on Multiscale Mathematic Morphology Operators and Otsu Method

The aim of the current study lies in the development of a reformative technique of image segmentation for Computed Tomography (CT) concrete images with the strength grades of C30 and C40. The results, through the comparison of the traditional threshold algorithms, indicate that three threshold algorithms and five edge detectors fail to meet the demand of segmentation for Computed Tomography concrete images. The paper proposes a new segmentation method, by combining multiscale noise suppression morphology edge detector with Otsu method, which is more appropriate for the segmentation of Computed Tomography concrete images with low contrast. This method cannot only locate the boundaries between objects and background with high accuracy, but also obtain a complete edge and eliminate noise.

Multi-scale Heat Kernel based Volumetric Morphology Signature.

Here we introduce a novel multi-scale heat kernel based regional shape statistical approach that may improve statistical power on the structural analysis. The mechanism of this analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral mesh. In order to capture profound volumetric changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between two boundary surfaces by computing the streamline in the tetrahedral mesh. Secondly, we propose a multi-scale volumetric morphology signature to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the volumetric morphology signatures and generate the internal structure features. The multi-scale and physics based internal structure features may bring stronger statistical power than other traditional methods for volumetric morphology analysis. To validate our method, we apply support vector machine to classify synthetic data and brain MR images. In our experiments, the proposed work outperformed FreeSurfer thickness features in Alzheimer's disease patient and normal control subject classification analysis.

Tuning the surface wettability of carbon nanotube carpets in multiscale hierarchical solids

An attractive approach of increasing functionality of solid surfaces is to create hierarchical multiscale morphology by attaching tailored carpet-like arrays of Carbon nanotubes (CNT) on them. Such surfaces offer fractal morphology along with unprecedented increase in specific surface areas, and significantly boost the potency of porous materials used in surface-active applications. However, full utilization of these structures will require intimate interaction between the solid surface and its environmental fluid. CNT arrays tend to be hydrophobic, which limit their effectiveness in aqueous environments. In this research, we investigated two different surface modifications methods to induce hydrophilic property to CNT nano-carpets on graphitic substrates: dry oxygen plasma treatment and wet sol–gel oxide coating. Structure, morphology, composition and chemistry of these multiscale surfaces have been related to wettability and water flow properties. Plasma oxygen treatments did not alter the surface morphology, but induced temporary wettability, that could be reversed by heat treatment. On the other hand, sol–gel treatment permanently coated the nanotubes with a strongly bonded layer of amorphous SiO2. This coating imparts permanent alterations in surface chemistry, contact angle, wettability and water flow. Porous carbon foams were coated with CNT arrays and their water permeability measured before and after sol–gel silica coating. The hydrophilic coating was seen to increase flow rate and reduce pressure build-up. These results have important implications on all devices that utilize surface activity of porous solids, such as catalytic membranes, antimicrobial filters, and microfluidic sensors.

COSMIC WEB AND STAR FORMATION ACTIVITY IN GALAXIES ATz∼ 1

We investigate the role of the delineated cosmic web/filaments on the star formation activity by exploring a sample of 425 narrow-band selected H{\alpha} emitters, as well as 2846 color-color selected underlying star-forming galaxies for a large scale structure (LSS) at z=0.84 in the COSMOS field from the HiZELS survey. Using the scale-independent Multi-scale Morphology Filter (MMF) algorithm, we are able to quantitatively describe the density field and disentangle it into its major components: fields, filaments and clusters. We show that the observed median star formation rate (SFR), stellar mass, specific star formation rate (sSFR), the mean SFR-Mass relation and its scatter for both H{\alpha} emitters and underlying star-forming galaxies do not strongly depend on different classes of environment, in agreement with previous studies. However, the fraction of H{\alpha} emitters varies with environment and is enhanced in filamentary structures at z~1. We propose mild galaxy-galaxy interactions as the possible physical agent for the elevation of the fraction of H{\alpha} star-forming galaxies in filaments. Our results show that filaments are the likely physical environments which are often classed as the "intermediate" densities, and that the cosmic web likely plays a major role in galaxy formation and evolution which has so far been poorly investigated.