Deep Learning Applied on Functional Materials Characterization

Topic Information

Dear Colleagues,

Over the past few decades, deep learning has evolved into one of the most sought out techniques in many areas of research. It has developed into a technique that unites many different fields of science and engineering. The advances in chip processing abilities (GPU) and machine learning algorithms are responsible for the tremendous development observed in deep learning. These advances enable deep learning algorithms to train models from large amounts of data with high efficiency. The processing of multivariate data, image recognition, and processing are some of the applications of deep learning techniques that have attracted researchers to the field of material characterization. The development of advanced and innovative materials also requires advanced characterization techniques. Deep learning techniques can provide the ample support that is required for material characterization. The aim of this Special Issue is to collect high-quality research work on the application of deep learning techniques in material characterization. The topics of interest include, but are not limited to, the following:

• Damage assessment;
• Classification of damages;
• Identification of the stage of the defects;
• Prediction of the lifetime;
• Identification of the material phases;
• Microstructural analysis;
• Non-destructive evaluation;
• Material characterization;
• Civil structures.

Dr. Claudia Barile
Dr. Giovanni Pappalettera
Dr. Vimalathithan Paramsamy Kannan
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Coatings coatings	2.9	5.0	2011	14.5 Days	CHF 2600
Materials materials	3.1	5.8	2008	13.9 Days	CHF 2600
Nanomaterials nanomaterials	4.4	8.5	2010	14.1 Days	CHF 2400
Polymers polymers	4.7	8.0	2009	14.5 Days	CHF 2700

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Published Papers (13 papers)

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18 pages, 4028 KiB  

Open AccessArticle

The Flexural Strength Prediction of Carbon Fiber/Epoxy Composite Using Artificial Neural Network Approach

by Veena Phunpeng, Karunamit Saensuriwong, Thongchart Kerdphol and Pichitra Uangpairoj

Materials 2023, 16(15), 5301; https://doi.org/10.3390/ma16155301 - 28 Jul 2023

Cited by 10 | Viewed by 2837

Abstract

There is a developing demand for natural resources because of the growing population. Alternative materials have been developed to address these shortages, concentrating on characteristics such as durability and lightness. By researching composite materials, natural materials can be replaced. It is vital to [...] Read more.

There is a developing demand for natural resources because of the growing population. Alternative materials have been developed to address these shortages, concentrating on characteristics such as durability and lightness. By researching composite materials, natural materials can be replaced. It is vital to consider the mechanical properties of composite materials when selecting them for a specific application. This study aims to measure the flexural strength of carbon fiber/epoxy composites. However, the cost of forming these composites is relatively high, given the expense of composite materials. Consequently, this study seeks to reduce molding costs by predicting flexural strength. Conducting many tests for each case is costly; therefore, it is necessary to discover an economical method. To accomplish this, the flexural strength of carbon fiber/epoxy composites was investigated using an artificial neural network (ANN) technique to reduce the expense of material testing. The output parameter investigated was flexural strength, while input parameters included ply orientation, manufacturing, width, thickness, and graphite filler percentage. The scope alternative was determined by identifying the values of variables that substantially affect the flexural strength. The prediction of flexural strength was deemed acceptable if the mean squared error (MSE) value was less than 0.001, and the coefficient of determination (R²) was greater than or equal to 0.95. The obtained results demonstrated an MSE of 0.003039 and an R² value of 0.95274, indicating a low prediction error and high prediction accuracy for all flexural strength data. Thus, the outcomes of this study provide accurate predictions of flexural strength in the composite materials. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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17 pages, 6519 KiB  

Open AccessArticle

Super-Resolution Processing of Synchrotron CT Images for Automated Fibre Break Analysis of Unidirectional Composites

by Radmir Karamov, Christian Breite, Stepan V. Lomov, Ivan Sergeichev and Yentl Swolfs

Polymers 2023, 15(9), 2206; https://doi.org/10.3390/polym15092206 - 6 May 2023

Cited by 3 | Viewed by 2888

Abstract

Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is [...] Read more.

Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is not sufficient for a fully automated analysis of continuous mechanical deformations. We therefore investigate the possibility of increasing the quality of low-resolution in situ scans by means of super-resolution (SR) using 3D deep learning techniques, thus facilitating the subsequent fibre break identification. We trained generative neural networks (GAN) on datasets of high—(0.3 μm) and low-resolution (1.6 μm) statically acquired images. These networks were then applied to a low-resolution (1.1 μm) noisy image of a continuously loaded specimen. The statistical parameters of the fibre breaks used for the comparison are the number of individual breaks and the number of 2-plets and 3-plets per specimen volume. The fully automated process achieves an average accuracy of 82% of manually identified fibre breaks, while the semi-automated one reaches 92%. The developed approach allows the use of faster, low-resolution in situ tomography without losing the quality of the identified physical parameters. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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14 pages, 5827 KiB  

Open AccessArticle

Deep Learning Assisted Optimization of Metasurface for Multi-Band Compatible Infrared Stealth and Radiative Thermal Management

by Lei Wang, Jian Dong, Wenjie Zhang, Chong Zheng and Linhua Liu

Nanomaterials 2023, 13(6), 1030; https://doi.org/10.3390/nano13061030 - 13 Mar 2023

Cited by 16 | Viewed by 3942

Abstract

Infrared (IR) stealth plays a vital role in the modern military field. With the continuous development of detection technology, multi-band (such as near-IR laser and middle-IR) compatible IR stealth is required. Combining rigorous coupled wave analysis (RCWA) with Deep Learning (DL), we design [...] Read more.

Infrared (IR) stealth plays a vital role in the modern military field. With the continuous development of detection technology, multi-band (such as near-IR laser and middle-IR) compatible IR stealth is required. Combining rigorous coupled wave analysis (RCWA) with Deep Learning (DL), we design a Ge/Ag/Ge multilayer circular-hole metasurface capable of multi-band IR stealth. It achieves low average emissivity of 0.12 and 0.17 in the two atmospheric windows (3~5 μm and 8~14 μm), while it achieves a relatively high average emissivity of 0.61 between the two atmospheric windows (5~8 μm) for the purpose of radiative thermal management. Additionally, the metasurface has a narrow-band high absorptivity of 0.88 at the near-infrared wavelength (1.54 μm) for laser guidance. For the optimized structure, we also analyze the potential physical mechanisms. The structure we optimized is geometrically simple, which may find practical applications aided with advanced nano-fabrication techniques. Also, our work is instructive for the implementation of DL in the design and optimization of multifunctional IR stealth materials. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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14 pages, 4029 KiB  

Open AccessArticle

Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize

by Hang Xue, Yang Yang, Xiping Xu, Ning Zhang and Yaowen Lv

Appl. Sci. 2023, 13(6), 3507; https://doi.org/10.3390/app13063507 - 9 Mar 2023

Cited by 3 | Viewed by 2213

Abstract

Seed purity has an important impact on the yield and quality of maize. Studying the spectral characteristics of hybrid maize and exploring the rapid and non-destructive detection method of seed purity are conducive to the development of maize seed breeding and planting industry. [...] Read more.

Seed purity has an important impact on the yield and quality of maize. Studying the spectral characteristics of hybrid maize and exploring the rapid and non-destructive detection method of seed purity are conducive to the development of maize seed breeding and planting industry. The near-infrared spectral data of five hybrid maize seeds were collected in the laboratory. After eliminating the obvious noises, the multiple scattering correction (MSC) was applied to pretreat the spectra. PLS-DA, KNN, NB, RF, SVM-Linear, SVM-Polynomial, SVM-RBF, and SVM-Sigmaid were used as pattern recognition methods to classify five different types of maize seeds. The recognition accuracy of the models established by different algorithms was 84.4%, 97.6, 100%, 96.4, 99.2%, 100%, 98.4%, and 91.2%, respectively. The results indicated that hyperspectral imaging technology could be used for variety classification and the purity detection of maize seeds. To improve the calculation speed, using the principal component analysis (PCA) to reduce the dimension of hyperspectral data, we then established classification models based on characteristic wavelengths. The recognition accuracy of the models established by different algorithms was 80.8%, 86.8%, 98%, 94%, 96.8%, 98.4%, 94.4%, and 88.2%, respectively. The results showed that the selected sensitive wavelengths could be used to detect the purity of maize seeds. The overall results indicated that it was feasible to use near-infrared hyperspectral imaging technology for the variety identification and purity detection of maize seeds. This study also provides a new method for rapid and non-destructive detection of seed purity. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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20 pages, 15940 KiB  

Open AccessArticle

A Neural Network Framework for Validating Information–Theoretics Parameters in the Applications of Acoustic Emission Technique for Mechanical Characterization of Materials

by Claudia Barile, Giovanni Pappalettera, Vimalathithan Paramsamy Kannan and Caterina Casavola

Materials 2023, 16(1), 300; https://doi.org/10.3390/ma16010300 - 28 Dec 2022

Cited by 6 | Viewed by 1694

Abstract

A multiparameter approach is preferred while utilizing Acoustic Emission (AE) technique for mechanical characterization of composite materials. It is essential to utilize a statistical parameter, which is independent of the sensor characteristics, for this purpose. Thus, a new information–theoretics parameter, Lempel–Ziv (LZ) complexity, [...] Read more.

A multiparameter approach is preferred while utilizing Acoustic Emission (AE) technique for mechanical characterization of composite materials. It is essential to utilize a statistical parameter, which is independent of the sensor characteristics, for this purpose. Thus, a new information–theoretics parameter, Lempel–Ziv (LZ) complexity, is used in this research work for mechanical characterization of Carbon Fibre Reinforced Plastic (CFRP) composites. CFRP specimens in plain weave fabric configurations were tested and the acoustic activity during the loading was recorded. The AE signals were classified based on their peak amplitudes, counts, and LZ complexity indices using k-means++ data clustering algorithm. The clustered data were compared with the mechanical results of the tensile tests on CFRP specimens. The results show that the clustered data are capable of identifying critical regions of failure. The LZ complexity indices of the AE signal can be used as an AE descriptor for mechanical characterization. This is validated by studying the clustered signals in their time–frequency domain using wavelet transform. Finally, a neural network framework based on SqueezeNet was trained using the wavelet scalograms for a quantitative validation of the data clustering approach proposed in this research work. The results show that the proposed method functions at an efficiency of more than 85% for three out of four clustered data. This validates the application of LZ complexity as an AE descriptor for AE signal data analysis. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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26 pages, 11281 KiB  

Open AccessArticle

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

by Jesús de-Prado-Gil, Covadonga Palencia, P. Jagadesh and Rebeca Martínez-García

Materials 2022, 15(15), 5232; https://doi.org/10.3390/ma15155232 - 28 Jul 2022

Cited by 19 | Viewed by 2551

Abstract

A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the [...] Read more.

A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%). Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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17 pages, 3718 KiB  

Open AccessArticle

An Improved U-Net Image Segmentation Method and Its Application for Metallic Grain Size Statistics

by Peng Shi, Mengmeng Duan, Lifang Yang, Wei Feng, Lianhong Ding and Liwu Jiang

Materials 2022, 15(13), 4417; https://doi.org/10.3390/ma15134417 - 22 Jun 2022

Cited by 28 | Viewed by 5889

Abstract

Grain size is one of the most important parameters for metallographic microstructure analysis, which can partly determine the material performance. The measurement of grain size is based on accurate image segmentation methods, which include traditional image processing methods and emerging machine-learning-based methods. Unfortunately, [...] Read more.

Grain size is one of the most important parameters for metallographic microstructure analysis, which can partly determine the material performance. The measurement of grain size is based on accurate image segmentation methods, which include traditional image processing methods and emerging machine-learning-based methods. Unfortunately, traditional image processing methods can hardly segment grains correctly from metallographic images with low contrast and blurry boundaries. Moreover, the proposed machine-learning-based methods need a large dataset to train the model and can hardly deal with the segmentation challenge of complex images with fuzzy boundaries and complex structure. In this paper, an improved U-Net model is proposed to automatically accomplish image segmentation of complex metallographic images with only a small training set. The experiments on metallographic images show the significant advantage of the method, especially for the metallographic images with low contrast, a fuzzy boundary and complex structure. Compared with other deep learning methods, the improved U-Net scored higher in ACC, MIoU, Precision, and F1 indexes, among which ACC was 0.97, MIoU was 0.752, Precision was 0.98, and F1 was 0.96. The grain size was calculated based on the segmentation according to the American Society for Testing Material (ASTM) standards, producing a satisfactory result. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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16 pages, 5587 KiB  

Open AccessArticle

A Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data

by Yongdi Huang, Qionghai Chen, Zhiyu Zhang, Ke Gao, Anwen Hu, Yining Dong, Jun Liu and Lihong Cui

Polymers 2022, 14(9), 1897; https://doi.org/10.3390/polym14091897 - 6 May 2022

Cited by 10 | Viewed by 3666

Abstract

Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecular level can be examined. However, this high-precision method is computationally [...] Read more.

Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecular level can be examined. However, this high-precision method is computationally inefficient and time-consuming, which limits its application. The combination of machine learning and MD is one of the most promising directions to speed up simulations and ensure the accuracy of results. In this work, a surrogate machine learning method trained with MD data is developed to predict not only the tensile stress of NR but also other mechanical behaviors. We propose a novel idea based on feature processing by combining our previous experience in performing predictions of small samples. The proposed ML method consists of (i) an extreme gradient boosting (XGB) model to predict the tensile stress of NR, and (ii) a data augmentation algorithm based on nearest-neighbor interpolation (NNI) and the synthetic minority oversampling technique (SMOTE) to maximize the use of limited training data. Among the data enhancement algorithms that we design, the NNI algorithm finally achieves the effect of approaching the original data sample distribution by interpolating at the neighborhood of the original sample, and the SMOTE algorithm is used to solve the problem of sample imbalance by interpolating at the clustering boundaries of minority samples. The augmented samples are used to establish the XGB prediction model. Finally, the robustness of the proposed models and their predictive ability are guaranteed by high performance values, which indicate that the obtained regression models have good internal and external predictive capacities. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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14 pages, 6568 KiB  

Open AccessArticle

Influence of Fluorinated Polyurethane Binder on the Agglomeration Behaviors of Aluminized Propellants

by Chen Shen, Shi Yan, Yapeng Ou and Qingjie Jiao

Polymers 2022, 14(6), 1124; https://doi.org/10.3390/polym14061124 - 11 Mar 2022

Cited by 16 | Viewed by 3028

Abstract

In this study, fluorinated polyurethane (FPU) was prepared from dialcohol-terminated perfluoropolyether as a soft segment; isophorone diisocyanate (IPDI) as a curing agent; 1,2,4-butanetriol (BT) as a crosslinker; and 1,4-butanediol (BDO) as a chain extender. Fourier transform infrared spectroscopy (FTIR) and ¹H NMR [...] Read more.

In this study, fluorinated polyurethane (FPU) was prepared from dialcohol-terminated perfluoropolyether as a soft segment; isophorone diisocyanate (IPDI) as a curing agent; 1,2,4-butanetriol (BT) as a crosslinker; and 1,4-butanediol (BDO) as a chain extender. Fourier transform infrared spectroscopy (FTIR) and ¹H NMR were used to characterize the structure of the FPU. The mechanical properties of the FPUs with different BDO and BT contents were also measured. The tensile strength and breaking elongation of the optimized FPU formula were 3.7 MPa and 412%, respectively. To find out the action mechanism of FPU on Al, FPU/Al was prepared by adding Al directly to FPU. The thermal decomposition of the FPU and FPU/Al was studied and compared by simultaneous differential scanning calorimetry-thermogravimetry-mass spectrometry (DSC-TG-MS). It was found that FPU can enhance the oxidation of Al by altering the oxide-shell properties. The combustion performance of the FPU propellant, compared with the corresponding hydroxyl-terminated polyether (HTPE)-based polyurethane (HPU) propellant, was recorded by a high-speed video camera. The FPU propellants were found to produce smaller agglomerates due to the generation of AlF₃ in the combustion process. These findings show that FPU may be a useful binder for tuning the agglomeration and reducing two-phase flow losses of aluminized propellants. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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10 pages, 4187 KiB  

Open AccessArticle

Characterizing the Effect of Adding Boron Nitride Nanotubes on the Mechanical Properties of Electrospun Polymer Nanocomposite Microfibers Mesh

by Ohood Alsmairat and Nael Barakat

Materials 2022, 15(5), 1634; https://doi.org/10.3390/ma15051634 - 22 Feb 2022

Cited by 1 | Viewed by 2026

Abstract

Electrospun fibrous meshes have a variety of applications such as filtration, drug delivery, energy storage, and engineered tissues due to their high surface area to mass ratio. Therefore, understanding the mechanical properties of these continuously evolving meshes is critical to expand and improve [...] Read more.

Electrospun fibrous meshes have a variety of applications such as filtration, drug delivery, energy storage, and engineered tissues due to their high surface area to mass ratio. Therefore, understanding the mechanical properties of these continuously evolving meshes is critical to expand and improve their performance. In this study, the effect of adding Boron Nitride Nanotube (BNNT) to Polymethylmethacrylate (PMMA) composite meshes on the mechanical properties of the polymer is studied. Electrospinning is used to fabricate microfiber meshes of PMMA and BNNT-PMMA. The fabricated meshes are tested experimentally with a uniaxial tensile tester. In addition, a theoretical model is introduced to investigate the effect of the number of fibers and the diameter of fiber inside the mesh on Young’s Modulus and Tensile Strength of the PMMA mesh. By adding 0.5% BNNT to the PMMA, Young’s Modulus and Tensile Strength of the PMMA mesh improved by 62.4% and 9.3%, respectively. Furthermore, simulated results show enhanced mesh properties when increasing the number of fibers and the single fiber diameter inside the mesh. The findings of this study help in understanding the mechanical properties of the nanocomposite electrospun meshes which expands and improves its utilization in different applications. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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15 pages, 3629 KiB  

Open AccessArticle

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

by Mehrdad Jalali, Manuel Tsotsalas and Christof Wöll

Nanomaterials 2022, 12(4), 704; https://doi.org/10.3390/nano12040704 - 20 Feb 2022

Cited by 12 | Viewed by 5422

Abstract

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of [...] Read more.

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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12 pages, 2972 KiB  

Open AccessArticle

Inverse Identification of Residual Stress Distribution in Aluminium Alloy Components Based on Deep Learning

by Tulin Xiong, Lu Wang, Xianzhi Gao and Guangyan Liu

Appl. Sci. 2022, 12(3), 1195; https://doi.org/10.3390/app12031195 - 24 Jan 2022

Cited by 6 | Viewed by 3076

Abstract

Residual stress within a structural component can significantly affect the mechanical performance and stability of a structure. Therefore, it is crucial to find a way to determine the residual stress distribution to maintain the normal working of structures. Conventional methods for residual stress [...] Read more.

Residual stress within a structural component can significantly affect the mechanical performance and stability of a structure. Therefore, it is crucial to find a way to determine the residual stress distribution to maintain the normal working of structures. Conventional methods for residual stress determination primarily include experimental testing, finite element simulations and inverse identification. However, these methods suffer from disadvantages of high testing costs, long calculation time and low inverse efficiency. To avoid these shortcomings, this study developed a high-performance method based on a deep learning technique. In this method, an artificial neural network was used to replace the finite element calculation in the finite element model updating (FEMU) technique and the residual stress distribution of structural components was inversely obtained based on the measured residual stresses of a finite number of measuring points. Compared with the conventional FEMU technique, the calculation efficiency of the proposed method was considerably improved. Furthermore, the accuracy and efficiency of the method were verified by simulated four-point bending experiments considering an elastic-plastic material. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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18 pages, 2214 KiB  

Open AccessReview

A Review of Electromagnetic Shielding Fabric, Wave-Absorbing Fabric and Wave-Transparent Fabric

by Jianjun Yin, Wensuo Ma, Zuobin Gao, Xianqing Lei and Chenhui Jia

Polymers 2022, 14(3), 377; https://doi.org/10.3390/polym14030377 - 19 Jan 2022

Cited by 30 | Viewed by 7949

Abstract

As the basic materials with specific properties, fabrics have been widely applied in electromagnetic (EM) wave protection and control due to their characteristics of low density, excellent mechanical properties as well as designability. According to the different mechanisms and application scenarios on EM [...] Read more.

As the basic materials with specific properties, fabrics have been widely applied in electromagnetic (EM) wave protection and control due to their characteristics of low density, excellent mechanical properties as well as designability. According to the different mechanisms and application scenarios on EM waves, fabrics can be divided into three types: EM shielding fabric, wave-absorbing fabric and wave-transparent fabric, which have been summarized and prospected from the aspects of mechanisms and research status, and it is believed that the current research on EM wave fabrics are imperfect in theory. Therefore, in order to meet the needs of different EM properties and application conditions, the structure of fabrics will be diversified, and more and more attentions should be paid to the research on structure of fabrics that meets EM properties, which will be conductive to guiding the development and optimization of fabrics. Furthermore, the application of fabrics in EM waves will change from 2D to 3D, from single structure to multiple structures, from large to small, as well as from heavy to light. Full article

(This article belongs to the Topic Deep Learning Applied on Functional Materials Characterization)

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