Sage Journals: Discover world-class research

Abstract

This study introduces an innovative high-Quality factor (Q-factor) double negative (DNG) metamaterial sensor designed for textile fabric and fabric moisture sensing applications in the dynamic realm of textile innovation. The sensor is specifically designed to detect the dielectric properties and moisture content of different textile fabrics. The high Q-factor of this metamaterial structure ensures heightened sensitivity and accuracy in fabric sensing, facilitating precise detection of even subtle changes in fabric properties. By measuring frequency shifting and analyzing S₂₁ values, the sensor provides crucial information about the fabric’s dielectric characteristics. Sensing experiments conducted on various fabrics, including cotton, denim, corduroy, organza, and polyester unveil distinctive patterns of frequency shifting and Q-factors, establishing a nuanced link between fabric structure and sensor performance. The proposed sensor is capable of detecting fabrics with a very low dielectric constant variation of 0.05. In the experiment, the high-dielectric fabric denim (1.7) exhibited frequency shifting and Q-factor of 6970 and 834.87, respectively. Moreover, it is worth noting that the low-dielectric fabric organza (1.03) exhibits frequency shifting and Q factors of 2190 and 1367.03, respectively. Experimental results affirm the prominent efficacy of the proposed sensor in fabric and fabric moisture sensing. Its high Q-factor empowers the sensor to accurately detect and monitor fabric properties, rendering it highly suitable for critical tasks such as quality control, energy efficiency optimization, and process enhancement within the textile industry. The proposed metamaterial sensor (MMS) can significantly contribute to the development of a smart textile sensing technology and pave the way for innovative applications in the textile industry.

Keywords

double negative metamaterial microwave sensors fabric moisture sensor textile fabric sensor Q-factor

Introduction

High Q-factor DNG metamaterials have attracted significant attention in recent years due to their unique electromagnetic properties and potential applications in various fields likes sensing,¹ imaging,^2,3 communications,⁴ antennas,⁵ energy harvesting,⁶ and smart fabrics.⁷ One such promising area of application is in textile fabrics and fabric moisture sensing. By incorporating the influence factors of dielectric constant, these metamaterials can enable efficient and accurate moisture sensing in fabrics. In recent years, the field of metamaterials has experienced remarkable progress, leading to the exploration of unique opportunities across a wide range of sensing applications, such as fabric,⁸ plastics,⁹ and wood,¹⁰ while still providing high-resolution imaging. Metamaterials are engineered materials with unique electromagnetic properties that allow them to manipulate electromagnetic waves in ways that are not possible with natural materials.¹¹ Metamaterials sensing involves the use of metamaterials to detect and measure changes in the electromagnetic properties of a material. This can be done by measuring the reflection, transmission, or absorption of electromagnetic waves by the metamaterial. Metamaterials in sensing applications have many advantages, including high sensitivity,¹² high resolution, and the ability to operate in a wide range of frequencies.¹³

The first breakthrough in metamaterial sensing was the demonstration of a negative refractive index in a slab of artificially engineered material by¹⁴ in 2001. This discovery sparked a flurry of research activity in the field, developing a wide range of metamaterial structures and devices for electromagnetic applications. In 2004, researchers from Duke University demonstrated the use of metamaterials for sub-wavelength imaging, which opened new possibilities for sensing applications.¹⁵ Since then, researchers have explored various sensing applications of metamaterials, including sensing of physical quantities such as temperature,¹⁶ pressure,¹⁷ and humidity,¹⁸ as well as chemical¹⁹ and biological sensing.²⁰ Over the past decade, research in metamaterials sensing has continued to advance, with new structures and devices being developed for a wide range of applications.²¹ With ongoing research and development, metamaterials sensing is expected to play an increasingly important role in various fields, including healthcare,²² environmental monitoring,²³ and industrial process control.²⁴ Researchers have been exploring using metamaterials in fabric sensing applications in recent years due to their unique properties and potential advantages over traditional sensing materials.²⁴ These properties provide valuable information about the internal responses of dielectric materials, such as chemical composition, bonding strength, flexibility, and structural arrangement. Investigations into the dielectric properties of fibers and yarns help in textile product development, manufacturing, and quality control.²⁵

The dielectric constant of a material plays a crucial role in its moisture-sensing capability.²⁶ By incorporating DNG metamaterials into textile fabrics, it is possible to manipulate the dielectric constant in a controlled manner. This allows for accurate measurement of moisture levels within the fabric, providing valuable information about comfort, breathability, and other properties. The dielectric properties of cotton fabrics have attracted interest for low-frequency wearable sensing applications.²⁷ Zhang et al.²⁸ and Hasegawa et al.²⁹ have demonstrated that capacitance measurements of cotton fabrics can be used to determine applied pressures. Additionally, Zhai et al.³⁰ have suggested the potential for relative humidity (RH) sensing using capacitance measurements of cotton fabrics. As a result, leveraging the dielectric properties of cotton fabrics for sensor applications is considered an emerging topic in the field of smart clothing.

Recent studies have focused on investigating the dielectric properties of cotton fabrics using various methods.³¹ These include the parallel-plate method at low frequencies,^32–34 the microwave resonator method,³⁴ and patch antenna method^35–37 at microwave frequencies. However, limited research exists on understanding the influence of fabric structural parameters, such as fabric construction, thread count, and solid (fiber) volume fraction (SVF), on the dielectric properties of cotton fabrics. Establishing a scientific connection between these structural parameters and dielectric properties is a novel research area that can develop new structural analysis methods and guide the engineering of cotton fabrics for optimal performance in wearable systems. The referenced article³⁷ provides a detailed explanation of the sensor’s design and implementation, along with a mathematical model for sensitivity calculation. Experimental results demonstrate high sensitivity and accuracy in detecting small changes in dielectric constant (10⁻²). However, the article lacks discussion on practical applications of the sensor and utilizes a limited set of materials, potentially limiting its representativeness for broader material testing. In another study,³⁸ the authors employ a metamaterial absorber to measure the thermal properties of clothing fabrics. The absorber is placed on fabric samples, and its absorption rate is measured using a FTIR spectrometer at varying temperatures. The results exhibit accurate thermal conductivity measurement, thermal diffusivity, and specific heat capacity. However, the study is confined to a narrow range of fabrics, and the effectiveness of the method on other fabric types remains uncertain.

Research in the field of high Q-factor DNG metamaterials for textile fabric and fabric moisture sensing applications has been progressing steadily. Several studies have demonstrated the feasibility of using these metamaterials for moisture sensing in fabrics.³⁹ Researchers have explored various designs and fabrication techniques to optimize the performance of these materials for specific applications such as fashion and wearable technology,⁴⁰ industrial and occupational safety,⁴¹ home textiles and bedding,⁴² and many more. Despite the progress made, there are still challenges to address. One such challenge is ensuring the durability and washability of the metamaterials when integrated into fabrics.⁴³ Further research is needed to explore the scalability and cost-effectiveness of large-scale production of these metamaterials for commercial applications.

This article highlights the integration of high Q-factor DNG metamaterials into textile fabrics shows great potential for fabric and fabric moisture sensing applications. Advancements have been made in this field by considering the influence factors of dielectric constant and conducting extensive research. The remarkable Q factor provides enhanced sensitivity while minimizing the shift in resonance frequency. The DNG metamaterial property showcases exceptional electromagnetic characteristics. The distribution of electric field, magnetic field, and surface current is discussed to gain insights into performance evaluation and characterization of resonant behavior.

This work introduces several key innovations, including the following contributions:

a. This article offers a practical assessment of metamaterial sensors applied in the field of textile fabric and fabric moisture sensing applications.

b. The metamaterial sensor offers a non-destructive and non-contact sensing approach, continuously monitoring fabric properties without damaging or altering the fabric.

c. The sensor incorporates a Double Negative (DNG) metamaterial structure, which exhibits unique electromagnetic properties.

Sensor design and simulation

Figure 1 depicts the unit cell structure of the proposed GHz MMS. The MMS has been engineered with overall dimensions of 25 mm × 25 mm, aligning with the SMA (Subminiature version A) connector specifications. This size was specifically chosen to facilitate seamless integration and compatibility with the SMA connector and to fit the sensing layer, which features a mating interface designed for 25 mm × 25 mm dimensions. The use of this standardized size ensures a reliable and efficient connection between the MMS and SMA connector.

Figure 1.

Schematic representation of the designed MMS unit cell with detailed dimensions indicated in millimeter (mm) (a) perspective view (b) sensing region with dimension (c) ground layer (d) sensing setup.

The MMS design incorporates four distinct layers: ground, substrate, resonator, and sample layer (sensor layer). The substrate layer utilizes Flame Retardant-4 (FR-4) material due to its advantageous properties including minimal loss, affordability, and high mechanical strength. The FR-4 substrate is characterized by a relative permittivity of 4.3, a loss tangent of 0.025, and a thickness of 1.6 mm. For the ground and resonator layer, copper metal is employed and printed on the substrate’s top sides. The copper material has a thickness of 0.035 mm and a conductivity of 5 × 10⁻⁸ S/m. This choice of copper as the resonator material enables efficient performance within the MMS design. The resonator layers have been intricately designed in a split car steering shape configuration. This unique shape enhances the functionality and performance of the resonators within the MMS design. Table 1 displays the comprehensive set of detailed dimensions for the MMS unit cell, providing a thorough reference for further examination and analysis.

Table 1.

Detailed dimensions of the designed MMS.

Parameters	Value (mm)
a	25
a1	20
b	6
d1	0.8
d2	0.5
d3	0.5
d4	0.31
d5	0.29
s1	0.4
s2	1.92
s3	2.14
s4	2
s5	0.5
r1	7
r2	6.3
r3	4.5
r4	3.5

In order to provide a comprehensive understanding of the design approach, a concise theoretical explanation is presented to highlight the unique properties exhibited by the unit cell. The unit cell demonstrates the simultaneous manifestation of negative permittivity and permeability by employing an MMS structure. The fundamental principle in characterizing any resonator lies in the identification of alterations in the transmission coefficient, S₂₁. These modifications in S₂₁ serve as indicators for sensing parameters such as variations in dielectric values, permittivity, permeability, or refractive index. To facilitate a thorough analysis, each of these parameters will be mathematically examined individually to extract the effective medium characteristics of the proposed structure. Instead of employing conventional field equations, equation (1) will be utilized to showcase the effective medium parameters of the MMS. This equation focuses explicitly on the demonstration of magnetic and electric fields, further enhancing the uniqueness of the approach.

B_{a v e} = μ_{e f f} μ_{ο} H_{a v e} and

D_{a v e} = ε_{e f f} ε_{ο} E_{a v e}

(1)

To provide a comprehensive understanding, it is essential to explain the relationship between the flux densities associated with the electric field (E) and the magnetic field (H) using Maxwell’s equations in integral form. The symbols used in the equations have their usual meanings. Maxwell’s equations describe the fundamental principles of electromagnetism. In integral form, they can be expressed as follows⁴⁴:

\int_{c} H . d I = 0 + \frac{\partial}{\partial t} \int \int_{c} D . d s and

\int_{c} E . d I = 0 - \frac{\partial}{\partial t} \int \int_{c} B . d s

(2)

In the case of a homogeneous field distribution, the permittivity typically assumes a value of unity. However, equation (2) introduces a unique characteristic where the distribution function for the magnetic field (H) and magnetic flux density (B) differs from the norm. This distinct distribution function sets equation (2) apart from conventional approaches and contributes to the altered behaviour exhibited by the system. A general approach involves assuming a homogeneous field distribution to overcome the challenges associated with spatial dispersion in composite media and polarization-dependent metamaterial structures. By doing so, we can avoid the need to consider the product of the wave vector (k) and the dimension (d) of the patch layer particle.⁴⁵ As a result, the propagated wave in a simplified manner such as:

[\frac{\partial}{\partial x^{2}} + \frac{\partial}{\partial y^{2}} + (k^{2} - β_{z})] E_{z} = 0

(3)

In the context where the z-direction represents the path of wave propagation, the parameter z denotes the phase constant when the electric field (E) propagation is absent, corresponding to a boundary condition. To fulfill this requirement, $\sqrt{k^{2} (β_{x}, β_{y}, 0) + {β_{z}}^{2}}$ which condition contradicts normal mode propagation for any EM wave. The presence of both positive and negative values in the single ordinary EM wave arises when the wave vector (k) in equation (3) takes the form of a second-order polynomial. As a result, extracting dielectric properties for an anisotropic medium becomes possible by utilizing the S-parameters obtained from the unit cell reflector.⁴⁶ Notably, the focus of interest in the proposed unit cell lies in selecting the permittivity (axial component) as

ε (k, β_{z}) = ε_{h} [1 - \frac{{k_{p}}^{2}}{k^{2} - {β_{z}}^{2}}]

(4)

In the given context, “h” represents the permittivity of the substrate or host medium, while “k_p” represents the artificial frequency generated by the patch structure and the medium.

The design and analysis of the sensor were conducted utilizing the electromagnetic high-frequency solver computer simulation technology, specifically CST-2021 Microwave Studio.⁴⁷ The simulation incorporated specific boundary conditions to assess the transmission response (S₂₁) of the sensor. The x-axis and y-axis were assigned the boundary conditions of perfect electric conductor (PEC) and magnetic conductor, respectively. Along the propagation path in the z-axis, free space is assumed. Considering the metallic structure of the side-wall waveguide, boundary conditions including open space, periodic distribution, PEC/PMC, and PEC were deemed suitable.⁴⁸ The selection of PEC boundary conditions in the simulation study was based on the similarity to the experimental setup conditions. The simulation was conducted within the frequency range of 4–5 GHz, enabling comprehensive analysis and evaluation of the sensor’s performance.

Mathematical modelling and result investigation

Design evaluation is essential for metamaterial sensors to validate their performance, optimize design, assess reliability, ensure compatibility, and comply with safety and regulatory standards. The design optimization process involves a sequential step-by-step approach to gradually develop the structure presented in Figure 2(a). The evolution of the design has been assessed by analyzing the S₂₁ in Figure 2(b). In design 1, 2, and 3, the split car steering-based resonator is introduced and achieved less significant scattering parameters. However, in the final design with a split in the transmission line, a high Q factor is due to an increase in the capacitance of the resonators, as shown by the equations⁴⁹ Q = ω * C * R, where, Q denotes quality factor, ω is the angular frequency of the resonator, C is the capacitance of the resonator, and R is the resistance of the resonator. The capacitance is a key factor in this equation that determines the resonator’s Q factor. A higher capacitance value generally leads to a higher Q factor, indicating a narrower bandwidth of the metamaterial structure.

Figure 2.

(a) Design evaluation stages for proposed metamaterial sensor (b) The transmission coefficient (S₂₁) of the proposed sensor design evaluation.

The substrate material plays a significant role in determining the sensing performance of the proposed structure. Figure 3 depicts the resonance spectra of the sensor with varying substrate materials as the refractive index (n) of the analyte changes. Substituting the FR-4 substrate with alternative materials reduces the resonant frequency tuning range, indicating lower sensitivity to refractive index variations. Additionally, the use of other substrates diminishes the sensor’s performance due to the significant limitations in resolution caused by the low Q factor value of the resonances.

Figure 3.

Resonance response of the designed MMS with different substrate materials.

Metamaterial properties of a metamaterial sensor are crucial because they directly influence the sensor’s performance and functionality. Metamaterials are artificially engineered materials with unique electromagnetic properties not found in natural materials.⁵⁰ By comprehending the metamaterial properties, the sensor’s sensitivity and selectivity can be optimized. This understanding also enables the development of new sensing modalities, pushing the boundaries of sensing technologies and fostering innovation. The metamaterial properties of a metamaterial sensor enhance its capabilities and drive advancements in the field of sensing. The metamaterial properties of designed MMS are illustrated in Figure 4. The designed metamaterial sensor (MMS) attained the DNG (Double-Negative) property, which was effective for metamaterial sensing due to its negative permittivity (ε) and permeability (μ). It offered a tailorable electromagnetic response, subwavelength resolution, enhanced field-material interaction, and compatibility with miniaturization. These properties enabled DNG metamaterial sensors to provide enhanced sensitivity and control over electromagnetic waves, making them well-suited for various sensing applications.

Figure 4.

The metamaterial properties of the designed MMS (a) relative permittivity (ε) real (Re) and imaginary (Im) part, (b) relative permeability (μ) real (Re) and imaginary (Im) part, and (c) refractive Index real (Re) and imaginary (Im) part.

Referring to the provided Figure 4(a), it can be observed that within the frequency range of 4.26 to 4.44 GHz, the permittivity (ε) of the material is negative, indicating an electric response opposite to conventional materials in this frequency range. Similarly in Figure 4(b), the permeability of the DNG metamaterial is negative from 4 to 4.8 GHz, implying an unconventional magnetic response. Moreover, in Figure 4(c), the refractive index of the material is negative from 4.27 to 4.43 GHz. This negative refractive index suggests that the DNG metamaterial can manipulate the bending and refraction of electromagnetic waves in the opposite direction compared to typical materials, resulting in unique propagation characteristics. The combination of negative permittivity, negative permeability, and negative refractive index within their respective frequency ranges allows the DNG metamaterial to refer the unique property of metamaterials where both the permittivity (ε) and permeability (μ) are negative. This property allows the MMS to exhibit extraordinary electromagnetic behavior, such as negative refraction and backward wave propagation. By exploiting this property, the MMS can capture and amplify the incoming signals more effectively, making it highly sensitive to even weak signals. This enhanced sensitivity allows the sensor to detect and measure small variations in the targeted quantities with greater precision.

The analysis and comprehension of the electric field, magnetic field, and surface current distribution play a crucial role in characterizing and optimizing DNG (Double-Negative) metamaterial-based sensors. Researchers can gain valuable insights into the electromagnetic behavior and performance by investigating these fields and distributions within the sensor.⁵¹ The evaluation of the electric field provides information about the forces acting on electric charges, allowing for the assessment of charge interactions and wave propagation within the sensor. On the other hand, the analysis of the magnetic field reveals the behavior of magnetic forces and their impact on the functionality of the sensor. Additionally, the examination of the surface current distribution offers insights into the flow of electric current along the surface of the metamaterial structure, providing valuable information about current paths and their influence on the sensor’s electromagnetic response. The analysis of these fields and distributions enables engineers to optimize the design of the DNG metamaterial sensor by identifying areas of high field concentration, undesired current paths, or other inefficiencies.

The working mechanism of the proposed sensor depended on resonant frequency-based sensors. The achieved Q-factor is a measure of the sharpness of the resonance peak. A high Q-factor indicates a low rate of energy loss and a very narrow resonance bandwidth and ensures that even small changes in the fabric’s properties lead to noticeable shifts in resonant frequency and S₂₁ values, enabling precise detection and monitoring. When the DNG metamaterial is exposed to a fabric, changes in the fabric’s properties will cause a shift in the sensor’s resonant frequency. For instance, the resonant frequency of the DNG metamaterial will decrease or increase in proportion to changes in the fabric and fabric moisture content. The frequency shift and the changes in S₂₁ values in response to variations in fabric properties are analyzed to provide critical information about the fabric’s characteristics.

In the provided Figure 5, the distribution of (a) electric field, (b) magnetic field, and (c) surface current is depicted. The electric field (E) can be described by Maxwell’s equations, which establish the relationship between electric fields and their sources.¹³

\nabla \times E = - \partial B / \partial t

(5)

where ∇ represents the gradient operator.

Figure 5.

Resonance frequency-dependent distribution of (a) electric field, (b) magnetic field, and (c) surface current distribution.

The magnetic field (B) is related to the electric field through Ampere’s law, which describes the magnetic field generated by electric currents.¹⁰

\nabla \times B = μ_{0} J + μ_{0} ε_{0} \partial E / \partial t

(6)

Here, J represents the current density and μ₀ and ε₀ are the permeability and permittivity of free space, respectively.

The surface current (I) represents the flow of electric current along the surface of the metamaterial structure, and it can be determined using Ampere’s law.⁵²

\oint B \cdot d l = μ_{0} I

(7)

In this context, “dl” represents an infinitesimal vector element along a closed loop.

In the context of textile fabric and fabric moisture sensing applications, the proposed metamaterials can be designed to respond to changes in the electric field, magnetic field, and surface current distribution. When an external electric or magnetic field interacts with the metamaterial, it induces an electric or magnetic response in the meta-atoms, causing them to resonate. This resonance can be detected and used to identify the type of fabric or its moisture content.

The electric field distribution response in the resonators, causing them to resonate. This resonance can be detected and used to identify the type of fabric and its moisture content. The magnetic field distribution reveals the behavior and strength of magnetic forces within the metamaterial. The magnetic field distribution in the resonators of the metamaterial sensor provides a sensitive and effective means to detect changes in the fabric and its moisture content. The surface current distribution illustrates the flow of electric current along the surface, indicating the current paths and their influence on the sensor’s electromagnetic response. The geometric arrangement of the resonators affects the flow and distribution of surface currents as the sensor interacts with the fabric. Variations in the moisture level and structure of the fabric directly affect the surface current patterns, creating unique patterns. Through analyzing these patterns, the sensor can identify and describe various types of fabrics and moisture content fluctuations.

Sensing performance analysis

Fabric sensing

Metamaterial-based sensors provide distinct benefits for fabric sensing applications, offering a range of advantages. These sensors utilize electromagnetic wave interactions to identify various fabric types and can effectively measure fabric moisture by capitalizing on alterations in dielectric properties. Moreover, they can be specifically engineered for combined sensing, enabling the detection of fabric type and moisture content using a single sensor. There are different types of sensors used in fabric sensing, such as Capacitive Moisture Sensors,⁵³ Resistive Moisture Sensors,⁵⁴ Optical Moisture Sensors,⁵⁵ Near-Infrared (NIR) Spectroscopy,⁵⁶ and Conductive Yarns and Threads.⁵⁷ The sensors mentioned have certain limitations. For instance, capacitive moisture sensors are susceptible to environmental conditions such as temperature fluctuations, interference from other electronics, material degradation, etc., and have a limited detection depth.

Resistive moisture sensors can deteriorate over time and are sensitive to temperature fluctuations. Optical moisture sensors face challenges when used with opaque fabrics and can be disrupted by external light sources. NIR spectroscopy sensors require sophisticated data interpretation and are often associated with higher costs. Additionally, the integration of conductive yarns and threads in fabrics can lead to wear and may alter the fabric’s characteristics.

Metamaterials are preferred for fabric and fabric moisture sensing to overcome those limitations due to their distinct advantages over traditional materials and methods. One notable benefit is their enhanced sensitivity, enabling accurate and reliable fabric types and moisture content detection. Metamaterials can be precisely engineered to interact with microwave radiation, optimizing their performance for fabric sensing applications. Moreover, the properties of metamaterials can be fine-tuned to accommodate various fabric types and sensing requirements, offering flexibility in their application. The designed sensors can simultaneously detect fabric type and moisture, providing comprehensive and integrated sensing capabilities.

Figure 6 illustrates the measurement procedure of the proposed MMS. Figure 6(b) illustrates the configuration where two SMA connectors are affixed to the coaxial cable, which is then connected through a TNC female connector to the N5227A PNA microwave network analyzer. The measurements are conducted utilizing the PNA-L series vector network analyzer (VNA) within the frequency range of 10 MHz to 67 GHz. To begin the experimental setup, the metamaterial Sensor (MMS) is affixed to the SMA (Sub Miniature version A) connector by soldering, and a coaxial cable is connected to the SMA port using an additional connector. Subsequently, the fabric sample is carefully positioned on the designated sensor. This specific configuration was employed to assess the transmission coefficient (S₂₁) within the frequency range of 4–6 GHz. It’s worth noting that an identical experimental procedure was conducted for every fabric sample. The transmission coefficient (S₂₁) characteristics of the proposed metamaterial Sensor (MMS) were measured and are shown in Figure 7.

Figure 6.

(a) Different fabric sample. (b) The sensing measurement setup.

Figure 7.

The measured S₂₁ parameters (zoom view) of the metamaterial sensor for different fabrics.

The formula determines the resonance frequency $f_{0} = \frac{1}{2 π \sqrt{L C}}$ , where L represents the inductance and C represents the capacitance of the resonant system. In the context of a metamaterial sensor, changes in the dielectric constant (ε) of the medium surrounding the sensor can alter the capacitance, consequently affecting the resonance frequency. An increase in ε tends to decrease the capacitance and shift the resonance frequency upward, while a decrease in ε has the opposite effect.⁵⁸ In Figure 7, some of the transmission coefficient (S₂₁) exhibits a shift towards lower frequencies. This shift is attributed to the influence of the thickness and weight of the fabric being sensed by the MMS. When a thicker and heavier fabric is applied to the sensor, the transmission coefficient undergoes this lower-frequency shift. The reason behind this shift is the fabric’s loading effect on the metamaterial elements within the sensor. The increased mass and density of the thicker and heavier fabric result in a stronger loading effect, subsequently modifying the resonant behavior of the metamaterial elements. Consequently, the frequencies at which maximum reflection occurs experience a shift towards lower values. Hence, the presence of a thicker and heavier fabric leads to a lower-frequency shift in the transmission coefficient (S₂₁) of the designed MMS.

Conversely, when a thin and lightweight fabric is utilized, the transmission coefficient (S₂₁) shifts towards higher frequencies presented in Figure 7. This shift can be attributed to the lighter weight and lower density of the fabric, which results in a smaller loading effect on the metamaterial sensor. Consequently, the resonant behavior of the sensor’s elements remains relatively unaffected, causing a shift in the frequencies at which maximum reflection occurs to higher values. Therefore, the presence of a thin and lightweight fabric leads to a higher-frequency shift in the transmission coefficient (S₂₁) of the proposed MMS. The performance of the metamaterial in sensing the fabric and fabric moisture of textiles depends on the Q factor, which is a key parameter. The Q factor is obtained by examining the resonance properties in the frequency response spectrum of the metamaterial. The Q factor is computed using this formula⁵⁹:

Q = \frac{f_{0}}{Δ f}

(8)

Where, Q denotes the Quality Factor, f₀ is the centre frequency of the resonance peak, and

Δ f

is the bandwidth at half-maximum amplitude.

The experimental sensing results for different fabric samples are summarized in Table 2.

Table 2.

Sensing performance evaluation of the proposed MMS with different fabric sample.

Fabric name	Measured dielectric constant (ε)	Resonance frequency shift (KHz)	Q factor
Denim	1.7	6970	834.87
Polyester	1.55	2440	679.94
Corduroy	1.43	1040	752.97
Cotton	1.08	2570	3680.77
Organza	1.03	2190	1367.03

Table 2 provides a comprehensive analysis of the sensing performance of the proposed MMS with respect to different types of fabrics. With its high shift value of 6970 kHz, Denim exemplifies the effect of a dense and thick weave, while Corduroy’s minimal shift of 1040 kHz could reflect its delicate, lightweight characteristics. This suggests the sensor’s potential sensitivity to attributes such as mass, weave tightness, and fibre thickness. Furthermore, the observed variability in the Q factor, a measure of the resonance peak’s sharpness, underscores the sensor’s selectivity. Cotton’s high Q factor of 3680.77 suggests a resonance favourable structure. In contrast, polyester shows the lowest Q factor at 679.94, potentially due to its smoother fibres and less distinct weave. In conclusion, the resonance frequency shift and Q factor data collectively suggest a discernible link between the physical structure of fabrics and their interaction with the MMS, with denser fabrics like Denim significantly impacting the resonance frequency and variations in Q factor highlighting the sensor’s nuanced response to different fabric types.

Changes in the dielectric constant(ε) influence the loss mechanisms within the system and, consequently, the Q-factor. An increase in (ε) enhance energy storage, leading to a higher Q-factor,⁶⁰ while a decrease in ε result in increased losses and a lower Q-factor which is presented in Figure 8.

Figure 8.

Resonance frequency and Q-factor shifting according to changes of dielectric constant (ε).

Fabric moisture sensing

Fabric moisture sensing is an integral part of quality control in the textile industry.⁶¹ The moisture content in fabrics significantly affects their quality, durability, and lifespan.⁶² By incorporating moisture sensing techniques during manufacturing, manufacturers can ensure consistent quality and prevent issues such as mold growth, fabric degradation, and loss of functionality.⁶³ In addition to quality control, fabric moisture sensing is vital in energy efficiency. Efficient moisture management in textiles, whether in home textiles or outdoor gear, is crucial for optimizing energy usage.⁶⁴ By accurately sensing and controlling moisture levels, processes like drying, heating, or cooling can be optimized, leading to reduced energy consumption and a positive environmental impact.

Moreover, fabric moisture sensing is highly valuable for process optimization in various industrial applications.⁶⁵ Manufacturers can monitor and control moisture levels throughout the production process, allowing them to make necessary adjustments in parameters such as drying time or temperature. This optimization enhances overall efficiency, and ultimately improves the quality of the end products. By leveraging fabric moisture sensing, the textile industry can ensure consistent quality, achieve energy efficiency, and optimize production processes. This technology empowers manufacturers to make informed decisions based on real-time moisture data, leading to improved product performance and customer satisfaction.

Fabric moisture sensing using metamaterial sensors offers precise and sensitive monitoring of moisture levels in textiles. These sensors possess exceptional sensitivity to detect even minute changes in the fabric’s dielectric properties resulting from moisture absorption. The Figure 9 illustrates the sensing characteristics of denim, corduroy, and cotton fabrics at various moisture levels. Figure 9(a) shows the moisture percentages of 0%, 15%, and 30% for denim fabric. In the case of denim, the resonant frequency shift might not vary monotonically with humidity due to the complex interaction between the denim fibres and the water molecules.⁶⁶ Denim, being a natural fibre, has a complex structure with spaces where water molecules can be absorbed.⁶⁷ Figure 9(b) illustrates the moisture percentages of 0%, 27%, and 54% for corduroy fabric, while Figure 9(c) presents the moisture levels of 0%, 10%, and 20% for cotton fabric. All three fabrics mentioned above exhibit a significant shift towards lower frequencies in the transmission coefficient (S₂₁). Additionally, they demonstrate a high Q factor, which is essential for effective sensing purposes.

Figure 9.

Experimental sensing performance of different moisture levels in various fabrics. (a) Denim fabric, (b) corduroy fabric, and (c) cotton fabric.

When moisture is introduced to a fabric sample for sensing, it causes a shift in the transmission coefficient (S₂₁) towards lower frequencies. This shift can be attributed to the unique dielectric properties of moisture. Moisture has a higher dielectric constant compared to air or most fabrics. Consequently, when water is added to the fabric sample, it significantly increases the overall dielectric constant of the composite material. This alteration in the dielectric constant directly impacts the resonant behavior of the designed metamaterial sensor, resulting in a noticeable shift in the frequency response.

In Figure 10, the graph illustrates the correlation between the moisture content of a fabric and the corresponding frequency shift. The relationship between moisture content and frequency shift is generally linear because the change in stiffness and density of the fabric with increasing moisture content is often proportional. As more moisture is absorbed, the fabric becomes less stiff and denser, resulting in a consistent linear change in the resonant frequency.

Figure 10.

Linear relationship between fabric moisture content and frequency shifting.

The overall sensing performance of the designed Metamaterial Sensor for different moisture levels in different fabrics is presented in Table 3.

Table 3.

Sensing performance of the designed MMS for different moisture levels in various fabrics.

Fabric name	Moisture(%)	Measured dielectric constant (ε)	Resonance frequency shift (MHz)	Q factor
Cotton	0	1.08	2.57	3680.77
	10	2.65	39.73	1355.71
	20	4.48	317.9	646.36
Denim	0	1.7	6.97	918.35
	15	2.41	77.44	500.53
	30	6.30	57.44	241.07
Corduroy	0	1.43	1.04	735.6
	27	2.84	120.84	1035.9
	54	4.89	361.44	566.79

Conclusion

This work presents the design and fabrication of a low-profile, High Q factor metamaterial sensor for fabric and fabric moisture sensing applications. The proposed sensor design based on a dielectric sensing mechanism can detect fabric properties and moisture content with respect to resonance frequencies shifting phenomena. Numerical analyses are also performed on a fabric sample with dielectric constants ranging from 1.03 to 1.7, such as cotton, organza, denim, corduroy, and polyester. The design achieves a high Q-factor of 3680.77, 1367.03, 834.87, 752.97, and 679.94 with corresponding resonance frequency shifts of 2570 KHz, 2190 KHz, 6970 KHz, 1040 KHz, and 2440 KHz for cotton, organza, denim, corduroy, and polyester, respectively. This investigation achieves significant frequency shifting for a little dielectric variation of 0.05. The proposed sensor also demonstrates exceptional performance in terms of frequency shifting, sensitivity, and Q factor for different fabric moisture. This data can be used in the textile industry to improve manufacturing procedures, optimise energy efficiency, and maintain quality control. In addition, the suggested MMS can be used for IoT connectivity, robustness, smooth integration, and sophisticated monitoring to adapt the evolving demands of the textile sector.

Supplemental Material

Supplemental Material - High qualityfactor double negative metamaterial for textile fabric and fabric moisture sensing applications

Supplemental Material for High quality factor double negative metamaterial for textile fabric and fabric moisture sensing applications by Md. Bakey Billa, Mohammad Tariqul Islam, Touhidul Alam, Saleh Albadran, Ahmed Alzamil, Ahmed S. Alshammari, Haitham Alsaif, Md Shabiul Islam and Mohamed S. Soliman in Journal of Industrial Textiles.

Footnotes

Acknowledgements

The authors are grateful to Universiti Kebangsaan Malaysia.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been funded by Scientific Research Deanship at University of Ha’il - Saudi Arabia through project number RG-23163.

ORCID iD

Md. Bakey Billa

Supplemental Material

Supplemental material for this article is available online.

References

Hakim

Alam

Almutairi

, et al. Polarization insensitivity characterization of dual-band perfect metamaterial absorber for K band sensing applications. Sci Rep 2021; 11(1): 17829.

Hasan

Samsuzzaman

Siam

, et al. Omnidirectional wideband slotted antenna for human head imaging. In: 2020 2nd international conference on sustainable technologies for industry 4.0 (STI), Dhaka, Bangladesh, 19–20 December 2020. IEEE.

Ullah

Alam

Islam

. Performance analysis of a 3D unidirectional antenna opted for biomedical microwave imaging. Microw Opt Technol Lett 2018; 60(11): 2849–2853.

Alam

Almutairi

Samsuzzaman

, et al. Metamaterial array based meander line planar antenna for cube satellite communication. Sci Rep 2021; 11(1): 14087.

Alam

Sahar

Imtiaz

, et al. Design and analysis of a patch antenna for nanosatellite transmission system. In: 2021 7th international conference on space science and communication (IconSpace), Selangor, Malaysia, 23–24 November 2021. IEEE.

Billa

Hakim

Alam

, et al. Near-ideal absorption high oblique incident angle stable metamaterial structure for visible to infrared optical spectrum applications. Opt Quant Electron 2023; 55(12): 1115.

Sedighi

Lallart

Zhuang

. Metamaterials: design, modelling, simulation and implementation. London, UK: Sage Publications Sage UK, 2022, pp. 2153–2156.

Yamada

. Dielectric properties of textile materials: analytical approximations and experimental measurements—a review. Textiles 2022; 2(1): 50–80.

Yang

Gao

, et al. High performance D-type plastic fiber SPR sensor based on a hyperbolic metamaterial composed of Ag/MgF 2. J Mater Chem C 2021; 9(39): 13647–13658.

10.

Islam

, et al. Metamaterial sensor based on rectangular enclosed adjacent triple circle split ring resonator with good quality factor for microwave sensing application. Sci Rep 2022; 12(1): 6792.

11.

Hakim

Alam

Soliman

, et al. Polarization insensitive symmetrical structured double negative (DNG) metamaterial absorber for Ku-band sensing applications. Sci Rep 2022; 12(1): 1–18.

12.

Hakim

Alam

Islam

, et al. Polarization-independent fractal square splits ring resonator (FSSRR) multiband metamaterial absorber/artificial magnetic conductor/sensor for Ku/K/Ka/5G (mm-Wave) band applications. Measurement 2023; 210: 112545.

13.

Hoque

Almutairi

Islam

. Triple-band modified V-shape with AU-shape ring resonator metamaterial absorber for C-and X-band sensing applications. In: 2023 3rd international conference on innovative research in applied science, engineering and technology (IRASET), Mohammedia, Morocco, 18–19 May 2023. IEEE.

14.

Shelby

Smith

Schultz

. Experimental verification of a negative index of refraction. Science 2001; 292(5514): 77–79.

15.

Halir

Cheben

Luque-González

, et al. Ultra-broadband nanophotonic beamsplitter using an anisotropic sub-wavelength metamaterial. Laser Photon Rev 2016; 10(6): 1039–1046.

16.

Zou

Cheng

. Design of a six-band terahertz metamaterial absorber for temperature sensing application. Opt Mater 2019; 88: 674–679.

17.

Lai

W-H

, et al. Tunable MEMS-based terahertz metamaterial for pressure sensing application. Micromachines 2023; 14(1): 169.

18.

Ekmekci

Kose

Cinar

, et al. The use of metamaterial type double-sided resonator structures in humidity and concentration sensing applications. Sensor Actuator Phys 2019; 297: 111559.

19.

Zheng

Wen

, et al. Metamaterial grating for colorimetric chemical sensing applications. Mat Today Phys 2023; 33: 101056.

20.

Singh

Gupta

Kaler

, et al. Designing and analysis of ultrathin metamaterial absorber for W band biomedical sensing application. IEEE Sensor J 2022; 22(11): 10524–10531.

21.

Fang

Zhou

Yurchenko

, et al. Multistability phenomenon in signal processing, energy harvesting, composite structures, and metamaterials: a review. Mech Syst Signal Proc 2022; 166: 108419.

22.

Jiang

Liu

, et al. Flexible metamaterial electronics. Adv Mater 2022; 34: 2200070.

23.

Monfared

Qasymeh

. Graphene-assisted infrared plasmonic metamaterial absorber for gas detection. Results Phys 2021; 23: 103986.

24.

Jiao

Alavi

. Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends. Int Mater Rev 2021; 66(6): 365–393.

25.

Mukai

. Dielectric properties of cotton fabrics and their applications. Raleigh, NC: North Carolina State University, 2019.

26.

Lee

C-Y

Lee

G-B

. Humidity sensors: a review. Sens Lett 2005; 3(1–2): 1–15.

27.

Esfandiari

Nazokdast

Rashidi

, et al. Review of polymer-organoclay nanocomposites. J Appl Sci 2008; 8(3): 545–561.

28.

Zhang

, et al. Study of the structural design and capacitance characteristics of fabric sensor. Adv Mater Res 2011; 194–196: 1489–1495.

29.

Hasegawa

Shikida

Ogura

, et al. Novel type of fabric tactile sensor made from artificial hollow fiber. In: 2007 IEEE 20th international conference on micro electro mechanical systems (MEMS), Hyogo, Japan, 21–25 January 2007.

30.

Zhai

Zhang

Hao

, et al. Metal–organic frameworks materials for capacitive gas sensors. Adv Mater Technol 2021; 6(10): 2100127.

31.

Esfandiari

. PPy covered cellulosic and protein fibres using novel covering methods to improve the electrical property. World Appl Sci J 2008; 3(3): 470–475.

32.

Mustata

FSC

Mustata

. Dielectric behaviour of some woven fabrics on the basis of natural cellulosic fibers. Adv Mater Sci Eng 2014; 2014: 1–8.

33.

Cerovic

Dojcilovic

Asanovic

, et al. Dielectric investigation of some woven fabrics. J Appl Phys 2009; 106(8): 084101.

34.

Cerovic

Asanovic

Maletic

, et al. Comparative study of the electrical and structural properties of woven fabrics. Compos B Eng 2013; 49: 65–70.

35.

Hertleer

Rogier

Van Langenhove

. The effect of moisture on the performance of textile antennas. In: 2009 2nd IET seminar on antennas and propagation for body-centric wireless communications, London, UK, 20 April 2009.

36.

Hertleer

Van Laere

Rogier

, et al. Influence of relative humidity on textile antenna performance. Textil Res J 2010; 80(2): 177–183.

37.

Sankaralingam

Gupta

. Determination of dielectric constant of fabric materials and their use as substrates for design and development of antennas for wearable applications. IEEE Trans Instrum Meas 2010; 59(12): 3122–3130.

38.

Huang

Zhong

. Verification of a large-band metamaterial absorber and its application in the measurement of thermal properties of clothing fabrics. Mater Res Express 2022; 9(11): 115801.

39.

Wang

Niu

Zhou

, et al. Multifunctional directional water transport fabrics with moisture sensing capability. ACS Appl Mater Interfaces 2019; 11(25): 22878–22884.

40.

Gil

Seager

Fernández-García

. Embroidered metamaterial antenna for optimized performance on wearable applications. Phys Status Solidi (A) 2018; 215(21): 1800377.

41.

Kumar

Chohan

, et al. Overview on metamaterial: history, types and applications. Mater Today Proc 2022; 56: 3016–3024.

42.

Saber

Abd El-Aziz

. Advanced materials used in wearable health care devices and medical textiles in the battle against coronavirus (COVID-19): a review. J Ind Textil 2022; 51(1_suppl): 246S–271S.

43.

Wang

Yan

Peng

, et al. Study on terahertz spectrum variation of inner package with different wrapped objects. J Ind Textil 2023; 53: 152808372311785.

44.

Hoque

Islam

Almutairi

, et al. DNG metamaterial reflector using SOCT shaped resonator for microwave applications. IEEE Acce 2021; 9: 59148–59159.

45.

Capolino

. Theory and phenomena of metamaterials. Boca Raton, FL: CRC Press, 2017.

46.

Hannan

Islam

Sahar

, et al. Modified-segmented split-ring based polarization and angle-insensitive multi-band metamaterial absorber for X, Ku and K band applications. IEEE Acces 2020; 8: 144051–144063.

47.

CST Studio suite. https://www.3ds.com/ko/products-services/simulia/products/cst-studio-suite/solvers/. (Accessed 28 February 2023). CST, 2023.

48.

Nisanci

de Paulis

. Efficient analytical prediction of the cavity resonant behavior of PEC–PMC metallic enclosures and packages. IEEE Trans Electromagn C 2021; 63(1): 93–102.

49.

Engheta

Ziolkowski

. Metamaterials: physics and engineering explorations. Hoboken, NJ: John Wiley & Sons, 2006.

50.

Liu

Zhang

. Metamaterials: a new frontier of science and technology. Chem Soc Rev 2011; 40(5): 2494–2507.

51.

Jairath

Singh

Shabaz

, et al. Performance analysis of metamaterial-inspired structure loaded antennas for narrow range wireless communication. Sci Prog 2022; 2022: 7940319.

52.

Alam

Faruque

MRI

Islam

. A double-negative metamaterial-inspired mobile wireless antenna for electromagnetic absorption reduction. Materials 2015; 8(8): 4817–4828.

53.

Eller

Denoth

. A capacitive soil moisture sensor. J Hydrol 1996; 185(1–4): 137–146.

54.

Pahuja

. Development of semi-automatic recalibration system and curve-fit models for smart soil moisture sensor. Measurement 2022; 203: 111907.

55.

Chaudhary

Verma

Mishra

, et al. Preparation of carbon quantum dots using bike pollutant soot: evaluation of structural, optical and moisture sensing properties. Phys E Low-dimens Syst Nanostruct 2022; 139: 115174.

56.

da Silva Medeiros

Cruz-Tirado

Lima

, et al. Assessment oil composition and species discrimination of Brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and chemometrics. J Food Compos Anal 2022; 107: 104403.

57.

Stavrakis

Simić

Stojanović

. Electrical characterization of conductive threads for textile electronics. Electronics 2021; 10(8): 967.

58.

Rahmani Charvadeh

Ghalibafan

. Investigation of textile permittivity under the influence of fever for designing a wearable temperature sensing antenna. Biosens Bioelectron X 2022; 12: 100264.

59.

Zhang

Liu

, et al. A dual-band terahertz metamaterial sensor with high Q-factor and sensitivity. Opt Quant Electron 2023; 55(13): 1126.

60.

Juan

Bronchalo

Potelon

, et al. Concentration measurement of microliter-volume water–glucose solutions using $ Q $ factor of microwave sensors. IEEE Trans Instrum Meas 2019; 68(7): 2621–2634.

61.

Gonçalves

Ferreira da Silva

Gomes

, et al. Wearable e-textile technologies: a review on sensors, actuators and control elements. Inventions 2018; 3(1): 14.

62.

Lee

Choy

, et al. A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry. Expert Syst Appl 2013; 40(7): 2435–2446.

63.

Islam

GMN

Ali

Collie

. Textile sensors for wearable applications: a comprehensive review. Cellulose 2020; 27: 6103–6131.

64.

Chen

Khan

, et al. Textile-based batteryless moisture sensor. IEEE Antennas Wirel Propag Lett 2020; 19(1): 198–202.

65.

Ullah

Wagih

Beeby

. Design of textile antenna for moisture sensing. Engineering Proceedings 2022; 15(1): 11.

66.

Zhang

Yan

. Dielectric constants of sewed multilayer fabric for wearable e-textiles. J Ind Textil 2022; 51(2_suppl): 2354S–2371S.

67.

Atanasova

Atanasov

. Impact of electromagnetic properties of textile materials on performance of a low-profile wearable antenna backed by a reflector. In: 2020 international workshop on antenna technology (iWAT), Bucharest, Romania, 25–28 February 2020.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.07 MB