Multi-component gas detection device and method based on metasurface quasi-continuous domain bound state

Abstract

The invention discloses a multi-component gas detection device and method based on a metasurface quasi-continuous domain bound state. The advanced nanometer manufacturing technology and the deep learning algorithm are adopted, and high-precision, high-sensitivity and high-selectivity gas detection is achieved. According to the invention, the metasurface array with specific design is utilized, and the quasi-continuous domain bound state in the middle-infrared band is realized by accurately regulating and controlling the geometric parameters and material characteristics of the microstructure, so that excellent optical response is obtained. In combination with a deep learning algorithm, complex spectral information is converted into accurate gas composition and concentration information, and a reliable solution is provided for the fields of environmental monitoring, industrial production, safety monitoring and the like.

Description

Technical Field

[0001] The present invention relates to the technical fields of photonics and gas detection, and more particularly, to a multi-component gas detection method based on bound states in the continuum (BIC) of metasurfaces and its applications. Background Art

[0002] In recent years, gas detection has become particularly crucial in fields such as environmental monitoring, medical diagnosis, and industrial production. Meeting the modern social demand for rapid, accurate, and low-cost gas detection has promoted the continuous development and innovation of new detection technologies. In particular, the development of intelligent sensors enables us to simulate and optimize the functions of various sensory organs of organisms, including vision, hearing, touch, and smell. Although the performance of sensors designed based on physical characteristics in terms of vision, hearing, and touch has reached or even exceeded that of humans, artificial olfaction, such as electronic noses, faces complex chemical reactions and slow progress when mimicking biological olfactory cells. This has led to two major challenges in the field of multi-component gas detection: (1) insufficient selectivity of sensors, manifested as cross-sensitivity to multiple gases; (2) the scale of artificial olfactory sensor arrays is much smaller than that of biological olfactory organs.

[0003] The development of metasurfaces provides a potential method for solving these problems. By precisely controlling sub-wavelength scale structural units and arranging metasurfaces orderly, array-based optical metasurfaces can achieve highly sensitive perception of multi-component gases. This method overcomes the problem of uncontrollable chemical reaction processes compared with traditional electronic noses, and its spectrum is specific to gases and is convenient for large-scale array integration.

[0004] Optical bound states in the continuum (BIC) can perfectly localize the optical field. When the BIC state is perturbed, it can be transformed into a quasi-BIC state with an ultra-high Q value, greatly promoting the interaction between light and matter, and thus is suitable for highly sensitive sensors. In particular, metasurfaces based on quasi-BIC exhibit specific optical responses to different gas molecules, opening up new possibilities for realizing highly sensitive, rapid, and multi-component gas detection. However, the specific applications and implementation methods of this new technology are still in the preliminary stage. Key issues include how to fabricate efficient metasurfaces, how to select suitable materials, and how to optimize the spectral decoupling algorithm to improve detection performance.

[0005] At present, although there are already some related patents and researches, different multi-component gas detection methods and implementation methods of metasurface quasi-BIC have been proposed. For example, in 2015, Patent CN105445216A disclosed "a multi-gas concentration measurement sensor based on metasurface infrared absorption". This method utilizes the spectral splitting characteristics of the anomalous reflection metasurface, can realize the simultaneous measurement of multiple gases, and has the characteristics of simple structure and small volume. In 2022, Patent CN115015158A disclosed "a hypersensitive terahertz biosensor based on bound states in the quasi-continuum". This method induces bound states in the quasi-continuum in the frequency domain, thus realizing hypersensitive detection of trace bio-sample molecules in the terahertz frequency band. In 2022, Patent CN115901670A disclosed "a tunable terahertz sensor dominated by multiple bound states in the continuum". This method obtains multiple quasi-BICs by adjusting the distance between the two slits of the slotted U-shaped resonant arm and the distance of the two slotted U-shaped resonant arms moving relative to the substrate.

[0006] In summary, at present, the optical metasurface technology can realize the detection of multi-component gases. However, the quasi-BIC technology with higher sensitivity and lower detection limit has been applied to the field of biomolecule detection, but has not been applied to the field of gas detection. Multiple quasi-BICs can also be realized technically, but have not been applied to the field of gas detection. Therefore, there is no multi-component gas detection method based on the bound states in the metasurface quasi-continuum at home and abroad. How to prepare an efficient and sensitive metasurface-based quasi-BIC and how to realize its application in multi-component gas detection are urgent problems to be solved currently. Summary of the Invention

[0007] The present invention aims to solve the problems of insufficient detection limit and low recognition accuracy in the detection of multi-component gases in the prior art. For this purpose, the present invention proposes a metasurface-based bound state in the quasi-continuum with innovative design and optimized structure to achieve the goals of low detection limit and high recognition accuracy for multi-component gases.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] In the first aspect, the present invention provides a multi-component gas detection device based on the bound state in the metasurface quasi-continuum. The device includes a gas chamber, a metasurface array, an infrared radiation source, and a detector;

[0010] The gas chamber is used to accommodate and circulate the multi-component gas to be measured, and an air inlet and an air outlet are provided on the gas chamber;

[0011] The metasurface array is fixed inside the gas chamber and is composed of a substrate and a dielectric nanostructure layer. The dielectric nanostructure layer contains a series of microstructural units arranged in an array on the substrate, and the microstructural units are exposed to the multi-component gas environment inside the gas chamber. Each microstructural unit in the metasurface array is targeted at a specific infrared light resonance peak, and this metasurface array can achieve the characteristics of quasi-bound states in the continuous domain to enhance the interaction between gas molecules and infrared light.

[0012] The infrared radiation source is used to emit infrared light to the surface of the metasurface array.

[0013] The detector is used to receive the spectral signal of the infrared light reflected from the metasurface array.

[0014] The detector needs to send the received infrared spectral signal to an external data processing unit for identifying and quantifying different gas components in the multi-component gas to be measured.

[0015] As a preference of the above first aspect, the infrared radiation source is a tunable infrared laser source to achieve selective excitation of different infrared wavelengths.

[0016] As a preference of the above first aspect, the detector is a spectrometer.

[0017] As a preference of the above first aspect, the material type of the substrate is silicon or silica, and a layer of metal is coated on the bottom to prevent light from passing through.

[0018] As a preference of the above first aspect, the material types of the microstructural units in the dielectric nanostructure layer include silicon, silica, silicon nitride, graphene, alumina, magnesium fluoride, or other suitable dielectric materials. By selecting appropriate dielectric materials, the light confinement effect and the sensitivity of multi-component gas detection can be further enhanced. In addition, the geometric parameters that can be optimized for the dielectric nanostructure layer include the geometric pattern width, length, and thickness of the microstructural units, as well as the angle relative to the vertically incident light source. By adjusting these geometric parameters, the working range can be flexibly regulated in the mid-infrared region to meet the requirements of different application scenarios.

[0019] As a preference of the above first aspect, the geometric pattern of the microstructural units in the dielectric nanostructure layer includes rectangles, circles, ellipses, triangles, or other customized shapes. The geometric pattern size ranges from 500 to 5000 nm, the period ranges from 1500 to 7000 nm, and the geometric pattern supports the structure of quasi-BIC due to the breaking of C2 symmetry to achieve the optimization of the light confinement effect and the enhancement of the sensitivity of multi-component gas detection.

[0020] Preferably, in the first aspect described above, a temperature and a commercial gas sensor are provided in the gas chamber to monitor and adjust the environmental conditions in the gas chamber and ensure the accuracy of gas detection.

[0021] Preferably, in the first aspect described above, the data processing unit includes a spectral analysis and component detection module, which is implemented by a neural network. Its input is the infrared spectral signal received by the detector, and its output is the content of different gas components.

[0022] Preferably, in the first aspect described above, the neural network adopts one or a combination of more of a convolutional neural network (CNN), a long short-term memory network (LSTM), or a recurrent neural network (RNN).

[0023] In a second aspect, the present invention provides a multi-component gas detection method using any of the multi-component gas detection devices described in the first aspect above, which includes the following steps:

[0024] Introduce the multi-component gas to be detected into the gas chamber so that it fills the gas chamber and completely replaces the original gas in the gas chamber;

[0025] Then, emit infrared light from the infrared radiation source to the surface of the metasurface array, and then receive the infrared spectral signal reflected from the metasurface array through the detector; the interaction between the multi-component gas in the gas chamber and the surface of the metasurface array will produce a spectral response change in the characteristic band, so that the infrared spectral signal records the unique fingerprint information of different component gases;

[0026] Finally, send the infrared spectral signal received by the detector to an external data processing unit, and the neural network decouples the spectrum to obtain the concentrations of different types of gas components in the multi-component gas.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] The present invention provides an innovative multi-component gas detection device and method based on metasurface quasi-bound states in the continuum. It uses advanced nanofabrication technology and deep learning algorithms to achieve high-precision, high-sensitivity, and high-selectivity gas detection. The present invention utilizes a specifically designed metasurface array to achieve quasi-bound states in the continuum in the mid-infrared band by precisely regulating the geometric parameters and material properties of the microstructures, thereby obtaining excellent optical responses. In addition, the present invention combines deep learning algorithms to convert complex spectral information into accurate gas composition and concentration information. In summary, the present invention significantly improves the accuracy and efficiency of multi-component gas detection, meets the requirements of multi-component gas detection in fields such as environmental monitoring, industrial safety, and medical health detection, and provides an effective technical solution for realizing customized high-performance sensing of multi-component gases. Description of the Drawings

[0029] Figure 1 The correspondence between the metasurface array (taking 6×6 as an example) and the reflection spectrum;

[0030] Figure 2 Schematic diagram of the change in reflectivity of the metasurface in a multi-component gas environment;

[0031] Figure 3 Schematic diagram of the working principle of the integrated signal processing and deep learning algorithm for the metasurface array (taking 3×3 as an example);

[0032] Figure 4 Schematic diagram of a multi-component gas detection device based on the quasi-bound states in the continuum of the metasurface. Detailed Description of the Invention

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0034] Embodiment 1

[0035] In this embodiment, a method for processing a multi-component gas detection device based on quasi-bound states in the continuum and a method for detecting multi-component gases are provided, including the following steps:

[0036] (1) Design and fabricate a metasurface array

[0037] The metasurface array is composed of a substrate and a dielectric nanostructure layer, and the dielectric nanostructure layer contains a series of microstructural units arranged in an array form on the substrate.

[0038] In this embodiment, a dielectric nanostructure with specific dimensions, shapes and periods can be fabricated on a suitable substrate using a high-precision lithography process to form the desired light confinement characteristics. Taking a 6×6 metasurface array as an example in this embodiment, 36 microstructural units are exposed to the multi-component gas environment inside the gas chamber, and each microstructural unit is targeted at a specific infrared light resonance peak, and its correspondence with the reflection spectrum is as Figure 1 shown. This metasurface array can achieve the characteristics of quasi-bound states in the continuum to enhance the interaction between gas molecules and infrared light.

[0039] In this embodiment, the substrate of the metasurface array is selected from silicon or silicon dioxide. Meanwhile, a layer of metal material is coated on the bottom of the dielectric material layer to prevent light from passing through. Additionally, the dielectric nanostructure layer on the top surface of the substrate can be made of a dielectric material with a high refractive index and low absorption rate, such as silicon, silicon dioxide, graphene, or silicon nitride (Si3N4). At the same time, a metal material or metal oxide with a high refractive index can also be used, such as gold (Au), silver (Ag), aluminum (Al), aluminum oxide, magnesium fluoride, etc. During processing, high-precision electron beam lithography technology (energy range 8 keV - 30 keV) and ion beam etching technology (etching depth 20 nm - 200 nm) can be used to form nanostructures with the required size, pattern shape, and period. The geometric pattern size range of the microstructures is 500 - 5000 nm, the period range is 1500 - 7000 nm, and the shape is designed as rectangular, circular, elliptical, triangular, polygonal, or a customized shape.

[0040] (2) Expose the above metasurface array to the target multi-component gas environment. Due to the existence of quasi-bound states in the continuum, this structure can strongly scatter and absorb light within a specific frequency range.

[0041] (3) When infrared light is incident and reflected by the metasurface array, use a spectrometer to monitor the spectral response of the metasurface photon structure after exposure to the gas. The schematic diagram of the reflectivity change of the metasurface array in a multi-component gas environment is as Figure 2 shown. The interaction between the gas and the metasurface will cause spectral response changes in a specific wavelength band, thus providing unique fingerprints for different components of the gas.

[0042] (4) Use a neural network as a decoupling algorithm to automatically identify and analyze the patterns and features in the experimental data. The neural network can adopt a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory network (LSTM), etc. The input of the neural network is the infrared spectral signal received by the detector, and the output is the concentration of different types of gas components in the multi-component gas. This can not only improve the analysis accuracy of the experimental data but also help to more deeply understand the mechanism of action of quasi-BIC in multi-component gas detection. Taking LSTM as an example of the decoupling algorithm, the working principle of the integrated signal processing and deep learning algorithm of the metasurface array (taking 3×3 as an example) is as Figure 3 shown.

[0043] Example 2

[0044] Based on Example 1, in this example, the metasurface structure is further optimized to significantly enhance its sensitivity to specific gases. This optimization is mainly achieved by breaking the C2 symmetry of the dielectric nanostructure layer of the metasurface array to support a structure of quasi-bound states in the continuum (quasi-BIC). The specific methods include: 1. Exploring nanostructures of different shapes on the metasurface, such as star-shaped, circular-ring-shaped, and irregular polygon-shaped, to study their effects on light confinement characteristics. Through symmetry-breaking designs, such as asymmetric bilayer structures or non-centrosymmetric arrangements, the support for quasi-BIC modes is enhanced. 2. Adjusting the size range of the nanostructures, considering different sizes from the micrometer scale to the nanometer scale of dozens of nanometers, to optimize the scattering and absorption capabilities of light at specific wavelengths. Precisely controlling the height, width, and period of the nanostructures to finely adjust the optical response of the metasurface.

[0045] Example 3

[0046] Using the method described in Example 1, a portable multi-component gas detection device is developed. As Figure 4 shown, the device includes a metasurface, a gas chamber, an infrared radiation source, a spectrometer, and a data processing unit. The gas chamber is used to accommodate and circulate the multi-component gas to be measured, and an inlet and an outlet are provided on the gas chamber. The metasurface array is fixed inside the gas chamber and is composed of a substrate and a dielectric nanostructure layer, which can be specifically implemented according to Example 1 or Example 2. Moreover, the metasurface array needs to be reasonably installed in the gas chamber so that the microstructure units on its surface are fully exposed to the multi-component gas environment inside the gas chamber. The infrared radiation source is placed on one side of the gas chamber and is used to emit infrared light to the surface of the metasurface array. The spectrometer is set on the other side of the gas chamber and is used to receive the spectral signals of the infrared light reflected from the metasurface array. The detector needs to send the received infrared spectral signals to an external data processing unit, and different gas components in the multi-component gas to be measured are identified and quantified through the aforementioned spectral decoupling algorithm module. In this example, the gas chamber can be designed as a small gas chamber (internal dimensions of approximately 15 cm × 15 cm × 15 cm), made of transparent polycarbonate, and equipped with an adjustable gas flow controller; the infrared radiation source can use a tunable infrared laser with a wavelength coverage range of 2.5 - 15 μm; the spectrometer can select a Fourier transform infrared spectrometer with high sensitivity and a resolution of 0.1 nm; the data processing unit can use a microcomputer integrated with signal processing and data transmission functions, supporting wireless data transmission, and is used to analyze and display the detection results in real time.

[0047] Example 4

[0048] To further optimize the detection effect, different sensitive materials can be selected to coat the dielectric nanostructure layer on the metasurface array and observe its response to various target gases.

[0049] (1) Prepare the metasurface and coat it with the sensing material according to the steps in Example 1. Select materials with high gas adsorption capacity, such as metal-organic frameworks (MOFs), as the sensitive layer. Choose solution deposition method or other methods for film formation, and control the film thickness to about 50 nm.

[0050] (2) Introduce various standard gases, such as carbon dioxide (CO2), methane (CH4), and ammonia (NH3), into the controlled environment, and control their concentrations by adjusting the flowmeter. Use a Fourier transform infrared spectrometer (FTIR) to record the spectral response of the metasurface in different gas environments. Focus on the changes in the absorption or reflection peaks at specific wavelengths.

[0051] (3) Select neural networks suitable for time series analysis, such as a combined model of recurrent neural network (RNN) and convolutional neural network (CNN), as the spectral decoupling algorithm module. Train the neural network model with a large amount of labeled data with different concentrations and types of gases. Then, by analyzing the spectral data processed by the model, the characteristic spectra of different gases can be determined, and the network outputs the type and concentration of each gas.

[0052] Example 5

[0053] The following shows several application scenarios of the gas detection device and method in the above embodiments.

[0054] 1. Gas detection for early warning of lithium battery thermal runaway

[0055] Detection target: Detection of gases such as CO2, CO, H2, CH4, C2H4, and C3H6, which are early indicators of overheating or thermal runaway of lithium batteries.

[0056] Detection technology: Use infrared spectroscopy technology, equipped with infrared sensors at specific wavelengths, to identify and quantify these gases.

[0057] Experimental setup: Test in an environment simulating battery thermal runaway, simulate different overheating conditions by adjusting temperature and current, and record the changes in the concentrations of various gases.

[0058] 2. Detection of disease markers in exhaled breath

[0059] Detection range: In the infrared wavelength range of 8 - 12 μm, focus on the detection of nitrogen oxides, acetone, and nitrogen-containing compounds (ammonia, dimethylamine, trimethylamine).

[0060] Application scenario: Used to monitor specific compounds in human exhaled breath as an early diagnostic tool for diseases.

[0061] Device configuration: Use a portable infrared spectroscopy analyzer, designed as a non-invasive exhalation analysis tool for convenient use by patients.

[0062] 3. Ambient Air Quality Detection

[0063] Detection Objectives: Focus on detecting gases such as SO2 and CO2 in the wavelength range of 3 - 5μm, which are the main components of air pollution.

[0064] Monitoring Method: Deployed at key locations in the city, such as industrial areas and transportation hubs, using remote sensing technology to monitor air quality in real time.

[0065] Data Processing: Combining Geographic Information System (GIS) and Real - Time Air Quality Index (AQI) reports to provide data support for environmental monitoring and policy making.

[0066] 4. Chemical and Explosive Detection

[0067] Detection Range: Detect chemical substances such as TNT and acetone in the wavelength range of 7 - 10μm. These compounds are common in explosives and certain industrial chemicals.

[0068] Application Scenarios: Used for security inspection and detection of dangerous goods, especially at airports, railway stations and important public places.

[0069] Technical Implementation: Deploy high - sensitivity infrared detectors, combined with an automatic alarm system and a real - time data analysis platform to improve detection efficiency and safety.

[0070] It is easy for those skilled in the art to understand that the above - mentioned are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-component gas detection device based on the quasi-bound states in the continuum of the metasurface, characterized in that The device includes a gas chamber, a metasurface array, an infrared radiation source, and a detector; The gas chamber is used to accommodate and circulate the multi-component gas to be measured, and an air inlet and an air outlet are provided on the gas chamber; The metasurface array is fixed inside the gas chamber and is composed of a substrate and a dielectric nanostructure layer. The dielectric nanostructure layer contains a series of microstructure units arranged in an array form on the substrate, and the microstructure units are exposed to the multi-component gas environment inside the gas chamber; each microstructure unit in the metasurface array is targeted at a specific infrared light resonance peak, and the metasurface array can achieve the characteristics of bound states in the quasi-continuum to enhance the interaction between gas molecules and infrared light; The infrared radiation source is used to emit infrared light to the surface of the metasurface array; The detector is used to receive the spectral signal of the infrared light reflected from the metasurface array; The detector needs to send the received infrared spectral signal to an external data processing unit for identifying and quantifying different gas components in the multi-component gas to be measured.

2. The multi-component gas detection device according to claim 1, characterized in that, The infrared radiation source is a tunable infrared laser source to achieve selective excitation of different infrared wavelengths.

3. The multi-component gas detection device according to claim 1, wherein, The detector is a spectrometer.

4. The multi-component gas detection device according to claim 1, characterized in that, The material type of the substrate is silicon or silicon dioxide, and a layer of metal is coated on the bottom to prevent light from passing through.

5. The multi-component gas detection device according to claim 1, wherein The material type of the microstructure units in the dielectric nanostructure layer includes silicon, silicon dioxide, silicon nitride, graphene, aluminum oxide, magnesium fluoride, or other suitable dielectric materials.

6. The multi-component gas detection device according to claim 1, characterized in that, The geometric patterns of the microstructure units in the dielectric nanostructure layer include rectangles, circles, ellipses, triangles, or other customized shapes. The size range of the geometric patterns is 500 - 5000 nm, and the period range is 1500 - 7000 nm. The geometric patterns support the structure of quasi-BIC due to the breaking of C2 symmetry to optimize the light confinement effect and enhance the detection sensitivity of multi-component gases.

7. The multi-component gas detection device according to claim 1, characterized in that, A temperature and a commercial gas sensor are arranged inside the gas chamber to monitor and adjust the environmental conditions inside the gas chamber to ensure the accuracy of gas detection.

8. The multi-component gas detection device according to claim 1, characterized in that, The data processing unit includes a spectral analysis and component detection module. The spectral analysis and component detection module is implemented through a neural network. Its input is the infrared spectral signal received by the detector, and the output is the content of different gas components.

9. The multi-component gas detection device according to claim 1, wherein The neural network adopts one or a combination of convolutional neural network (CNN), long short-term memory network (LSTM), or recurrent neural network (RNN).

10. A multi-component gas detection method using the multi-component gas detection device according to any one of claims 1 to 9, characterized in that, It includes the following steps: Introduce the multi-component gas to be detected into the gas chamber to fill the gas chamber and completely replace the original gas inside the gas chamber; Then emit infrared light to the surface of the metasurface array through the infrared radiation source, and then receive the infrared spectral signal reflected from the metasurface array through the detector; the interaction between the multi-component gas inside the gas chamber and the surface of the metasurface array will generate spectral response changes in the characteristic band, so that the unique fingerprint information of different component gases is recorded in the infrared spectral signal; Finally, send the infrared spectral signal received by the detector to an external data processing unit, and the neural network decouples the spectrum to obtain the concentrations of different types of gas components in the multi-component gas.

Images