Sound wave sensing device based on carbon nano tube

Abstract

The invention provides a sound wave sensing device based on a carbon nanotube, and the device comprises a front-end sound wave receiving and transmitting module which is used for receiving sound waves and converting the sound waves into mechanical vibration; wherein the front-end sound wave receiving and conducting module comprises a front-end vibrating membrane and a transmission guided wave structure; the multi-walled carbon nanotube array sensing unit is used for converting the mechanical vibration into an electrical signal; the signal acquisition module is arranged around the multi-walled carbon nanotube array sensing unit and is used for acquiring electrical signals; the signal processing module is used for carrying out amplification, filtering and digital processing on the collected electrical signals; and the sound wave output port is used for outputting the processed signal to external equipment. According to the invention, the defects of the traditional acoustic sensor in the aspects of sensitivity, frequency response range, environmental adaptability, energy consumption and the like can be overcome, so that high-precision, wide-frequency-band and strong-environmental-adaptability acoustic signal detection is realized.

Description

Technical Field

[0001] The present invention relates to the field of acoustic wave sensing technology, and in particular to an acoustic wave sensing device based on carbon nanotubes, which is widely used in industrial equipment monitoring, medical diagnosis, environmental monitoring and other fields. Background Art

[0002] An acoustic wave sensor is a sensing technology that uses the propagation characteristics of sound waves to detect the state of an environment or object. It senses the presence, location, or characteristics of a target through changes in the reflection, refraction, or propagation time of sound waves. However, traditional acoustic wave sensors typically operate based on piezoelectric materials, capacitive structures, or fiber optic technology, and their detection accuracy and sensitivity are limited to a certain extent by the physical properties of the materials. Traditional acoustic wave sensors often rely on preset fixed algorithms or thresholds and are primarily suitable for acoustic wave detection of static targets, with limited real-time monitoring capabilities for complex dynamic scenarios. Furthermore, traditional technologies tend to degrade in performance under high temperature, high humidity, or corrosive environments, and their narrow frequency response range makes it difficult to cover the full frequency band from low to high frequencies. Consequently, they cannot fully meet the needs of modern industrial, medical, and intelligent devices for high-precision, wide-frequency, and environmentally adaptable acoustic wave detection.

[0003] In recent years, acoustic wave sensing technology has gained increasing attention, aiming to provide users with a more accurate and efficient detection experience. Its applications in industry, medicine, and consumer electronics continue to expand. Currently, technologies similar to carbon nanotube-based acoustic wave sensor technology on the market mainly include acoustic wave sensors based on piezoelectric ceramics, quartz crystals, conductive polymers, polyvinylidene fluoride (PVDF), etc. The most similar technology is polyvinylidene fluoride (PVDF) acoustic wave sensor technology. However, these traditional sensor devices still have some problems compared to carbon nanotube-based acoustic wave sensor devices:

[0004] Inaccurate sensitivity: Although PVDF is a highly sensitive piezoelectric material, it still exhibits certain limitations compared to carbon nanotubes. PVDF has limited detection capabilities for low sound pressure signals or weak sound waves, especially in complex background noise or long-distance signal acquisition. It is easily interfered with by environmental noise, resulting in increased detection errors. In addition, PVDF has a slow response speed in dynamically changing scenarios, making it difficult to accurately capture rapidly changing sound wave signals, making it perform poorly in high-precision application scenarios (such as ultrasonic medical imaging or industrial vibration monitoring). Carbon nanotubes, due to their ultra-high electron mobility and sensitivity, can capture more subtle sound wave signals in similar scenarios.

[0005] Narrow frequency response range: PVDF's piezoelectric properties determine that its frequency response range is mainly concentrated in the medium and low frequency bands, which is suitable for detecting voice frequencies or medium-frequency vibration signals. However, when the application is extended to the high-frequency range (such as ultrasonic detection or high-frequency sound wave signal analysis), the response ability of PVDF drops significantly, and it may even be completely unable to detect. The limitations of frequency response make PVDF difficult to adapt to scenarios that require wide-band detection, such as multi-frequency monitoring from low-frequency mechanical noise to high-frequency sharp sound waves. Carbon nanotubes, with their unique nanoscale structure and excellent electrical properties, can easily cover a wide frequency range from low frequency to ultrasonic waves, meeting more complex and diverse application requirements.

[0006] High energy consumption: While PVDF's piezoelectric properties can convert sound waves into electrical signals, its conversion efficiency is relatively low, meaning that detecting weak acoustic signals requires greater energy input to amplify and process the signal. This energy consumption issue is particularly pronounced in long-term monitoring or large-scale deployment scenarios, such as distributed acoustic sensor networks in IoT devices. Furthermore, PVDF material requires additional signal amplification for high-frequency detection, further increasing energy consumption. However, carbon nanotubes, with their high conductivity and electroacoustic conversion efficiency, enable efficient signal detection with low energy consumption, making them particularly suitable for portable devices and low-power scenarios.

[0007] Poor environmental adaptability: PVDF's mechanical properties and chemical stability are weak in extreme environments, especially in high temperature, high humidity or corrosive environments, where they are prone to degradation. For example, under high temperature conditions, the piezoelectric properties of PVDF will drop significantly, and its signal output may drift or even fail. In a high humidity environment, the mechanical strength of the PVDF material will be reduced due to water absorption, which in turn affects its sensing performance. In addition, PVDF has poor tolerance to acid and alkali corrosion and is difficult to work stably for a long time in industrial chemical environments or marine environments. Carbon nanotubes have excellent chemical stability and heat resistance, and can maintain stable performance in high temperature environments of up to hundreds of degrees Celsius. At the same time, they can still work normally in environments with high humidity, making them very suitable for long-term monitoring under harsh conditions.

[0008] Miniaturization limitations: PVDF is flexible and lightweight, but its processing thickness is often limited by material properties, preventing it from reaching nanoscale dimensions. Furthermore, the performance of PVDF films decreases with decreasing thickness, meaning that even in miniaturized designs, ideal sensing performance cannot be maintained. This limitation makes PVDF difficult to meet the miniaturization and integration requirements of future high-precision sensors, such as those used in embedded devices or nanorobots. Carbon nanotubes, on the other hand, can be easily fabricated into ultrathin films with a thickness of only nanometers, maintaining excellent sensing performance while supporting flexible designs, making them a perfect fit for miniaturization and integration.

[0009] While PVDF is widely used in traditional acoustic wave sensing, its limitations in sensitivity, frequency response range, energy consumption, environmental adaptability, and miniaturization make it difficult to meet the demands of modern high-precision, multifunctional, and complex environmental applications. In contrast, carbon nanotubes, with their ultra-high sensitivity, wide frequency response, low power consumption, strong environmental adaptability, and miniaturization capabilities, offer broader application prospects in acoustic wave sensing technology.

[0010] Therefore, to improve the sensitivity and accuracy of acoustic wave detection and meet the needs of acoustic wave monitoring in complex scenarios, there is an urgent need for the application of low-cost, high-precision acoustic wave sensing technology that does not rely on high-power devices, has a wide frequency response capability, is highly adaptable to environmental changes, and can maintain stable performance in complex environments while achieving efficient capture and precise analysis of weak acoustic wave signals to meet the diverse needs of modern industry, medical care, consumer electronics and other fields. Summary of the Invention

[0011] In response to the problems and shortcomings of the existing technology, the present invention provides a carbon nanotube-based acoustic wave sensing device, which aims to address the shortcomings of traditional acoustic wave sensors in terms of sensitivity, frequency response range, environmental adaptability and energy consumption, and realize high-precision, wide-band and environmentally adaptable acoustic wave signal detection.

[0012] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0013] A carbon nanotube-based acoustic wave sensing device, comprising:

[0014] A front-end sound wave receiving and conducting module for receiving sound waves and converting them into mechanical vibrations; wherein the front-end sound wave receiving and conducting module includes a front-end diaphragm located at the front end of the device, directly facing the direction of sound wave input, for capturing sound waves and converting them into mechanical vibrations; a transmission waveguide structure, arranged behind the front-end diaphragm, for efficiently transmitting mechanical vibrations to the multi-walled carbon nanotube array sensing unit;

[0015] A multi-walled carbon nanotube array sensing unit, connected to the front-end sound wave receiving and conducting module, for converting mechanical vibrations into electrical signals;

[0016] a signal acquisition module, arranged around the multi-walled carbon nanotube array sensing unit, and configured to acquire the electrical signal;

[0017] A signal processing module, connected to the signal acquisition module, for amplifying, filtering and digitizing the collected electrical signals;

[0018] The sound wave output port is connected to the signal processing module and is used to output the processed signal to an external device.

[0019] According to a carbon nanotube-based acoustic wave sensing device provided by the present invention, the multi-walled carbon nanotube array sensing unit includes:

[0020] Multi-walled carbon nanotube arrays, composed of multiple multi-walled carbon nanotubes, are used to convert mechanical vibrations into electrical signals;

[0021] The base support layer is used to provide mechanical support to ensure that the vibration signal is evenly transmitted to the multi-walled carbon nanotube array.

[0022] The multi-walled carbon nanotubes are grown on a substrate support layer by chemical vapor deposition to form a regularly arranged array, wherein each multi-walled carbon nanotube has a diameter in the range of 10 to 50 nanometers and a length in the range of 10 to 100 micrometers, and the total area of ​​the array matches the effective receiving area of ​​the front diaphragm;

[0023] Among them, the base support layer is made of single crystal silicon, quartz glass or alumina ceramic, and its surface is covered with a conductive film. The conductive film is selected from platinum, gold or other highly conductive materials to enhance the electrical connection between the multi-walled carbon nanotube array and the signal acquisition circuit.

[0024] According to a carbon nanotube-based acoustic wave sensing device provided by the present invention, the signal acquisition module includes:

[0025] a metal ring, arranged around the multi-walled carbon nanotube array sensing unit, for uniformly collecting the electrical signal;

[0026] an auxiliary shielding layer, covering the outside of the metal ring and used to prevent electromagnetic interference from entering the signal path;

[0027] Among them, the auxiliary shielding layer is composed of a composite of aluminum-plated polyester film PET and polyimide film, in which the aluminum-plated layer faces the external electromagnetic interference source, and the polyimide layer adheres to the surface of the metal ring; the shielding layer is tightly combined with the metal ring through conductive glue or mechanical fasteners to form a continuous electromagnetic shielding structure.

[0028] According to a carbon nanotube-based acoustic wave sensing device provided by the present invention, the signal processing module includes:

[0029] A signal amplifier uses a low-noise operational amplifier chip, paired with metal film resistors and ceramic capacitors to form a differential input structure. Its gain is adjustable from 20dB to 60dB, and is used to amplify weak electrical signals to a detectable range to meet the amplification requirements of signals with different sound pressure levels.

[0030] The filter is a multi-stage passive filter network composed of ferrite core inductors and tantalum capacitors, including low-pass, band-pass, and high-pass switchable modules. The cutoff frequency is dynamically configured by a microprocessor, covering the frequency range of 20Hz to 500kHz, and is used to remove noise and useless frequency bands;

[0031] The analog-to-digital converter uses the ADS1115 chip with 16-bit resolution and integrates a programmable gain amplifier (PGA). It is connected to the filter output via a high-speed SPI bus to convert the analog signal into a digital signal.

[0032] The microprocessor is used to execute built-in algorithms to perform real-time processing and feature extraction of digital signals. The microprocessor is based on the ARM Cortex-M architecture, has a built-in floating-point unit (FPU) and Flash memory, runs a fast Fourier transform algorithm and an adaptive noise suppression program, and extracts the frequency, amplitude, and phase characteristics of the sound wave signal in real time.

[0033] According to the present invention, a carbon nanotube-based acoustic wave sensing device, when acoustic waves propagate through an underwater or air environment to the acoustic wave receiving module of the sensor, the front-end diaphragm captures the acoustic wave signal and converts it into mechanical vibration. The mechanical vibration of the diaphragm is transmitted to the multi-walled carbon nanotube array sensing unit through a waveguide structure made of polytetrafluoroethylene (PTFE) or carbon fiber composite material.

[0034] The multi-walled carbon nanotube array responds to mechanical vibrations, and each carbon nanotube produces a piezoelectric effect and changes in conductivity due to deformation:

[0035] Piezoelectric effect: deformation causes the charge to be redistributed at both ends of the carbon nanotube, generating a weak voltage;

[0036] Conductivity change: Deformation changes the resistance or capacitance characteristics of carbon nanotubes, generating dynamic current signals;

[0037] The generated weak electrical signals are evenly collected by the signal collection module.

[0038] According to a carbon nanotube-based acoustic wave sensing device provided by the present invention, when a single carbon nanotube is subjected to an axial stress σ, the piezoelectric charge Q generated at both ends of the carbon nanotube is proportional to the deformation, and the formula is:

[0039] Q=d 33 ×F(F=σ×A)

[0040] Among them, d 33 =2.5×10-12C / N is the piezoelectric coefficient, F is the force, and A=π(d / 2)2 is the cross-sectional area of ​​the carbon nanotube.

[0041] The charge accumulated at both ends forms an open circuit voltage Vpiezo , the formula is:

[0042]

[0043] Among them, C tube is the static capacitance of a single carbon nanotube, ∈0 = 8.85 × 10-12 F / m is the vacuum dielectric constant;

[0044] When the carbon nanotube is subjected to radial pressure P, resulting in a diameter contraction of Δd, the relationship between its resistance change rate ΔR / R0 and strain ∈=Δd / d is:

[0045]

[0046] Among them, ν = 0.25 is Poisson's ratio, Δρ / ρ0 is the resistivity change;

[0047] According to the present invention, a carbon nanotube-based acoustic wave sensor device is provided, at a constant bias voltage V bias Under this condition, the current change ΔI is:

[0048]

[0049] Where ω is the signal angular frequency, τ = RC is the time constant, R is the contact resistance, and C is the parasitic capacitance;

[0050] The inner diameter of the metal ring electrode matches the outer diameter of the carbon nanotube array, with a spacing of ≤0.1mm;

[0051] A single carbon nanotube is equivalent to a voltage source V piezo With resistor R tube When connected in series, the total output voltage of the array is:

[0052]

[0053] Where n is the number of effective carbon nanotubes in the multi-walled carbon nanotube array, R load is the load resistance, R tube is the resistance of a single carbon nanotube.

[0054] According to the present invention, a carbon nanotube-based acoustic wave sensor device uses a low-noise operational amplifier chip. The input end of the low-noise operational amplifier chip uses metal film resistors R1 and R2 to construct a differential input circuit. The resistance values ​​meet the following matching conditions:

[0055] R1=R2=10kΩ±1%, R f =100kΩ±1%

[0056] Among them, R f is the feedback resistor, and the gain G is dynamically adjusted by the following formula:

[0057]

[0058] Ceramic capacitors C1 and C2 are connected in parallel at the input to suppress high-frequency noise;

[0059] The microprocessor dynamically modifies R f The resistance value can achieve linear adjustment of the gain range from 20dB to 60dB, which can meet the amplification requirements of signals with different sound pressure levels.

[0060] The third-order Butterworth filter is composed of ferrite core inductors L1 and L2 with Q value ≥ 50 and tantalum capacitors C3 and C4 with capacitance ranging from 0.1μF to 10μF. The cutoff frequency fc is dynamically adjusted through the microprocessor configuration register:

[0061]

[0062] The filter mode switching is achieved through a relay array, supporting three configurations: low-pass LPF, band-pass BPF, and high-pass HPF. Their transfer functions are:

[0063]

[0064] Ferrite core inductors provide ≥40dB of EMI attenuation in the 10kHz to 1GHz frequency band, and tantalum capacitors provide ≥60dB of ripple suppression in the DC to 1MHz frequency band.

[0065] According to a carbon nanotube-based acoustic wave sensing device provided by the present invention, the microprocessor divides the Flash memory into three areas:

[0066] Algorithm code area: stores the fast Fourier transform core program and adaptive noise suppression algorithm;

[0067] Data buffer: temporarily stores sampling signals and intermediate calculation results;

[0068] Parameter configuration area: stores dynamic parameters such as filter cutoff frequency, gain value, etc.

[0069] The digital signal x[n] (n=0, 1, ..., N-1) output by the analog-to-digital converter is transferred to the microprocessor memory through the DMA channel;

[0070] The radix 2-time decimation FFT algorithm is used to calculate the N-point discrete Fourier transform. The formula is:

[0071]

[0072] Where x(n) is the input time domain signal, X(k) is the frequency domain spectrum, and N is the number of sampling points.

[0073] According to the present invention, a carbon nanotube-based acoustic wave sensing device is provided, which uses FPU to accelerate complex multiplication, and the single-point FFT operation time is ≤10μs;

[0074] Spectral leakage is suppressed by the window function (Hanning window), and the window function coefficient w(n) is:

[0075]

[0076] The minimum control recursive averaging (MCRA) algorithm is used to dynamically estimate the noise power spectrum P noise (k):

[0077] P noise (k) = αP noise (k-1)+(1-α)|X(k)| 2

[0078] Among them, α is the smoothing coefficient.

[0079] Adjust the frequency domain gain G(k) according to the noise power to achieve spectral subtraction noise reduction:

[0080]

[0081] Among them, β is the over-reduction factor and γ is the gain lower limit.

[0082] Determine the signal frequency f by using the FFT main peak detection algorithm peak :

[0083]

[0084] Among them, k max is the spectrum maximum index, f s is the sampling rate.

[0085] Amplitude A:

[0086]

[0087] Phase φ:

[0088]

[0089] Where N is the number of sampling points or signal length, X(k max ) is the frequency k max The discrete Fourier transform result at contains the real part Re and the imaginary part Im.

[0090] It can be seen that compared with the prior art, the carbon nanotube-based acoustic wave sensing device proposed in the present invention has the following beneficial effects:

[0091] 1. The acoustic wave sensing device of this invention leverages the high sensitivity and excellent mechanical properties of carbon nanotubes to precisely capture subtle acoustic signals. The device can easily detect both extremely low and high-frequency sounds, providing users with higher-quality sound acquisition and authentic acoustic feedback. This makes the device widely applicable in fields such as medical diagnosis, audio engineering, and environmental monitoring. In the medical field, high-precision sound capture helps doctors more accurately diagnose conditions; in audio engineering, it provides a more realistic and detailed sound experience; and in environmental monitoring, it can more accurately capture and analyze sound signals in the environment, providing strong support for environmental protection.

[0092] 2. The acoustic wave sensing device of the present invention possesses excellent dynamic response performance, enabling real-time sound wave capture and analysis. Even in complex acoustic environments, the device maintains efficient and accurate response speeds, meeting the needs of demanding applications. For example, in real-time monitoring systems, the device can quickly capture and analyze abnormal sounds and issue timely alarms. In equipment fault diagnosis, it can accurately identify abnormal sounds emitted by equipment, helping maintenance personnel quickly locate the fault point. In high-frequency acoustic research, it can provide accurate and reliable data support.

[0093] 3. The present invention has innovated an efficient acoustic wave sensing solution by deeply combining the physical properties of carbon nanotubes with acoustic wave detection technology. The stability and anti-interference properties of carbon nanotubes enable the acoustic wave sensing device of the present invention to work stably in a variety of complex environments. Whether in extreme environments such as high temperature, high humidity or noise background, the device can maintain stable performance, effectively breaking through the limitations of traditional acoustic wave sensors in complex environments. The acoustic wave sensing device of the present invention adopts a lightweight and modular design concept. Thanks to the ultra-lightweight characteristics and high performance ratio of carbon nanotube materials, the sensor can achieve high-precision acoustic wave detection without complex hardware configuration, greatly reducing manufacturing costs and equipment energy consumption. At the same time, the modular design also facilitates the maintenance and upgrading of the sensor, improving the reliability and scalability of the equipment.

[0094] 4. The signal processing module of the present invention integrates a low-noise operational amplifier, a multi-stage filter, and a high-speed analog-to-digital converter, capable of real-time signal amplification, filtering, and digitization. This ensures that the sensor maintains efficient and accurate response speeds even in complex acoustic environments, making it suitable for demanding scenarios such as real-time monitoring and equipment fault diagnosis. Built-in fast Fourier transform (FFT) and pattern recognition algorithms can extract key characteristics of sound waves (such as frequency, amplitude, and phase) in real time, providing accurate data support for subsequent analysis, further improving the real-time and accuracy of dynamic response.

[0095] 5. Each functional module (such as signal acquisition, processing and output) is independently designed and easy to replace, upgrade and expand, which reduces manufacturing costs and maintenance difficulties. At the same time, it supports flexible integration with various systems such as underwater robots and acoustic monitoring stations.

[0096] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 This is a schematic diagram of an embodiment of a carbon nanotube-based acoustic wave sensing device of the present invention.

[0098] Figure 2 This is a schematic diagram of a process flow of an embodiment of a carbon nanotube-based acoustic wave sensing device of the present invention.

[0099] Figure 3 This is a schematic diagram of a cross-section of a multi-walled carbon nanotube in an embodiment of a carbon nanotube-based acoustic wave sensing device according to the present invention.

[0100] Figure 4 This is a schematic diagram of the relationship between frequency and output voltage under different sound pressure conditions in an embodiment of an acoustic wave sensor device based on carbon nanotubes of the present invention.

[0101] Figure 5 This is a schematic diagram of the change of signal-to-noise ratio with sound pressure under different frequency conditions in an embodiment of a carbon nanotube-based acoustic wave sensing device of the present invention.

[0102] Figure 6 This is a graph showing the change in output voltage with frequency in an embodiment of a carbon nanotube-based acoustic wave sensor device of the present invention. DETAILED DESCRIPTION

[0103] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0104] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0105] See also Figures 1 to 3 This embodiment provides a carbon nanotube-based acoustic wave sensing device, comprising:

[0106] A front-end sound wave receiving and conducting module, which is used to receive sound waves and convert them into mechanical vibrations. The front-end sound wave receiving and conducting module includes a front-end diaphragm located at the front end of the device, directly facing the direction of sound wave input, and is used to capture sound waves and convert them into mechanical vibrations. A transmission waveguide structure is arranged behind the front-end diaphragm, which is used to efficiently transmit mechanical vibrations to the multi-walled carbon nanotube array sensing unit.

[0107] The multi-walled carbon nanotube array sensing unit is connected to the front-end sound wave receiving and conducting module to convert mechanical vibrations into electrical signals;

[0108] A signal acquisition module is arranged around the multi-walled carbon nanotube array sensing unit and is used to collect electrical signals;

[0109] The signal processing module is connected to the signal acquisition module and is used to amplify, filter and digitize the collected electrical signals;

[0110] The sound wave output port is connected to the signal processing module and is used to output the processed signal to an external device.

[0111] The carbon nanotube-based acoustic wave sensing device provided in this embodiment is a high-precision acoustic wave detection device. The overall structure consists of an acoustic wave receiving and conducting module, a multi-walled carbon nanotube array sensing unit, a metal ring signal acquisition unit, a signal processing module, and an acoustic wave output port. The acoustic wave receiving and conducting module is located at the front end of the device, capturing acoustic waves through a vibrating membrane and converting them into mechanical vibrations. The multi-walled carbon nanotube array sensing unit immediately follows it, which utilizes the changes in resistance and capacitance of the carbon nanotubes to convert the mechanical vibrations captured by the vibrating membrane into electrical signals. The metal ring signal acquisition unit is then arranged around the sensing unit to ensure that the signal can be evenly collected and transmitted to the signal processing module. The signal processing module then analyzes the signal through amplification, filtering, and digital processing, and transmits the result to an external device through the acoustic wave output port.

[0112] In this embodiment, the front-end diaphragm is located at the front end of the device, directly facing the input direction of the sound wave, and is the entrance part of the entire sensor. The front-end diaphragm is tightly attached to the inner side of the shell of the sound wave receiving module and connected to the waveguide structure. The diaphragm is made of polyimide (PI) film, which is durable and relatively light. It has high sensitivity to sound waves and can more accurately sense low-frequency, ultra-high-frequency and other sound waves. It is used to capture underwater sound waves and convert the wave energy of the sound waves into mechanical vibrations to provide input for subsequent sensing modules. It adapts to a wide frequency range (low-frequency ambient sound to high-frequency sound waves) to ensure capture efficiency and signal integrity.

[0113] In this embodiment, the transmission waveguide structure is closely attached to the back of the front vibrating membrane, and serves as the transmission path of the vibration signal, connecting the front vibrating membrane and the middle multi-walled carbon nanotube array sensing unit. Made of carbon fiber composite material or polytetrafluoroethylene (PTFE, Teflon), it has good sound wave conduction performance, especially stable performance in the low and high frequency ranges, excellent wide frequency response performance, and excellent performance at low frequencies. Low cost and light weight. The mechanical vibration captured by the vibrating membrane is efficiently transmitted to the carbon nanotube array to reduce the attenuation of signal energy. The smooth inner wall design avoids sound wave reflection and interference, ensuring signal integrity. It supports high-sensitivity transmission mechanism and realizes high-fidelity sound wave guiding in underwater environment.

[0114] In this embodiment, the multi-walled carbon nanotube array sensing unit includes:

[0115] Multi-walled carbon nanotube arrays (MWCNTs), composed of multiple multi-walled carbon nanotubes, are used to convert mechanical vibrations into electrical signals;

[0116] The base support layer is used to provide mechanical support to ensure that the vibration signal is evenly transmitted to the multi-walled carbon nanotube array.

[0117] The multi-walled carbon nanotubes are grown on a substrate support layer by chemical vapor deposition to form a regularly arranged array, wherein each multi-walled carbon nanotube has a diameter in the range of 10 to 50 nanometers and a length in the range of 10 to 100 micrometers, and the total area of ​​the array matches the effective receiving area of ​​the front diaphragm;

[0118] Among them, the base support layer is made of single crystal silicon, quartz glass or alumina ceramic, and its surface is covered with a conductive film. The conductive film is selected from platinum, gold or other highly conductive materials to enhance the electrical connection between the multi-walled carbon nanotube array and the signal acquisition circuit.

[0119] In this embodiment, the signal acquisition module includes:

[0120] A metal ring is arranged around the multi-walled carbon nanotube array sensing unit to uniformly collect electrical signals;

[0121] Auxiliary shielding layer, covering the outside of the metal ring, used to prevent electromagnetic interference from entering the signal path;

[0122] Among them, the auxiliary shielding layer is composed of a composite of aluminum-plated polyester film PET and polyimide film, in which the aluminum-plated layer faces the external electromagnetic interference source, and the polyimide layer adheres to the surface of the metal ring; the shielding layer is tightly combined with the metal ring through conductive glue or mechanical fasteners to form a continuous electromagnetic shielding structure.

[0123] In this embodiment, the signal processing module includes:

[0124] The signal amplifier uses a low-noise operational amplifier chip, paired with metal film resistors and ceramic capacitors to form a differential input structure. Its gain is adjustable from 20dB to 60dB, and is used to amplify weak electrical signals to a detectable range to meet the amplification requirements of signals with different sound pressure levels.

[0125] The filter is a multi-stage passive filter network composed of ferrite core inductors and tantalum capacitors. It includes low-pass, band-pass, and high-pass switchable modules. The cutoff frequency is dynamically configured by the microprocessor and covers the frequency range of 20Hz to 500kHz. It is used to remove noise and useless frequency bands.

[0126] The analog-to-digital converter uses the ADS1115 chip with 16-bit resolution and integrates a programmable gain amplifier (PGA). It is connected to the filter output via a high-speed SPI bus to convert the analog signal into a digital signal.

[0127] The microprocessor is used to execute built-in algorithms to perform real-time processing and feature extraction of digital signals. The microprocessor is based on the ARM Cortex-M architecture, has a built-in floating-point unit (FPU) and Flash memory, runs a fast Fourier transform algorithm and an adaptive noise suppression program, and extracts the frequency, amplitude, and phase characteristics of the sound wave signal in real time.

[0128] Specifically, the preparation and integration of the carbon nanotube array sensing unit includes:

[0129] Multi-walled carbon nanotube (MWCNT) arrays were grown by chemical vapor deposition (CVD) with acetylene (C2H2) as the carbon source and iron (Fe) as the catalyst to grow vertically aligned MWCNT arrays on a single crystal silicon substrate.

[0130] Process parameters: growth temperature: 700-800°C; gas flow: C2H2 50sccm, H2 200sccm, Ar 500sccm; growth time: 30 minutes, forming a MWCNT array with a height of 110±5 microns and a diameter of 10-50 nanometers.

[0131] Fixed coating: Spin-coat a polyimide solution (thickness 2-5 μm) on the array surface and cure it at 350°C to form a mechanical support layer to prevent the array from collapsing.

[0132] The preparation of the base support layer includes:

[0133] Substrate material: Single crystal silicon (thickness 0.5 mm) is selected, and a platinum conductive film (thickness 200 nm) is deposited on the surface by magnetron sputtering, with a resistivity of ≤10- 4 Ω·cm.

[0134] Structural integration: The MWCNT array is transferred to the central area of ​​the substrate, the electrode pattern is defined by photolithography, and gold / chromium (Au / Cr, thickness 100 / 10 nm) is deposited by electron beam evaporation as a metal electrode to form ohmic contact with both ends of the MWCNT array.

[0135] The assembly of the signal acquisition module includes:

[0136] Metal ring design and manufacturing

[0137] Material selection: The inner ring is made of gold-plated copper (Au / Cu, thickness 5 / 0.5 mm), and the outer ring is covered with aluminized polyester film (PET / Al, thickness 12 microns) as an electromagnetic shielding layer.

[0138] Structural parameters: inner diameter: 0.2 mm larger than the outer diameter of the MWCNT array; width: 3 mm; surface roughness: Ra ≤ 0.1 μm (polished).

[0139] Fixing method: The metal ring is fixed to the edge of the substrate using polyimide tape to ensure that the electrical contact resistance with the MWCNT array electrode is ≤0.1Ω.

[0140] Auxiliary shield integration

[0141] Shielding film pasting: Wrap the aluminum-plated PET film on the outer surface of the metal ring, and pass the conductive silver glue (volume resistivity ≤10- 3 Ω·cm) is connected to the metal ring ground wire to form a Faraday cage structure.

[0142] Fixed support structure: Alumina ceramic frame (thickness 2 mm) is used to support the metal ring, which is fixed to the base through metal screws (M2), with a spacing stability of ≤1 micron.

[0143] The circuit implementation of the signal processing module includes:

[0144] Signal amplifier design

[0145] Circuit topology: A three-stage low-noise amplifier (LNA) is used, and the TL082 operational amplifier is selected with a gain-bandwidth product (GBW) of 3MHz.

[0146] Component parameters:

[0147] Input resistance: 10MΩ (metal film resistor); feedback capacitance: 10pF (ceramic capacitor);

[0148] Power supply voltage: ±5V (powered by linear regulator LDO).

[0149] PCB layout: Use a four-layer glass fiber (FR4) substrate, isolate the signal layer from the power layer, and keep the parasitic capacitance ≤ 5pF.

[0150] Filter Implementation

[0151] Filter type: Bandpass filter (center frequency 100kHz, bandwidth 20kHz).

[0152] Component Selection:

[0153] Capacitor: NP0 type ceramic capacitor (10nF, accuracy ±1%); Inductor: Ferrite core coil (100μH, Q value ≥50); Resistor: Metal film resistor (1kΩ, temperature coefficient ±25ppm / ℃).

[0154] Simulation optimization: S-parameter simulation was performed using ADS software, with insertion loss ≤ 1dB and out-of-band suppression ≥ 40dB.

[0155] Analog-to-digital converter (ADC) configuration

[0156] Chip selection: ADS1115 (16 bits, sampling rate 860SPS).

[0157] Interface circuit: Input: connected to the filter output, high-frequency noise is suppressed by RC low-pass filtering (cut-off frequency 10kHz); Reference voltage: 2.048V (internal reference source); Communication protocol: I 2 C (clock frequency 400kHz).

[0158] Microprocessor Programming

[0159] Chip selection: STM32F407 (ARM Cortex-M4, main frequency 168MHz).

[0160] Algorithm implementation:

[0161] Fast Fourier Transform (FFT): Processes 1024-point data and calculates the spectrum in real time. Dynamic Calibration: Outputs test signals through the built-in DAC to automatically adjust the amplifier gain.

[0162] Power management: LDO (LP2985) and DC-DC converter (TPS62175) are used to achieve multi-voltage output (3.3V / 1.8V), with quiescent current ≤10μA.

[0163] The acoustic wave output port is integrated with the power module

[0164] Data interface design

[0165] Type selection: USB-C interface (supports USB 2.0 high-speed transmission, rate 480Mbps).

[0166] Electrical connections:

[0167] Differential signal line: length difference ≤ 50 mil (impedance matching 90Ω); power line: VBUS (5V, current carrying capacity 1.5A).

[0168] Power management module implementation

[0169] Topology: Switching power supply (BUCK converter, LM2596).

[0170] Parameter design:

[0171] Input voltage: 9-36V (compatible with vehicle power supply);

[0172] Output voltage: 5V / 3.3V (accuracy ±1%);

[0173] Efficiency: ≥90% (at full load).

[0174] Protection functions: overvoltage protection (OVP, trigger voltage 40V), overcurrent protection (OCP, current limit 2A).

[0175] In this embodiment, system assembly and testing includes:

[0176] Fixing the MWCNT array sensing unit on the base support layer;

[0177] Install the signal acquisition module (metal ring + shielding layer);

[0178] Welding signal processing module (PCBA);

[0179] Connect the sound wave output port to the power module;

[0180] Overall packaging (IP68 waterproof, depth ≤ 100 meters).

[0181] Sensitivity test: input 1Pa sound pressure, output voltage ≥50mV (frequency 1kHz);

[0182] Frequency response: flatness ±1dB (10Hz-1 MHz);

[0183] Noise level: Equivalent input noise ≤ 5nV / √Hz (at 1kHz);

[0184] Electromagnetic compatibility (EMC): Meets IEC 61000-4-3 standard (radiated immunity 20V / m).

[0185] Application scenario 1: Ocean monitoring

[0186] Deployment method: Fix the sensor to the seabed observation station and connect it to the underwater robot via the USB-C interface.

[0187] Test data: Captured a weak 0.1Pa acoustic signal (corresponding to marine biological activity); worked continuously for 72 hours with 30% battery power remaining (lithium-ion battery, 5000mAh capacity).

[0188] Application Scenario 2: Medical Ultrasound Imaging

[0189] Integration method: Replace the traditional PZT piezoelectric probe and communicate with the B-ultrasound host via Wi-Fi.

[0190] Imaging effect: The resolution is increased to 0.1 mm (traditional equipment is 0.3 mm), and tumors with a diameter of 2 mm can be detected.

[0191] This embodiment utilizes the piezoelectric-conductive coupling effect of the MWCNT array, combined with modular design and low-power circuits, to achieve significant improvements in the sensitivity (≥50mV / Pa), frequency range (10Hz-1MHz), and environmental adaptability (IP68) of the acoustic wave sensor. It can be widely used in marine, medical, and industrial detection fields.

[0192] In this embodiment, when sound waves propagate through an underwater or air environment to the sensor's sound wave receiving module, the front-end diaphragm captures the sound wave signal and converts it into mechanical vibration. The mechanical vibration of the diaphragm is transmitted to the multi-walled carbon nanotube array sensing unit through a waveguide structure made of polytetrafluoroethylene (PTFE) or carbon fiber composite material.

[0193] The multi-walled carbon nanotube array responds to mechanical vibrations, and each carbon nanotube produces a piezoelectric effect and changes in conductivity due to deformation:

[0194] Piezoelectric effect: deformation causes the charge to be redistributed at both ends of the carbon nanotube, generating a weak voltage;

[0195] Conductivity change: Deformation changes the resistance or capacitance characteristics of carbon nanotubes, generating dynamic current signals;

[0196] The weak electrical signals generated are evenly collected by the signal acquisition module.

[0197] When a single carbon nanotube is subjected to axial stress σ, the piezoelectric charge Q generated at its two ends is proportional to the deformation, and the formula is:

[0198] Q=d 33 ×F(F=σ×A)

[0199] Among them, d 33 =2.5×10-12C / N is the piezoelectric coefficient, F is the force, and A=π(d / 2)2 is the cross-sectional area of ​​the carbon nanotube.

[0200] The charge accumulated at both ends forms an open circuit voltage V piezo , the formula is:

[0201]

[0202] Among them, C tube is the static capacitance of a single carbon nanotube, ∈0 = 8.85 × 10-12 F / m is the vacuum dielectric constant;

[0203] When the carbon nanotube is subjected to radial pressure P, resulting in a diameter contraction of Δd, the relationship between its resistance change rate ΔR / R0 and strain ∈=Δd / d is:

[0204]

[0205] Among them, ν = 0.25 is Poisson's ratio, Δρ / ρ0 is the resistivity change;

[0206] At a constant bias voltage V bias Under this condition, the current change ΔI is:

[0207]

[0208] Where ω is the signal angular frequency, τ = RC is the time constant, R is the contact resistance, and C is the parasitic capacitance;

[0209] The inner diameter of the metal ring electrode matches the outer diameter of the carbon nanotube array, with a spacing of ≤0.1mm;

[0210] A single carbon nanotube is equivalent to a voltage source V piezo With resistor R tube When connected in series, the total output voltage of the array is:

[0211]

[0212] Where n is the number of effective carbon nanotubes in the multi-walled carbon nanotube array, R load is the load resistance, R tube is the resistance of a single carbon nanotube.

[0213] In this embodiment, a low-noise operational amplifier chip is used, and its input end is connected to a differential input circuit through metal film resistors R1 and R2. The resistance values ​​meet the following matching conditions:

[0214] R1=R2=10kΩ±1%, R f =100kΩ±1%

[0215] Among them, R f is the feedback resistor, and the gain G is dynamically adjusted by the following formula:

[0216]

[0217] Ceramic capacitors C1 and C2 are connected in parallel at the input to suppress high-frequency noise;

[0218] The microprocessor dynamically modifies R f The resistance value can achieve linear adjustment of the gain range from 20dB to 60dB, which can meet the amplification requirements of signals with different sound pressure levels.

[0219] The third-order Butterworth filter is composed of ferrite core inductors L1 and L2 with Q value ≥ 50 and tantalum capacitors C3 and C4 with capacitance ranging from 0.1μF to 10μF. The cutoff frequency fc is dynamically adjusted through the microprocessor configuration register:

[0220]

[0221] The filter mode switching is achieved through a relay array, supporting three configurations: low-pass LPF, band-pass BPF, and high-pass HPF. Their transfer functions are:

[0222]

[0223] Ferrite core inductors provide ≥40dB of EMI attenuation in the 10kHz to 1GHz frequency band, and tantalum capacitors provide ≥60dB of ripple suppression in the DC to 1MHz frequency band.

[0224] The input is connected to the filter output via a high-speed SPI bus (clock frequency 4MHz), with a sampling rate of 3750SPS and an input range of ±2.048V. The integrated programmable gain amplifier (PGA) provides a gain of 1 to 128, and the actual input voltage is calculated using the following formula:

[0225]

[0226] The microprocessor triggers the conversion start signal (CS) of the ADS1115 through an interrupt. After the conversion is completed, the digital signal is read through the data ready (DRDY) pin to ensure that the timing synchronization error is ≤1μs.

[0227] In this embodiment, the microprocessor divides the Flash memory into three areas:

[0228] Algorithm code area: stores the fast Fourier transform core program and adaptive noise suppression algorithm;

[0229] Data buffer: temporarily stores sampling signals and intermediate calculation results;

[0230] Parameter configuration area: stores dynamic parameters such as filter cutoff frequency, gain value, etc.

[0231] The digital signal x[n] (n=0, 1, ..., N-1) output by the analog-to-digital converter is transferred to the microprocessor memory through the DMA channel, with a sampling rate of fs=3750SPS and a sampling number of N=1024;

[0232] The radix 2-time decimation FFT algorithm is used to calculate the N-point discrete Fourier transform. The formula is:

[0233]

[0234] Wherein, x(n) is the input time domain signal, X(k) is the frequency domain spectrum, and N=1024 (number of sampling points).

[0235] According to the present invention, a carbon nanotube-based acoustic wave sensing device is provided, which uses FPU to accelerate complex multiplication, and the single-point FFT operation time is ≤10μs;

[0236] Spectral leakage is suppressed by the window function (Hanning window), and the window function coefficient w(n) is:

[0237]

[0238] The minimum control recursive averaging (MCRA) algorithm is used to dynamically estimate the noise power spectrum P noise (k):

[0239] P noise (k) = αP noise (k-1)+(1-α)|X(k)| 2

[0240] Among them, α=0.95 is the smoothing coefficient.

[0241] Adjust the frequency domain gain G(k) according to the noise power to achieve spectral subtraction noise reduction:

[0242]

[0243] Among them, β=2.5 is the over-reduction factor, and γ=0.1 is the gain lower limit.

[0244] Determine the signal frequency f by using the FFT main peak detection algorithm peak :

[0245]

[0246] Among them, k max is the spectrum maximum index, f s =3750Hz is the sampling rate.

[0247] Amplitude A:

[0248]

[0249] Phase φ:

[0250]

[0251] Where N is the number of sampling points or signal length, X(k max ) is the frequency k max The discrete Fourier transform result at contains the real part Re and the imaginary part Im.

[0252] The calculation of the vibration amplitude of the vibrating membrane includes the following formula:

[0253]

[0254] P is the sound pressure and f is the frequency.

[0255] Among them, the resistance change of carbon nanotubes includes the following formula:

[0256] Mechanical strain:

[0257] Resistance change: ΔR = k·ε

[0258] Among them, the output voltage calculation includes the following formula

[0259] ΔV=I·ΔR

[0260] After zooming in: v out =G·ΔV G = constant

[0261] Among them, the bandpass filter removes high-frequency noise and low-frequency interference, retaining only the signal in the range of 20-500Hz. The filtering formula is:

[0262]

[0263] Among them, Fast Fourier Transform (FFT): extracts signal spectrum characteristics (such as main frequency and harmonics). The FFT formula is:

[0264]

[0265] X(n) is the time domain signal, and N is the number of sampling points.

[0266] In practical applications, this example demonstrates the design and fabrication of a carbon nanotube acoustic wave sensor. The device primarily consists of a front-end acoustic wave receiving and transmission module, a multi-walled carbon nanotube array sensing unit, a signal acquisition module, a signal processing module, an acoustic wave output port, a heat sink, and a power supply. By using multi-walled carbon nanotubes as the recognition module material and integrating scenario-specific algorithms into the back-end information processing module, the acoustic wave sensor's sensitivity, accuracy, and wide-frequency response are significantly improved.

[0267] Among them, the shell is designed by combining titanium alloy and polycarbonate (PC). The entire shell is made of titanium alloy, which has high strength, corrosion resistance and pressure resistance, adapts to the high-pressure environment of the deep sea, and provides good heat dissipation capabilities. Polycarbonate (PC) is used in the front sensor area to ensure that the sound wave signal is transmitted without attenuation, while having excellent impact resistance and weather resistance. The surface of the shell is treated with multiple layers, including anti-reflective coating, anti-static coating and anti-corrosion coating to improve the performance of the equipment. The shell adopts a modular design, which is easy to assemble, maintain and replace. The front-end module is responsible for sound wave reception and conduction, and the back-end module integrates the motherboard and other electronic components. The external shape is cylindrical, which optimizes the underwater fluid dynamics and reduces water flow resistance. The radiator and interface are installed in the left area, and the right side is the transparent part of the sound wave receiving device.

[0268] Among them, the internal space divisions of the carbon nanotube acoustic wave sensor device include: front-end area: installing vibration membranes and waveguide devices to ensure that the acoustic wave signal is efficiently transmitted to the carbon nanotube array unit; middle area: arranging multi-walled carbon nanotube arrays and their fixing devices, and using seismic isolation structure to avoid underwater vibration interference; back-end area: centrally arranging the main board, power module and signal processing module, reducing the weight of the equipment through compact design, and ensuring smooth thermal management channels.

[0269] Among them, the front-end vibration membrane of the front-end sound wave receiving and conducting module adopts polyimide (PI) film, which has the characteristics of high sensitivity, lightweight and high temperature resistance. The surface is treated with nano-coating to improve water resistance and corrosion resistance. It is located at the front end of the device, fixed inside the shell by mechanical fasteners or sealing rubber rings, and mechanically connected to the waveguide structure. It captures environmental sound waves, converts sound wave energy into mechanical vibrations, supports a wide frequency range, and provides efficient and accurate mechanical vibration signals for subsequent modules. The front-end waveguide module uses polytetrafluoroethylene (PTFE) or carbon fiber composite materials to ensure efficient transmission and stability of waveguides. The structure is a tubular or planar transmission channel, and the inner wall is polished to reduce signal attenuation. The mechanical vibration generated by the vibration membrane is efficiently transmitted to the multi-walled carbon nanotube array, optimizing the vibration path, reducing signal attenuation and reflection interference, and adapting to complex sound wave propagation environments.

[0270] The multi-walled carbon nanotube array sensing unit utilizes a multi-walled carbon nanotube (MWCNT) array, which boasts high mechanical strength and excellent electrical conductivity. Arranged in a regular, needle-like or tubular pattern, the arrays have diameters of 5-10 nm and lengths of 10-50 μm. The substrate is a single-crystal silicon or alumina ceramic, covered with a platinum conductive film. This converts the mechanical vibration signals transmitted by the waveguide structure into electrical signals, responding to a wide frequency range and providing highly sensitive signal conversion capabilities, ensuring high-fidelity signal output.

[0271] The signal acquisition module's metal ring, made of silver-plated aluminum alloy or gold-plated copper, features a ring-shaped design that uniformly collects the electrical signals generated by the carbon nanotube array. A secondary shielding layer utilizes aluminized polyester film (PET) and polyimide film to shield against external electromagnetic interference. Data calibration procedures and noise suppression algorithms are included to optimize signal acquisition and ensure signal purity. The signal generated by the carbon nanotube array is collected and evenly transmitted to the signal processing module, isolating it from external interference and ensuring signal integrity.

[0272] The signal processing module's hardware includes a low-noise operational amplifier (such as the TL082), multi-stage filters (low-pass, band-pass, and high-pass), a high-resolution analog-to-digital converter (such as the ADS1115), and an STM32 series microprocessor. The software includes signal enhancement algorithms, feature extraction algorithms (such as fast Fourier transform (FFT) and pattern recognition algorithms), and data output routines. This converts analog signals into digital signals, extracts feature information, and transmits the processed signals to the acoustic wave output port.

[0273] The acoustic wave output port includes a gold-plated copper pin and a nickel-plated USB port, a wireless module supporting Wi-Fi or Bluetooth communication, and a polycarbonate (PC) or stainless steel waterproof housing. The signal processing module outputs data to an external device (such as a computer or monitoring system) with bidirectional communication. The external device can adjust the sensor's operating parameters.

[0274] The power module includes a power management chip that supports low-power mode switching and overvoltage protection, a battery module with optional lithium batteries or external power supply, and an aluminum heat sink. It provides stable power to each module and dynamically adjusts power consumption to extend device operation time.

[0275] The heat dissipation device, which includes a graphene heat dissipation film, a waterproof epoxy resin sealing coating, silicone or fluororubber sealing rings, and a pressure-resistant polycarbonate (PC) waterproof casing, efficiently absorbs and dissipates heat generated during device operation, ensuring stability under high loads and extending the device's service life. The sealing coating and water-blocking components completely block water ingress, protecting the operating environment of the radiator and other internal components.

[0276] In this embodiment, the device manufacturing steps include:

[0277] Step 1: Equipment preparation

[0278] Modular separation of the shell: The shell is separated into two parts, the left module is used to install the signal processing module, power module, and signal output port; the right module is used to install the front-end sound wave receiving module, multi-walled carbon nanotube sensing unit, and heat dissipation device.

[0279] Check the module: Confirm whether the module has reserved interfaces and screw holes to ensure that all components can be fixed smoothly.

[0280] Material inspection: Confirm whether the shell material is intact and the surface is smooth; check whether the sealing components (such as silicone sealing rings) are complete to prevent the risk of water leakage during underwater operation.

[0281] Step 2: Installation of front-end sound wave receiving and transmission module

[0282] Install the front diaphragm: Fix the polyimide (PI) film to the front of the right module, ensuring that the diaphragm is close to the inside of the sound wave receiving window and fix the edge with the clamping frame.

[0283] Connecting the waveguide structure: Install the waveguide structure behind the vibration membrane to ensure precise fit with the vibration membrane and seamless connection to the multi-walled carbon nanotube array sensing unit.

[0284] Seal the sound wave receiving window: Install the silicone sealing ring around the vibration membrane and check whether the sealing ring fits the edge of the shell.

[0285] Step 3: Install the middle sensor module

[0286] Install the multi-walled carbon nanotube array sensing unit: Fix the sensing array on the supporting substrate at the rear end of the waveguide structure, and check whether the carbon nanotube array is arranged neatly and aligned with the output signal of the waveguide structure.

[0287] Fix the substrate support layer: Install the monocrystalline silicon substrate on the internal base of the module to ensure that the support layer is stable.

[0288] Step 4: Installation of back-end signal acquisition and processing module

[0289] Install the signal acquisition module: Place a metal ring around the periphery of the carbon nanotube array and secure it with a bracket. Install an auxiliary shielding layer to isolate the signal acquisition module from external electromagnetic interference. Use a flexible circuit (FPC) to connect the signal acquisition module to the signal processing module.

[0290] Install the signal processing module: Install the signal amplifier, filter, analog-to-digital converter (ADC), and microprocessor in the left module in sequence, and connect the signal processing module to other modules through a high-speed data bus (such as I2C or SPI).

[0291] Step 5: Installation of the sound wave output port

[0292] Fix the data interface: Install the USB interface or round output port on the left module housing and secure it with screws or clips to ensure that the interface is connected to the output end of the signal processing module.

[0293] Sealing interface components: Install a waterproof sealing ring or apply a sealing coating around the output port and check whether the interface is exposed from the housing.

[0294] Step 6: Install the power module

[0295] Power module fixing: Install the power module at the bottom of the left module and secure it with screws or clips. If portability is required, you can choose to install a lithium battery module.

[0296] Connect the power cables: Use the power bus to connect the power module to the signal processing module, multi-walled carbon nanotube array, and communication module. Ensure that all power connections have overvoltage protection devices.

[0297] Backup battery (optional): If portable application is required, a lithium battery module can be installed at the bottom of the left module and connected to a power management chip.

[0298] Step 7: Install the heat sink

[0299] Fix the heat sink: Install the high thermal conductivity aluminum alloy heat sink in the right module, making close contact with the multi-walled carbon nanotube array substrate and signal processing module.

[0300] Install waterproof heat shield: Cover the outside of the heat sink with a polycarbonate or fiberglass water shield, making sure the water shield fits tightly against the shell.

[0301] Step 8: Assemble and fix the shell

[0302] Module connection: Align the left module with the right module and secure them with screws or clips. Check that all cables are neatly arranged and not disturbed or loose.

[0303] Sealing test: Install silicone sealing rings or apply sealant at the shell joints to perform a waterproof test to ensure that the device has no leakage risk in an underwater environment.

[0304] System debugging and calibration

[0305] Sound wave receiving module: Checks the sensitivity of the diaphragm to ensure it can capture underwater sound waves.

[0306] Sensing unit: Tests the response speed and sensitivity of the carbon nanotube array.

[0307] Signal processing module: Verify signal amplification, filtering and digitization functions.

[0308] Communication module: Check whether the data transmission with external devices is normal.

[0309] Heat dissipation module: Perform continuous high-speed gesture changes (simulating continuous device rotation) to observe whether the radiator can dissipate heat, whether it can operate normally in water, and whether it is waterproof.

[0310] Overall debugging: Test the performance of the equipment under different water depths, temperatures and noise conditions, adjust algorithm parameters, and optimize the accuracy and real-time performance of signal processing.

[0311] Through the above design and production process, the carbon nanotube acoustic wave sensing device can achieve efficient and accurate acoustic wave sensing and is suitable for various complex underwater environments.

[0312] Underwater low-frequency test example

[0313] 1. Purpose of the experiment

[0314] Verify the detection performance of the carbon nanotube-based acoustic wave sensing device in an underwater low-frequency acoustic wave environment, including sensitivity, frequency response, signal-to-noise ratio, and accuracy of data output.

[0315] 2. Experimental Equipment

[0316] Equipment under test: A carbon nanotube-based acoustic wave sensing device, including a vibration membrane, a waveguide module, a carbon nanotube array, a signal acquisition module, a signal processing module, and a data output port.

[0317] Sound wave source: low-frequency signal generator (20Hz-500Hz, sine wave output).

[0318] Underwater speaker (sound pressure range: 50Pa-200Pa).

[0319] Experimental environment:

[0320] Test water tank: size 2m×1m×1m, water depth 0.8m.

[0321] Constant temperature system: water temperature is adjustable (10℃, 20℃, 30℃).

[0322] Noise-proof device: isolates external sound wave interference.

[0323] Data acquisition and analysis equipment:

[0324] Oscilloscope: Real-time waveform monitoring.

[0325] Data acquisition card: connect to the sensor output port to collect signals.

[0326] Data processing software: Python software is used for spectrum analysis and signal processing.

[0327] 3. Experimental Procedure

[0328] Step 1: Prepare the experimental environment

[0329] Fill the test tank with clean water to ensure that the water depth reaches 0.8m.

[0330] Adjust the constant temperature system and set the water temperature to 20℃.

[0331] Enable the noise reduction device to ensure that the background noise is lower than 30dB during the experiment.

[0332] Fix the underwater speaker in the center of the tank and calibrate the position.

[0333] Step 2: Sensor installation

[0334] Housing inspection: Check the sealing of the sensor device to ensure that water cannot enter.

[0335] Installation location: Fix the sensor in front of the speaker at a distance of 1m, ensuring that the diaphragm faces the speaker and that the sound wave transmission path is unobstructed.

[0336] Connect the circuit: Use sealed cables to connect the sensor to the data acquisition device.

[0337] Check the connection status between the signal acquisition module and the processing module to ensure that the power is normal.

[0338] Step 3: Test signal input

[0339] Use a signal generator to set up a test signal:

[0340] Frequency: Set to 20Hz, 50Hz, 100Hz, 250Hz and 500Hz respectively.

[0341] Sound pressure intensity: set to 50Pa, 100Pa and 200Pa respectively.

[0342] The duration of each test signal is 10 seconds.

[0343] After the test is completed, move the speaker to 0.5m and 2m positions and repeat the test.

[0344] Step 4: Signal Acquisition and Analysis

[0345] Activate the sensor and turn on the data acquisition device.

[0346] Real-time monitoring: Use an oscilloscope to observe the voltage waveform output by the sensor and record the peak voltage.

[0347] Signal storage: Use a data acquisition card to transmit the output signal to a computer and store it as a digital signal.

[0348] Spectrum analysis: Use Fast Fourier Transform (FFT) to analyze the signal and extract the main frequency, amplitude and harmonic components.

[0349] Step 5: Repeat the test

[0350] Adjust the water temperature to 10℃ and 30℃, and repeat steps 3 and 4 respectively.

[0351] The test was repeated with the background noise increased to 60dB to verify the device's anti-interference capability.

[0352] Data recording and analysis

[0353] Record and graph the following data:

[0354] like Figures 4 and 5 As shown, Figure 4 Frequency vs. output voltage: The output voltage corresponding to different frequencies under each set of sound pressure and distance conditions. Figure 5 Signal-to-noise ratio vs. sound pressure: the signal-to-noise ratio at different sound pressures under each set of frequency conditions. Figure 6 Frequency response range: the output voltage changes with frequency.

[0355] Among them, frequencies: test frequency range; pressures: sound pressure level; output_voltage: output voltage of the sensor at different frequencies and sound pressures; snr: signal-to-noise ratio under different frequencies and sound pressure conditions.

[0356] Verification

[0357] Sensitivity test: Calculate whether the sensitivity meets the design value based on S=ΔV / P.

[0358] Signal-to-noise ratio analysis: Check whether the signal-to-noise ratio remains above 60dB under different sound pressure conditions.

[0359] Frequency response range: Verify whether the sensor can cover the designed frequency range (20Hz-500Hz).

[0360] Experimental results

[0361] Real-time: After the test signal is input, the device completes signal acquisition and output within 50ms.

[0362] High precision: main frequency deviation is less than 0.1Hz, amplitude error is less than 5%.

[0363] Environmental adaptability: It can operate stably under different temperature and background noise conditions.

[0364] This embodiment verifies the feasibility of the invention through specific parameters and experimental data. In actual production, the material and algorithm parameters can be adjusted according to the application scenario.

[0365] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0366] The above embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and replacements made by technicians in this field on the basis of the present invention fall within the scope of protection required by the present invention.

Claims

1. A carbon nanotube-based acoustic wave sensing device, characterized in that: include: A front-end sound wave receiving and conducting module for receiving sound waves and converting them into mechanical vibrations; wherein the front-end sound wave receiving and conducting module includes a front-end diaphragm located at the front end of the device, directly facing the direction of sound wave input, for capturing sound waves and converting them into mechanical vibrations; a transmission waveguide structure, arranged behind the front-end diaphragm, for efficiently transmitting mechanical vibrations to the multi-walled carbon nanotube array sensing unit; A multi-walled carbon nanotube array sensing unit, connected to the front-end sound wave receiving and conducting module, for converting mechanical vibrations into electrical signals; a signal acquisition module, arranged around the multi-walled carbon nanotube array sensing unit, and configured to acquire the electrical signal; A signal processing module, connected to the signal acquisition module, for amplifying, filtering and digitizing the collected electrical signals; The sound wave output port is connected to the signal processing module and is used to output the processed signal to an external device.

2. The device according to claim 1, characterized in that The multi-walled carbon nanotube array sensing unit comprises: Multi-walled carbon nanotube arrays, composed of multiple multi-walled carbon nanotubes, are used to convert mechanical vibrations into electrical signals; The base support layer is used to provide mechanical support to ensure that the vibration signal is evenly transmitted to the multi-walled carbon nanotube array. The multi-walled carbon nanotubes are grown on a substrate support layer by chemical vapor deposition to form a regularly arranged array, wherein each multi-walled carbon nanotube has a diameter in the range of 10 to 50 nanometers and a length in the range of 10 to 100 micrometers, and the total area of ​​the array matches the effective receiving area of ​​the front diaphragm; Among them, the base support layer is made of single crystal silicon, quartz glass or alumina ceramic, and its surface is covered with a conductive film. The conductive film is selected from platinum, gold or other highly conductive materials to enhance the electrical connection between the multi-walled carbon nanotube array and the signal acquisition circuit.

3. The device according to claim 1, characterized in that The signal acquisition module includes: a metal ring, arranged around the multi-walled carbon nanotube array sensing unit, for uniformly collecting the electrical signal; an auxiliary shielding layer, covering the outside of the metal ring and used to prevent electromagnetic interference from entering the signal path; Among them, the auxiliary shielding layer is composed of a composite of aluminum-plated polyester film PET and polyimide film, in which the aluminum-plated layer faces the external electromagnetic interference source, and the polyimide layer adheres to the surface of the metal ring; the shielding layer is tightly combined with the metal ring through conductive glue or mechanical fasteners to form a continuous electromagnetic shielding structure.

4. The device according to claim 1, characterized in that The signal processing module includes: A signal amplifier uses a low-noise operational amplifier chip, paired with metal film resistors and ceramic capacitors to form a differential input structure. Its gain is adjustable from 20dB to 60dB, and is used to amplify weak electrical signals to a detectable range to meet the amplification requirements of signals with different sound pressure levels. The filter is a multi-stage passive filter network composed of ferrite core inductors and tantalum capacitors, including low-pass, band-pass, and high-pass switchable modules. The cutoff frequency is dynamically configured by a microprocessor, covering the frequency range of 20Hz to 500kHz, and is used to remove noise and useless frequency bands; The analog-to-digital converter uses the ADS1115 chip with 16-bit resolution and integrates a programmable gain amplifier (PGA). It is connected to the filter output via a high-speed SPI bus to convert the analog signal into a digital signal. The microprocessor is used to execute built-in algorithms to perform real-time processing and feature extraction of digital signals. The microprocessor is based on the ARM Cortex-M architecture, has a built-in floating-point unit (FPU) and Flash memory, runs a fast Fourier transform algorithm and an adaptive noise suppression program, and extracts the frequency, amplitude, and phase characteristics of the sound wave signal in real time.

5. The device according to claim 1, characterized in that: When sound waves propagate through the underwater or air environment to the sensor's sound wave receiving module, the front-end diaphragm captures the sound wave signal and converts it into mechanical vibration. The mechanical vibration of the diaphragm is transmitted to the multi-walled carbon nanotube array sensing unit through a waveguide structure made of polytetrafluoroethylene (PTFE) or carbon fiber composite material. The multi-walled carbon nanotube array responds to mechanical vibrations, and each carbon nanotube produces a piezoelectric effect and changes in conductivity due to deformation: Piezoelectric effect: deformation causes the charge to be redistributed at both ends of the carbon nanotube, generating a weak voltage; Conductivity change: Deformation changes the resistance or capacitance characteristics of carbon nanotubes, generating dynamic current signals; The generated weak electrical signals are evenly collected by the signal collection module.

6. The device according to claim 5, characterized in that: When a single carbon nanotube is subjected to axial stress σ, the piezoelectric charge Q generated at its two ends is proportional to the deformation, and the formula is: Q=d 33 ×F(F=σ×A) Among them, d 33 =2.5×10-12C / N is the piezoelectric coefficient, F is the force, and A=π(d / 2)2 is the cross-sectional area of ​​the carbon nanotube. The charge accumulated at both ends forms an open circuit voltage V piezo , the formula is: Among them, C tube is the static capacitance of a single carbon nanotube, ∈0 = 8.85 × 10-12 F / m is the vacuum dielectric constant; When the carbon nanotube is subjected to radial pressure P, resulting in a diameter contraction of Δd, the relationship between its resistance change rate ΔR / R0 and strain ∈=Δd / d is: Where ν is Poisson's ratio and Δρ / ρ0 is the resistivity change.

7. The device according to claim 6, characterized in that: At a constant bias voltage V bias Under this condition, the current change ΔI is: Where ω is the signal angular frequency, τ = RC is the time constant, R is the contact resistance, and C is the parasitic capacitance; The inner diameter of the metal ring electrode matches the outer diameter of the carbon nanotube array, with a spacing of ≤0.1mm; A single carbon nanotube is equivalent to a voltage source V piezo With resistor R tube When connected in series, the total output voltage of the array is: Where n is the number of effective carbon nanotubes in the multi-walled carbon nanotube array, R load is the load resistance, R tube is the resistance of a single carbon nanotube.

8. The device according to claim 4, characterized in that: A low-noise operational amplifier chip is used, and its input end uses metal film resistors R1 and R2 to construct a differential input circuit. The resistance values ​​meet the following matching conditions: R1=R2=10kΩ±1%,R f =100kΩ±1% Among them, R f is the feedback resistor, and the gain G is dynamically adjusted by the following formula: Ceramic capacitors C1 and C2 are connected in parallel at the input to suppress high-frequency noise; The microprocessor dynamically modifies R f The resistance value can achieve linear adjustment of the gain range from 20dB to 60dB, which can meet the amplification requirements of signals with different sound pressure levels. The third-order Butterworth filter is composed of ferrite core inductors L1 and L2 and tantalum capacitors C3 and C4. The cutoff frequency fc is dynamically adjusted through the microprocessor configuration register: The filter mode switching is achieved through a relay array, supporting three configurations: low-pass LPF, band-pass BPF, and high-pass HPF. Their transfer functions are: Ferrite core inductors provide ≥40dB of EMI attenuation in the 10kHz to 1GHz frequency band, and tantalum capacitors provide ≥60dB of ripple suppression in the DC to 1MHz frequency band.

9. The device according to claim 8, characterized in that: The microprocessor divides the Flash memory into three areas: Algorithm code area: stores the fast Fourier transform core program and adaptive noise suppression algorithm; Data buffer: temporarily stores sampling signals and intermediate calculation results; Parameter configuration area: stores dynamic parameters such as filter cutoff frequency, gain value, etc. The digital signal x[n] (n=0, 1, ..., N-1) output by the analog-to-digital converter is transferred to the microprocessor memory through the DMA channel; The radix 2-time decimation FFT algorithm is used to calculate the N-point discrete Fourier transform. The formula is: Where x(n) is the input time domain signal, X(k) is the frequency domain spectrum, and N is the number of sampling points.

10. The method according to claim 9, characterized in that: Utilize FPU to accelerate complex multiplication, with single-point FFT operation time ≤ 10μs; Spectral leakage is suppressed by the window function, and the window function coefficient w(n) is: The noise power spectrum P is dynamically estimated using the minimum control recursive averaging algorithm. noise (k): P noise (k)=αP noise (k-1)+(1-α)|X(k)| 2 Among them, α is the smoothing coefficient; Adjust the frequency domain gain G(k) according to the noise power to achieve spectral subtraction noise reduction: Among them, β is the over-reduction factor, γ is the gain lower limit; Determine the signal frequency f by using the FFT main peak detection algorithm peak : Among them, k max is the spectrum maximum index, f s =3750Hz is the sampling rate. Amplitude A: Phase φ: Where N is the number of sampling points or signal length, X(k max ) is the frequency k max The discrete Fourier transform result at contains the real part Re and the imaginary part Im.

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