WO2021114906A1 - 一种二维MXene基声音探测器及其制备方法和应用 - Google Patents

一种二维MXene基声音探测器及其制备方法和应用 Download PDF

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WO2021114906A1
WO2021114906A1 PCT/CN2020/123909 CN2020123909W WO2021114906A1 WO 2021114906 A1 WO2021114906 A1 WO 2021114906A1 CN 2020123909 W CN2020123909 W CN 2020123909W WO 2021114906 A1 WO2021114906 A1 WO 2021114906A1
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mxene
sound detector
dimensional
based sound
film
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PCT/CN2020/123909
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English (en)
French (fr)
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温博
丁惠君
张家宜
靳雨锟
梁维源
范涛健
康建龙
黄浩
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深圳瀚光科技有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H11/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties
    • G01H11/06—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/02—Prostheses implantable into the body
    • A61F2/20—Epiglottis; Larynxes; Tracheae combined with larynxes or for use therewith

Definitions

  • the present invention claims the priority of an earlier application with the title of “A two-dimensional MXene-based sound detector and its preparation method and application” filed on December 13, 2019.
  • the content of the above-mentioned earlier application is incorporated in the application number 201911281660.0
  • the method is incorporated into this text.
  • the invention relates to the technical field of sound detection devices, in particular to a two-dimensional MXene-based sound detector.
  • the invention also relates to an artificial electronic throat.
  • the invention also relates to a preparation method and application of a two-dimensional MXene-based sound detector.
  • the throat is a unique biological structure that is used to produce sounds and facilitate communication with each other.
  • Laryngeal diseases often lead to communication difficulties, which is manifested in that most patients with the disease cannot speak accurately through the throat.
  • there are many solutions to help patients to vocalize such as common esophageal vocalization and artificial electronic throat.
  • the sound of the esophagus depends on the vibration of the esophagus, which is closer to the sound produced by the normal larynx.
  • esophageal vocalization needs to be trained by various methods. The training cycle is long and the process is difficult. Even after a lot of training, more than 60% of patients still cannot learn esophageal vocalization.
  • the artificial electronic throat is mainly realized by a sound detector, which is specifically expressed as: the sound detector converts the biological vibration (such as throat vibration) signal generated by the sound into an electric signal, and the electric signal is analyzed and amplified, and the output is analyzed and amplified. Finally, the electrical signal is output to speaker equipment such as a horn to emit a sound that simulates the sound of the human throat.
  • the sound detector converts the biological vibration (such as throat vibration) signal generated by the sound into an electric signal, and the electric signal is analyzed and amplified, and the output is analyzed and amplified. Finally, the electrical signal is output to speaker equipment such as a horn to emit a sound that simulates the sound of the human throat.
  • traditional artificial electronic throats also have limited analytical functions. One is difficult to detect and distinguish biological vibration signals, and the two cannot accurately simulate the sound signals corresponding to biological vibrations.
  • the present invention provides a two-dimensional MXene-based sound detector.
  • the two-dimensional MXene-based sound detector utilizes the superior electrical and mechanical properties of MXene material, can efficiently detect and distinguish acoustic vibrations, and generate vibrations based on vibrations. Corresponding electrical signals to solve the problems of low detection limit and low resolution of existing sound detectors.
  • the present invention provides a two-dimensional MXene-based sound detector, including a base layer, an MXene film, an electrode, and a coating layer.
  • the base layer and the coating layer are combined to form a sealed container for accommodating the MXene film.
  • the electrodes include a pair, both of which are in contact with the MXene film, and the pair of electrodes are electrically connected through the MXene film.
  • the pair of electrodes are respectively arranged on both sides of the MXene film, and the MXene film and the pair of electrodes are built in a sealed accommodating cavity.
  • the base layer is a PDMS base layer
  • the coating layer is a PDMS coating layer
  • the electrode is electrically connected to the lead, the electrode is built in the sealed accommodating cavity, and the lead passes through the sealed accommodating cavity.
  • the material of the electrode includes at least one of chromium and gold; the thickness of the electrode is 25 nm to 90 nm.
  • the bias power supply further includes a bias power supply, both ends of the bias power supply are electrically connected to a pair of electrodes, and the bias power supply is used to provide a bias voltage to the MXene film.
  • a digital multimeter is further included.
  • the digital multimeter is electrically connected to a pair of electrodes, and the digital multimeter is used to detect the resistance value of the MXene film.
  • it further includes a deep learning network, the pair of electrodes are signal-connected to the deep learning network, and the deep learning network is used to detect changes in the resistance value of the MXene film.
  • the deep learning network is a SR-CNN (Syllable Recognition Convolutional Neural Network) network.
  • SR-CNN Session Control Network
  • the deep learning network sequentially includes a first convolutional layer, a first pooling layer, a second convolutional layer, a second pooling layer, a third convolutional layer, a fourth convolutional layer, and a fifth convolutional layer.
  • the two-dimensional MXene-based sound detector described in the first aspect of the present invention In the process of external acoustic vibration, the MXene film can vibrate and produce morphological bending. The nanosheets of the MXene film slide relatively and generate cracks or gaps, resulting in a change in the contact area between the nanosheets. When sound signals of different amplitudes and frequencies are applied to the MXene film, the contact resistance between the nanosheets of the MXene film is different, which is finally manifested as the total resistance change of the two-dimensional MXene-based sound detector.
  • the two-dimensional MXene-based sound detector When the external bias voltage is applied, as the total resistance of the two-dimensional MXene-based sound detector changes, it can be used to detect the electrical signal corresponding to the sound signal by detecting the change of the voltage or current signal between the two electrodes.
  • the two-dimensional MXene-based sound detector utilizes the superior electrical and mechanical properties of MXene material, that is, when acoustic signals of different amplitudes or frequencies are applied to the MXene film, it can bring about the difference in the total resistance of the MXene film and the frequency to achieve efficient detection. And distinguish the sound wave signal, and generate the corresponding electric signal function based on the sound wave signal.
  • the present invention provides an artificial electronic throat, including the two-dimensional MXene-based sound detector described in any one of the above and a sound generating device, the two-dimensional MXene-based sound detector is used to detect vibrations and generate electrical signals, The sound generating device is used for converting electrical signals into terminal sound waves.
  • the artificial electronic throat according to the second aspect of the present invention includes a two-dimensional MXene-based sound detector and a sound generating device.
  • the two-dimensional MXene-based sound detector realizes the function of efficiently detecting and distinguishing sound wave signals, and generating corresponding electric signals based on the sound wave signals .
  • the sound device converts the generated electric signal into a terminal sound wave, simulates the initial sound wave and emits external sound.
  • the artificial electronic throat can efficiently analyze effective initial sound waves, and effectively distinguish the frequency and amplitude changes between different initial sound wave vibrations, so as to realize high-resolution detection of initial sound waves.
  • the present invention provides a method for preparing a two-dimensional MXene-based sound detector, which includes the following steps:
  • a pair of electrodes are arranged on the MXene film, and the pair of electrodes are electrically connected through the MXene film;
  • a coating layer is provided, and the coating layer and the base layer are combined to form a sealed accommodating cavity for accommodating the MXene film, to obtain a two-dimensional MXene-based sound detector.
  • the preparation process of the MXene film includes the following steps:
  • the Ti 3 AlC 2 powder is 400-600 mesh; more preferably, the Ti 3 AlC 2 powder is 500 mesh.
  • the mass fraction of the hydrofluoric acid is 35%-50%; more preferably, the mass fraction of the hydrofluoric acid is 40%.
  • the mass-volume ratio of the Ti 3 AlC 2 powder and the hydrofluoric acid is 1: 50-150; more preferably, the mass-volume ratio of the Ti 3 AlC 2 powder and the hydrofluoric acid is 1:90 More preferably, the Ti 3 AlC 2 powder is 0.1 g, and the mass volume of the hydrofluoric acid is 9 ml.
  • the ambient temperature of the water bath is 43-46°C; more preferably, the ambient temperature of the water bath is 45°C.
  • the centrifugal speed is 2000-5000 r/min
  • the centrifugal time is 5-20 min
  • the centrifugal operation is repeated 3-8 times.
  • the centrifugation speed is 3500 r/min
  • the centrifugation time is 10 min
  • the centrifugation operation is repeated 6 times.
  • the pH is adjusted to 6.5-7.5; more preferably, the pH is adjusted to 6.5-7.
  • the base layer is a PDMS base layer
  • the coating layer is a PDMS coating layer
  • the solution containing MXene flakes is vacuum filtered, wherein the volume of the solution containing MXene flakes is 10-50 ml, and the pore size of the vacuum filtration membrane is 0.1-0.45 ⁇ m. More preferably, the volume of the solution containing MXene flakes is 30 ml, and the pore size of the vacuum filtration membrane is 0.22 ⁇ m.
  • the prepared PDMS is drip-coated on the mold, and the PDMS base layer is prepared by spin coating;
  • the prepared PDMS is dropped on the top, spin-coated to prepare the PDMS coating layer, and vacuum dried to prepare a two-dimensional MXene-based sound detector.
  • the PDMS A liquid and the B liquid are configured in a ratio of 10:1, and 2-5 ml of the configured PDMS is spin-coated on the mold to form the PDMS base layer.
  • the spin coating process includes low-speed spin-coating and high-speed spin-coating, the low-speed spin-coating is 200-500 r/min for 5-20 s, and the high-speed spin-coating is 1000-3000 r/min for 20-60 s. More preferably, the low-speed spin coating is 300 r/min for 10 s, and the high-speed spin coating is 2000 r/min for 30 s.
  • the vacuum drying is drying at 60-120°C for 0.5-2 hours; more preferably, the vacuum drying is drying at 80°C for 1 hour.
  • the conductive silver glue is placed in a room temperature and ventilated environment to dry naturally for 0.5 to 2 hours; more preferably, the conductive silver glue is placed in a room temperature and ventilated environment to dry naturally for 1 hour.
  • the two-dimensional MXene-based sound detector prepared by the preparation method of the two-dimensional MXene-based sound detector of the present invention has the advantages of high initial sound wave resolution and high detection limit, and can efficiently analyze initial sound waves of different amplitudes or frequencies.
  • the preparation method of the two-dimensional MXene-based sound detector has the advantages of relatively simple manufacturing process, relatively mature technology, low cost, stable performance of the two-dimensional MXene-based sound detector, and easy realization of large-scale mass production.
  • the present invention provides an application of the above-mentioned two-dimensional MXene-based sound detector in an artificial electronic throat.
  • the throat part produces sound and vibrates.
  • the vibration makes the two-dimensional MXene-based sound detector bend and change, and the internal resistance of the two-dimensional MXene-based sound detector changes.
  • the signal acquisition device collects the resistance change signal between a pair of electrodes to generate an electrical signal .
  • the signal acquisition device is a digital multimeter.
  • the signal acquisition device is a deep learning network.
  • the deep learning network collects the resistance value change signal, it filters out the interference signal and synthesizes the sound electrical signal corresponding to the vibration of the throat.
  • the sound electrical signal is used to output the parsed analog initial sound wave to make external sound, that is, the terminal sound wave .
  • the sound electric signal is connected to a speaker, and the sound electric signal controls the speaker to emit a terminal sound wave and emit sound to the outside.
  • the application of the two-dimensional MXene-based sound detector of the present invention on the artificial electronic throat has the advantages of high initial acoustic wave resolution, high detection limit, etc., and can efficiently analyze different amplitudes or frequencies.
  • the artificial electronic throat further sends out a terminal sound wave based on the detected sound wave electric signal, and realizes the conversion process from the initial sound wave to the electric signal to the terminal sound wave.
  • the artificial electronic throat prepared by the high-performance two-dimensional MXene-based sound detector can help people with speech disorders to effectively vocalize and correctly express the meaning contained in their initial sound waves.
  • FIG. 1 is a schematic structural diagram of a two-dimensional MXene-based sound detector provided by an embodiment of the present invention.
  • Fig. 2 is a schematic structural diagram of an SR-CNN network provided by another embodiment of the present invention.
  • Figure 3-a is the SEM spectrum of Ti 3 AlC 2 powder;
  • Figure 3-b is the SEM spectrum of the MXene film prepared after Ti 3 AlC 2 powder is etched;
  • Figure 3-c is the MXene thin film Scanning electron microscope spectra;
  • Figure 3-d shows the XRD spectra of Ti 3 AlC 2 powder and MXene film.
  • Figure 4 is the test result of the two-dimensional MXene-based sound detector made in Example 1 on the speaker sound
  • Figure 4-a is the test result of the two-dimensional MXene-based sound detector on the initial sound waves of different frequencies (the first peak From top to bottom: 250Hz, 100Hz, 300Hz, 400Hz, 200Hz, 350Hz, 500Hz, 150Hz, 50Hz; from top to bottom at the second peak: 250Hz, 100Hz, 300Hz, 400Hz, 200Hz, 350Hz, 500Hz , 150Hz, 50Hz; the third peak is 250Hz, 100Hz, 300Hz, 200Hz, 400Hz, 350Hz, 500Hz, 150Hz, 50Hz from top to bottom; the fourth peak is 250Hz, 100Hz from top to bottom , 300Hz, 200Hz, 400Hz, 350Hz, 500Hz, 150Hz, 50Hz; the fifth peak from top to bottom is: 250Hz, 100Hz, 300Hz, 200Hz, 400Hz, 350Hz, 500Hz, 150Hz, 50Hz); Figure 4-b The test results of the two-dimensional MXene-based
  • Figure 5 is the test result of the two-dimensional MXene-based sound detector made in Example 1 on the throat vocalization
  • Figure 5-a is the test result of the two-dimensional MXene-based sound detector on the pronunciation of different words (from left to right) Are “up”, “down”, “left”, “right”, “I”, “you”)
  • 5-b is the test result of the two-dimensional MXene-based sound detector for repeated pronunciation of the same word
  • 5-c is The test results of the two-dimensional MXene-based sound detector for the pronunciation of different tones (the first and second peaks on the left are " ⁇ ", and the third and fourth peaks are "ó").
  • Figure 6 is a flow chart of the deep learning network combined with the two-dimensional MXene-based sound detector for testing.
  • FIG. 1 is a two-dimensional MXene-based sound detector provided by an embodiment of the present invention.
  • the two-dimensional MXene-based sound detector includes a base layer 10, an MXene film 20, an electrode 30, and a coating layer 40.
  • the base layer 10 is arranged at the bottom of the two-dimensional MXene-based sound detector
  • the coating layer 40 is placed at the top of the two-dimensional MXene-based sound detector
  • the base layer 10 and the coating layer 40 are combined to accommodate the MXene.
  • the sealed accommodating cavity of the film, and the MXene film 20 and the electrode 30 are both accommodated in the sealed accommodating cavity, to protect the MXene film 20 from oxidation, and also to protect the electrode 30 from corrosion.
  • the electrode 30 includes a pair, and the pair of electrodes 30 are in contact with the MXene film 20.
  • the electrode 30 is directly connected to the MXene film 20 through a pad.
  • It can also be other connection methods, as long as the electrode 30 and the MXene film 20 can be electrically connected, so as to realize the electrical connection between the pair of electrodes 30 through the MXene film 20.
  • the two-dimensional MXene-based sound detector When using the two-dimensional MXene-based sound detector, first attach the two-dimensional MXene-based sound detector to the sound-producing part, for example, the throat part, and then connect a pair of electrodes 30 to a signal collection device, which is used for collection.
  • the resistance value change signal of the MXene thin film 20, or the signal collecting device is used to collect the voltage and current change signals caused by the resistance value change of the MXene thin film 20.
  • the MXene film 20 can vibrate accordingly and produce a morphological curvature.
  • the nanosheets of the MXene film 20 relatively slide and generate cracks or gaps, which causes the contact area between the nanosheets to change.
  • the two-dimensional MXene nano film has excellent electrical and mechanical properties, that is, when the initial sound waves of different amplitudes or frequencies are applied to the MXene film 20, it can bring about the difference in the total resistance of the MXene film 20 and the frequency of change, so as to achieve efficient detection and resolution of the initial Sound waves, and based on the initial sound waves to generate the corresponding electrical signal function.
  • the signal acquisition device can be selected as a digital multimeter.
  • the digital multimeter is electrically connected to a pair of electrodes 30.
  • the digital multimeter has its own power supply and can detect the resistance value of the MXene film. With the help of a computer system, the MXene can be effectively
  • the electrical signals such as the amplitude and frequency of the resistance value change of the film 20 are recorded and stored in the corresponding storage medium.
  • the electrical signal formed by the resistance value change can be input into the speaker to simulate the sound of the throat.
  • the base layer 10 is a PDMS base layer
  • the coating layer 30 is a PDMS coating layer.
  • the PDMS can effectively realize the placement and fixation of the MXene film 20, and the adhesion between PDMS can effectively form a sealed accommodating cavity to prevent air intrusion from oxidizing the MXene film 20 or corroding the electrode 30.
  • the electrode 30 is electrically connected to the lead 50, the electrode 30 is built in the sealed accommodating cavity, the lead 50 passes through the sealed accommodating cavity, and the electrode 30 is electrically connected to the external signal acquisition device through the lead 50.
  • the electrode 30 may also extend from the inside to the outside of the sealed accommodating cavity for electrical connection with an external signal acquisition device, which has the same effect.
  • the material of the electrode includes at least one of chromium and gold, for example, it may be a chromium electrode, a gold electrode, or a chromium or gold doped electrode.
  • the thickness of the electrode may be 25 nm to 90 nm, for example, it may be 25 nm, 40 nm, 55 nm, 70 nm, 80 nm, 90 nm.
  • the two-dimensional MXene-based sound detector also includes a bias power supply, the two ends of the bias power supply are electrically connected to a pair of electrodes 30, and the bias power supply is used to supply the MXene film 20 provides the bias voltage Vbia.
  • the signal acquisition device can be a voltmeter, ammeter, etc., which is used to detect the voltage or current change on the MXene film 20, and can also convert the initial sound wave into an electrical signal with a corresponding change in frequency and amplitude.
  • the signal acquisition device is preferably a deep learning network
  • a pair of electrodes 30 are signal-connected to the deep learning network
  • the deep learning network is used to detect changes in the resistance value of the MXene film 20.
  • the electrical signals obtained from the electrodes 30 can be processed into high-definition sound electrical signals, that is, the electrical signals corresponding to the initial sound waves can be parsed, and higher-definition sound electrical signals can be identified and fitted based on the electrical signals.
  • the amplitude and frequency of the electric sound signal are more specific and accurate, and the terminal sound wave converted from the electric sound signal is also more accurate and accurate.
  • the deep learning network is preferably an SR-CNN network.
  • the detected electrical signals are intelligently identified, optimized, and finally processed.
  • the SR-CNN network includes the first convolutional layer (convolutional layer composed of 16 convolution kernels, with a size of 32 ⁇ 1), and the first pooling layer (maximum pooling with a kernel size of 8 ⁇ 1).
  • Layer second convolution layer (convolution layer composed of 32 convolution kernels, size 32 ⁇ 1), second pooling layer (maximum pooling layer with kernel size 8 ⁇ 1), third Convolutional layer (convolutional layer composed of 64 convolution kernels, size 16 ⁇ 1), fourth convolutional layer (convolutional layer composed of 128 convolution kernels, size 8 ⁇ 1), fifth Convolutional layer (convolutional layer composed of 256 convolution kernels, size 4 ⁇ 1), third pooling layer (maximum pooling layer with kernel size 4 ⁇ 1), sixth convolutional layer (convolutional layer consisting of 512 A convolutional layer composed of two convolution kernels, with a size of 4 ⁇ 1), and a seventh convolutional layer (the convolutional layer is composed of 256 convolution kernels with a size
  • high-definition audio signals can be obtained, which are finally converted into high-definition and accurate sound vibrations, completing a series of processes from initial sound wave detection-conversion to electrical signals-intelligent identification of electrical signals, optimization-output of high-definition electrical signals, etc. It solves the defects of existing sound detectors such as inability to detect high-resolution, collect initial sound waves, low electrical signal resolution, and cannot be converted into high-definition terminal sound waves.
  • the SR-CNN network includes the first convolutional layer (convolutional layer composed of 16 convolution kernels, with a size of 32 ⁇ 1), and the first pooling layer (maximum pooling with a kernel size of 8 ⁇ 1).
  • Layer second convolution layer (convolution layer composed of 32 convolution kernels, size 32 ⁇ 1), second pooling layer (maximum pooling layer with kernel size 8 ⁇ 1), third Convolutional layer (convolutional layer composed of 64 convolution kernels, size 16 ⁇ 1), fourth convolutional layer (convolutional layer composed of 128 convolution kernels, size 8 ⁇ 1), fifth Convolutional layer (convolutional layer composed of 256 convolution kernels, size 4 ⁇ 1), third pooling layer (maximum pooling layer with kernel size 4 ⁇ 1), sixth convolutional layer (convolutional layer consisting of 512 Convolutional layer composed of four convolution kernels, the size is 4 ⁇ 1), the seventh convolutional layer (convolutional layer is composed of 1024 convolution kernels of size 4 ⁇ 1), the first neuron
  • An artificial electronic throat includes the two-dimensional MXene-based sound detector and the sound generating device in any one of Embodiment 1 or Embodiment 2.
  • the two-dimensional MXene-based sound detector is used to detect vibrations and generate electric signals
  • the sound device is used to convert the electric signals into initial sound waves.
  • the two-dimensional MXene-based sound detector realizes the function of efficiently detecting and distinguishing initial sound waves, and generating corresponding electric signals based on the detected initial sound waves.
  • the sound generating device converts the generated electric signals into terminal sound waves, simulating the initial sound waves and externally Vocalize.
  • the artificial electronic throat can efficiently analyze effective sound wave vibrations, and effectively distinguish the frequency and amplitude changes between different initial sound waves, so as to realize high-resolution detection of initial sound waves.
  • the preparation process of MXene film includes:
  • 0.1g of 500 mesh Ti 3 AlC 2 powder is placed in 9 ml of 40% hydrofluoric acid, and etched in a water bath at 45°C for 48 hours.
  • the etched reaction solution has a revolution of 3500r. Centrifuge for 10 min under the centrifugal condition of /min, repeat the above centrifugal operation 5-6 times, and then adjust the pH value to 6.5-7.0 to obtain the MXene solution.
  • the MXene solution was transferred to a constant temperature water bath for ultrasound (40KHz, ultrasound power 350W) for 1 hour to obtain a solution containing MXene flakes.
  • the solution containing MXene flakes is vacuum filtered to obtain an MXene film, wherein the volume of the solution containing MXene flakes is 30 ml, and the pore size of the vacuum filtration membrane is 0.22 ⁇ m.
  • the Ti 3 AlC 2 powder and the prepared MXene film were characterized separately. As shown in Figure 3-a and 3-b, the scanning electron microscopy (SEM) images of Ti 3 AlC 2 and MXene films, respectively. As shown in Figure 3-b, the MXene film is etched by hydrofluoric acid to form obvious multilayers. Structure, the multilayer structure is similar to an accordion.
  • Figure 3-c is the scanning electron micrograph of the MXene flakes. As shown in Figure 3-c, there are a small number of MXene flakes in the scanning range. The contour is displayed by a closed-loop dotted line.
  • Ti 3 AlC 2 powder and the prepared MXene film were subjected to X-ray diffraction (XRD) characterization, as shown in Figure 3-d, which are the X-ray diffraction patterns of the Ti 3 AlC 2 powder and the MXene film, respectively.
  • XRD X-ray diffraction
  • the first step is to configure the PDMS solution A and B solution in a ratio of 10:1 to prepare the configured PDMS; take 2-5ml of the configured PDMS and spin-coat on the mold to form the PDMS base layer.
  • the spin-coating process includes low-speed spin-coating and high-speed spin-coating, low-speed spin-coating is 300r/min spin-coating for 10s, and high-speed spin-coating is 2000r/min spin-coating for 30s.
  • the MXene film prepared in Example 4 was transferred to the PDMS base layer and dried under vacuum at 80° C. for 1 hour, so that the MXene film was firmly adhered to the PDMS base layer.
  • a pair of electrodes are set on the MXene film, and the pair of electrodes are electrically connected through the MXene film.
  • a pair of electrodes may be bonded to the MXene film through conductive glue, or the electrodes may be fixed on the MXene film through metal pads.
  • the conductive silver glue is preferably used for bonding, and then the electrode is drawn out through the conductive silver glue and the copper wire to realize the electrical connection between the electrode and the external signal collection device. After the electrode setting is completed, it is transferred to a room temperature and ventilated environment to dry naturally for 1 hour, and the conductive silver glue is cured.
  • the fourth step take 2-5ml of the prepared PDMS and drop it on the top of the detector, spin-coat to prepare the PDMS coating layer, and dry in vacuum to obtain a two-dimensional MXene-based sound detector.
  • the spin coating and drying process are the same as the first and second steps above.
  • An application of the two-dimensional MXene-based sound detector in Embodiment 1 or Embodiment 2 on an artificial electronic throat is specifically manifested in the preparation of an artificial electronic throat using a two-dimensional MXene-based sound detector.
  • the artificial electronic throat with a two-dimensional MXene-based sound detector has the advantages of high sound wave resolution and high detection limit. It can efficiently analyze initial sound waves of different amplitudes or frequencies, and further emit terminal sound waves based on the detected electrical signals to achieve The conversion process of sound wave-electric signal-terminal sound wave.
  • the artificial electronic throat prepared by the high-performance two-dimensional MXene-based sound detector can help people with speech disorders to speak effectively and correctly.
  • the application method of a two-dimensional MXene-based sound detector on an artificial electronic throat includes the following steps:
  • the two-dimensional MXene-based sound detector In the first step, attach the two-dimensional MXene-based sound detector to the human throat and electrically connect a pair of electrodes with the signal acquisition device.
  • the second step is to produce sound and vibration in the throat.
  • the vibration makes the two-dimensional MXene-based sound detector bend and change, and the internal resistance of the two-dimensional MXene-based sound detector changes.
  • the signal acquisition device collects the resistance change signal between a pair of electrodes. , Generate electrical signals.
  • the signal acquisition device is a digital multimeter, which has its own power supply and can test the resistance change of the MXene film. More preferably, the change in the resistance value of the MXene film displayed by the digital multimeter can be calculated by the computer system, and the electrical signal generated by the initial sound wave can be displayed through the display interface, including the amplitude and frequency of the resistance value oscillation.
  • a bias voltage is added to both ends of a pair of electrodes, and the voltage signal or current signal at both ends of the MXene film is collected by a signal acquisition device, so as to realize the conversion of the initial sound wave (vibration) generated by the throat sound into the corresponding electricity.
  • the "correspondence” here refers to the generation of a pulsed electrical signal corresponding to the change in the frequency and amplitude of the initial sound wave.
  • the signal acquisition device is a deep learning network. After the deep learning network collects the resistance value change signal, it filters out the interference signal and synthesizes the sound electrical signal corresponding to the vibration of the throat. The sound electrical signal is used to output the parsed terminal sound wave.
  • the deep learning network SR-CNN network As a preferred embodiment, the deep learning network SR-CNN network.
  • the sound electrical signal is connected to the speaker, and the sound electrical signal controls the speaker to emit terminal sound waves.
  • Effect embodiment 1 Detection of single audio signal with different frequency and different sound intensity
  • the two-dimensional MXene-based sound detector prepared in Example 1 is attached to the diaphragm of the speaker.
  • the single audio signal of 50Hz, 100Hz, 150Hz, 200Hz, 250Hz, 300Hz, 350Hz, 400Hz, 500Hz was played through computer control.
  • the playback time of each signal lasts for five seconds, and the interval between two playbacks is five seconds.
  • Test The result graph is shown in Figure 4-a (the abscissa represents the time, the ordinate represents the rate of change of resistance, ⁇ R represents the resistance change, R0 represents the initial resistance, and R represents the resistance after the change).
  • the two-dimensional MXene-based sound detector has different response results to sound signals of different frequencies, and can basically realize the detection of sound signals of different frequencies.
  • the resistance change rate of the device is the largest
  • the resistance change rate of the device is the smallest.
  • the sound frequency selected here is 100Hz.
  • five different sound intensities are selected in this experiment: 87dB, 94dB, 101dB, 106dB, 110dB.
  • the unit of sound intensity is dB, which is obtained by testing the microphone in a semi-anechoic room environment).
  • the experimental results are shown in Figure 4-b. It can be seen that the two-dimensional MXene-based sound detector prepared in Example 1 has different response results to different sound intensities. The increase in intensity increases, and the two show a positive correlation.
  • the MXene-based sound detector prepared in Example 1 can not only detect sound signals of different frequencies, but also detect different sound intensities at the same frequency.
  • the two-dimensional MXene-based sound detector prepared in Example 1 is attached to the hyoid bone of the human larynx.
  • the tester pronounced six different words, these six words are "up”, “down”, “left”, “right”, “I”, “you”, and then record the resistance change waveform, the results are as attached
  • Figure 5-a it can be seen from the figure that the two-dimensional MXene-based sound detector has different response results to different pronunciations, among which the “up” pronunciation causes the largest resistance change, which may be due to the test It is caused by the greater movement range of the larynx muscles when the reader reads "shang” compared to the other pronunciations. Then this effect embodiment also carried out a repeatability test.
  • the speaker plays audio signals.
  • the loudspeaker plays the long vowels and short vowels of "a" 750 times (total 1500 times), and 70% of the data in the 1500 test results, namely 1050 data, is used as the training set (including 525 long vowels and 525 Short vowels), and the remaining 450 data are used as the test set (including 225 long vowels and 225 short vowels).
  • Input the training set (1050 data) into our SR-CNN network The detailed structure of the network is shown in Figure 6. After the SR-CNN network is fully trained on the training set, the test set data (450 data ) Is input into the network to obtain the recognition results.
  • the recognition results are shown in Table 1.
  • the human larynx pronunciation The tester performs the pronunciation of the long vowel and short vowel of "a" 200 times (400 times in total), and uses 70% of the data of the 400 test results, namely 280 data, as the training set (including 140 long vowels). Sounds and 140 short vowels), and the remaining 120 data are used as the test set (including 60 long vowels and 60 short vowels).
  • the recognition results are shown in Table 2.
  • the analysis results show that one reason for the low detection accuracy of human throat signal data is that the sample size of the training data is too small. With the increase of the sample data amount, the deep learning algorithm will show more excellent recognition resolution. Another reason is that the initial sound wave data collected from the human throat has a greater degree of distortion than the initial sound wave data collected from the speaker. For example, in the process of vocalization in the human throat, there are other movements in the throat, such as swallowing. With the continuous increase of training data samples, the recognition efficiency of the deep learning network will be further improved, and the function of efficiently detecting the initial sound wave can be realized without additional assistance.

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Abstract

一种二维MXene基声音探测器，包括基底层（10）、MXene薄膜（20）、电极（30）及包覆层（40），基底层（10）与包覆层（40）合围成用于容置MXene薄膜（20）的密封容置腔；电极（30）包括一对，一对电极（30）均与MXene薄膜（20）接触，且一对电极（30）通过MXene薄膜（20）电导通。二维MXene基声音探测器利用了MXene材料优越的电学性能和力学性能，即不同振幅或者频率的初始声波作用于MXene薄膜（20）时，能够带来MXene薄膜（20）总电阻的大小及变化频率差异，实现高效检测和分辨初始声波并生成对应的电信号。一种人工电子喉咙、二维MXene基声音探测器的制备方法和二维MXene基声音探测器在人工电子喉咙上的应用。

Description

一种二维MXene基声音探测器及其制备方法和应用
本发明要求2019年12月13日递交的发明名称为“一种二维MXene基声音探测器及其制备方法和应用”的申请号201911281660.0的在先申请优先权，上述在先申请的内容以引入的方式并入本文本中。
技术领域
本发明涉及声音探测装置技术领域，具体涉及一种二维MXene基声音探测器，本发明还涉及一种人工电子喉咙，本发明还涉及一种二维MXene基声音探测器的制备方法及应用。
背景技术
喉咙(声带)是一种独一无二的生物结构，其用于发声且方便彼此沟通。喉部疾病往往导致沟通障碍，具体表现为大部分病患者无法通过喉咙准确发声。目前，存在许多解决方案用来帮助病人发声，例如常见的包括食道发声和人工电子喉咙。食道发声依靠食道的震动发出的声音，和正常喉发出的声音较为接近。但是，食道发声需要借助于各种方法进行训练，训练周期长、过程艰辛，即使经过大量的训练，仍有60％以上的患者学不会食道发声，这也成了食道发声无法克服的障碍。人工电子喉咙主要通过声音探测器实现，具体表现为：声音探测器将声音产生的生物振动(例如喉部振动)信号转换成电信号，通过对电信号进行解析和功放，输出解析和扩增后的声音电信号，最后将该电信号输出到喇叭等扬声器设备，发出模拟人体喉咙发声的声音。但是，传统的人工电子喉咙也存在解析功能有限，一者难以检测和分辨生物振动信号，二者也无法准确模拟出生物振对应的声音信号。
随着可穿戴电子产品、临床检测装置的快速发展，高灵敏度、电学性能和力学性能优越的柔性传感器材料越来越多的被人们发掘出来，研发出一种可穿戴的高分辨率声音探测器在技术上变得可能，也成为目前高性能医疗器械的研究热点。
发明内容
有鉴于此，本发明提供了一种二维MXene基声音探测器，该二维MXene基声音探测器利用了MXene材料优越的电学性能和力学性能，能够高效检测和分辨声波振动，并基于振动生成对应的电信号，以解决现有声音探测器存在的检出限低、分辨率低等问题。
第一方面，本发明提供了一种二维MXene基声音探测器，包括基底层、MXene薄膜、电极及包覆层，所述基底层与包覆层合围成用于容置MXene薄膜的密封容置腔；
所述电极包括一对，一对电极均与MXene薄膜接触，且一对电极通过MXene薄膜电导通。
本发明一具体实施方式中，所述一对电极分别设置于MXene薄膜的两侧，所述MXene薄膜及一对电极内置于密封容置腔。
优选地，所述基底层为PDMS基底层，所述包覆层为PDMS包覆层。
优选地，所述电极与引线电连接，所述电极内置于密封容置腔，所述引线穿过密封容 置腔。
优选地，所述电极的材质包括铬和金中的至少一种；所述电极的厚度为25nm～90nm。
优选地，还包括偏压电源，所述偏压电源两端分别与一对电极电连接，且所述偏压电源用于给MXene薄膜提供偏压。
本发明一具体实施方式中，还包括数字万用表，所述数字万用表分别与一对电极电连接，且所述数字万用表用于检测MXene薄膜的电阻值。
本发明另一具体实施方式中，还包括深度学习网络，所述一对电极与深度学习网络信号连接，且所述深度学习网络用于检测所述MXene薄膜的电阻值变化。
优选地，所述深度学习网络为SR-CNN(Syllable Recognition Convolutional Neural Network)网络。
优选地，所述深度学习网络依次包括第一卷积层、第一池化层、第二卷积层、第二池化层、第三卷积层、第四卷积层、第五卷积层、第三池化层、第六卷积层、第七卷积层。
本发明第一方面所述的二维MXene基声音探测器。外部发声振动过程中，MXene薄膜能够随之而振动并产生形态弯曲，MXene薄膜的各纳米片层之间相对滑动并产生裂缝或者间隙，导致各纳米片层间的接触面积变化。不同振幅和频率的声音信号作用于MXene薄膜时，MXene薄膜的的各纳米片之间的接触电阻不同，最终表现为二维MXene基声音探测器的总电阻变化。当通过外置偏压时，随着二维MXene基声音探测器的总电阻变化，通过检测两电极之间电压或者电流信号的变化，能够用来检测声音信号对应的电信号。该二维MXene基声音探测器利用了MXene材料优越的电学性能和力学性能，即不同振幅或者频率的声波信号作用于MXene薄膜时，能够带来MXene薄膜总电阻的大小及频率差异，实现高效检测和分辨声波信号，并基于声波信号生成对应的电信号的功能。
第二方面，本发明提供了一种人工电子喉咙，包括上述任一项所述的二维MXene基声音探测器及发声装置，所述二维MXene基声音探测器用于探测振动并生成电信号，所述发声装置用于将电信号转换成终端声波。
本发明第二方面所述的人工电子喉咙，包括二维MXene基声音探测器及发声装置，二维MXene基声音探测器实现高效检测和分辨声波信号，并基于声波信号生成对应的电信号的功能，发声装置基于生成的电信号转换成终端声波，模拟出初始声波并对外发声。该人工电子喉咙能够高效解析出有效的初始声波，且有效辨析出不同初始声波振动之间的频率及振幅变化，实现高分辨率探测初始声波。
第三方面，本发明提供了一种二维MXene基声音探测器的制备方法，包括以下步骤：
提供基底层，在所述基底层上设置MXene薄膜；
在所述MXene薄膜上设置一对电极，且一对电极通过MXene薄膜电导通；
设置包覆层，所述包覆层与基底层合围成用于容置MXene薄膜的密封容置腔，制得二维MXene基声音探测器。
本发明一具体实施方式中，所述MXene薄膜的制备过程包括以下步骤：
取Ti 3AlC 2粉末置于氢氟酸中，在42～48℃的水浴环境下刻蚀30～72小时，离心、调 节pH后，得到MXene溶液；
将MXene溶液经过水浴超声后得到含有MXene薄片的溶液；
将含有MXene薄片的溶液真空抽滤，得到MXene薄膜。
优选地，所述Ti 3AlC 2粉末为400～600目；更优选地，所述Ti 3AlC 2粉末为500目。
优选地，所述氢氟酸的质量分数为35％～50％；更优选地，所述氢氟酸的质量分数为40％。
优选地，所述Ti 3AlC 2粉末与氢氟酸的质量体积之比为1:50～150；更优选地，所述Ti 3AlC 2粉末与氢氟酸的质量体积之比为1:90；更优选地，所述Ti 3AlC 2粉末为0.1g，所述氢氟酸的质量体积为9ml。
优选地，所述水浴环境温度为43～46℃；更优选地，所述水浴环境温度为45℃。
优选地，在制备MXene薄膜过程中，所述离心转数为2000～5000r/min，离心时间为5～20min，重复离心操作3～8次。
更优选地，在制备MXene薄膜过程中，所述离心转数为3500r/min，离心时间为10min，重复离心操作6次。
优选地，所述pH调节至6.5～7.5；更优选地，所述pH调节至6.5～7。
本发明另一具体实施方式中，所述基底层为PDMS基底层，所述包覆层为PDMS包覆层。
优选地，将含有MXene薄片的溶液真空抽滤，其中，含有MXene薄片的溶液的体积为10～50ml，真空抽滤的滤膜孔径为0.1～0.45μm。更优选地，含有MXene薄片的溶液的体积为30ml，真空抽滤的滤膜孔径为0.22μm。
优选地，使用配制好的PDMS滴涂在模具上，旋涂制备PDMS基底层；
将抽滤好的MXene薄膜放置在PDMS基底层上，真空干燥后，使用导电银胶与铜导线引出电极；
导电银胶干燥后，再取配制好的PDMS滴涂在顶部，旋涂制备PDMS包覆层，真空干燥，制得二维MXene基声音探测器。
优选地，在PDMS配制过程中，将PDMS的A液与B液按照10:1的比例配置，取2～5ml配置好的PDMS旋涂到模具上形成PDMS基底层。
优选地，旋涂过程包括低速旋涂和高速旋涂，所述低速旋涂为200～500r/min旋涂5～20s，所述高速旋涂为1000～3000r/min旋涂20～60s。更优选地，所述低速旋涂为300r/min旋涂10s，所述高速旋涂为2000r/min旋涂30s。
优选地，所述真空干燥为60～120℃干燥0.5～2h；更优选地，所述真空干燥为80℃干燥1h。
优选地，所述导电银胶置于室温且通风的环境中自然干燥0.5～2h；更优选地，导电银胶置于室温且通风的环境中自然干燥1h。
本发明二维MXene基声音探测器的制备方法制备的二维MXene基声音探测器，具有初始声波分辨率高、检出限高等优点，能够高效解析出不同振幅或者频率的初始声波。该二维MXene基声音探测器的制备方法具有制作过程相对简单、工艺相对成熟、造价低，二 维MXene基声音探测器性能稳定，容易实现大规模量产等优点。
第四方面，本发明提供了一种上述二维MXene基声音探测器在人工电子喉咙上的应用。
本发明一具体实施方式中，包括以下步骤：
将二维MXene基声音探测器贴附于人体喉咙部位，并将一对电极与信号采集装置连接；
喉咙部位发声并产生振动，振动使得二维MXene基声音探测器弯曲变化，二维MXene基声音探测器的内部电阻值变化，信号采集装置采集一对电极之间的电阻值变化信号，生成电信号。
可选地，所述信号采集装置为数字万用表。
本发明另一具体实施方式中，所述信号采集装置为深度学习网络。
优选地，所述深度学习网络采集电阻值变化信号后，滤除干扰信号并合成喉咙部位振动对应的声音电信号，所述声音电信号用于输出解析后的模拟初始声波对外发声，即终端声波。
优选地，所述声音电信号与扬声器连接，所述声音电信号控制扬声器发出终端声波，并对外发声。
本发明二维MXene基声音探测器在人工电子喉咙上的应用，具备二维MXene基声音探测器的人工电子喉咙具有初始声波分辨率高、检出限高等优点，能够高效解析出不同振幅或者频率的初始声波；该人工电子喉咙进一步基于检出的声波电信号发出终端声波，实现从初始声波-电信号-终端声波的转换过程。该高性能二维MXene基声音探测器制备的人工电子喉咙，能够帮助发声障碍的人群有效发声、正确表达其初始声波所包含的意思。
本发明的优点将会在下面的说明书中部分阐明，一部分根据说明书是显而易见的，或者可以通过本发明实施例的实施而获知。
附图说明
为更清楚地阐述本发明的内容，下面结合附图与具体实施例来对其进行详细说明。
图1为本发明一实施方式提供的二维MXene基声音探测器的结构示意图。
图2为本发明另一实施方式提供的SR-CNN网络的结构示意图。
图3-a为Ti 3AlC 2粉末的扫描电镜谱图；图3-b为Ti 3AlC 2粉末经过刻蚀后所制得的MXene薄膜的扫描电镜谱图；图3-c为MXene薄片的扫描电镜谱图；图3-d为Ti 3AlC 2粉末及MXene薄膜的XRD谱图。
图4为实施例1中制得的二维MXene基声音探测器对扬声器发声的测试结果；图4-a为二维MXene基声音探测器对不同频率初始声波的测试结果(第一个峰处从上往下依次为：250Hz、100Hz、300Hz、400Hz、200Hz、350Hz、500Hz、150Hz、50Hz；第二个峰处从上往下依次为：250Hz、100Hz、300Hz、400Hz、200Hz、350Hz、500Hz、150Hz、50Hz；第三个峰处从上往下依次为：250Hz、100Hz、300Hz、200Hz、400Hz、350Hz、500Hz、150Hz、50Hz；第四个峰处从上往下依次为：250Hz、100Hz、300Hz、200Hz、400Hz、350Hz、500Hz、150Hz、50Hz；第五个峰处从上往下依次为：250Hz、100Hz、300Hz、200Hz、400Hz、350Hz、500Hz、150Hz、50Hz)；图4-b为二维MXene基声 音探测器对不同强度初始声波的测试结果(五个峰处从上往下依次为：110dB、106dB、101dB、94dB、87dB)。
图5为实施例1中制得的二维MXene基声音探测器对喉咙发声的测试结果；图5-a为二维MXene基声音探测器对不同单词进行发音的测试结果(从左往右依次为“上”、“下”、“左”、“右”、“我”、“你”)；5-b为二维MXene基声音探测器对同一单词重复发音的测试结果；5-c为二维MXene基声音探测器对不同音调进行发音的测试结果(左侧第一、二个峰为“ō”，第三、四个峰为“ó”)。
图6为深度学习网络结合二维MXene基声音探测器进行测试的流程图。
具体实施方式
以下所述是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也视为本发明的保护范围。对本发明做进一步描述，需要说明的是，在不相冲突的前提下，以下描述的各实施例之间或各技术特征之间可以任意组合形成新的实施例。
实施例1
请参照图1，为本发明一实施方式提供的二维MXene基声音探测器。该二维MXene基声音探测器包括基底层10、MXene薄膜20、电极30及包覆层40。其中，基底层10设置于二维MXene基声音探测器的最底部，包覆层40设置于二维MXene基声音探测器的最顶部，基底层10与包覆层40合围成用于容置MXene薄膜的密封容置腔，且MXene薄膜20及电极30均容置于密封容置腔中，起到保护MXene薄膜20不受氧化的作用，另外也能保护电极30不受腐蚀。上述二维MXene基声音探测器中，电极30包括一对，一对电极30均与MXene薄膜20接触，本实施例中，直接通过焊盘将电极30与MXene薄膜20连接，在其他实施例中，还可以是其他连接方式，仅需保证电极30与MXene薄膜20能够电导通即可，由此实现一对电极30通过MXene薄膜20电导通。
使用该二维MXene基声音探测器时，先将二维MXene基声音探测器贴附于发声部位，例如可以是喉咙部位，再将一对电极30与信号采集装置连接，信号采集装置用于采集MXene薄膜20的电阻值变化信号，或者是，信号采集装置用于采集因MXene薄膜20的电阻值变化而引起的电压、电流变化信号等。喉咙振动过程中，MXene薄膜20能够随之而振动并产生形态弯曲，MXene薄膜20的各纳米片层之间相对滑动并产生裂缝或者间隙，导致各纳米片层间的接触面积变化。不同振幅和频率的声音信号作用于MXene薄膜20时，MXene薄膜20的的各纳米片之间的接触电阻不同，最终表现为二维MXene基声音探测器的总电阻变化。二维MXene纳米薄膜具有优异的电学性能和力学性能，即不同振幅或者频率的初始声波作用于MXene薄膜20时，能够带来MXene薄膜20总电阻的大小及变化频率差异，实现高效检测和分辨初始声波，并基于初始声波生成对应的电信号的功能。
在本实施例中，信号采集装置可选为数字万用表，数字万用表分别与一对电极30电连接，数字万用表自带电源并能检测MXene薄膜的电阻值，借助于计算机系统，能够有效 地将MXene薄膜20电阻值变化的振幅及频率等电信号记录下来并存储在相应存储介质中，当需要模拟喉咙发声时，可以将电阻值变化形成的电信号输入扬声器，用于模拟喉咙发声。
在本实施例中，基底层10为PDMS基底层，包覆层30为PDMS包覆层。PDMS能够有效实现MXene薄膜20的放置以及固定，PDMS之间粘附能够有效形成密封容置腔，防止空气侵入而氧化MXene薄膜20或者腐蚀电极30。
在本实施例中，还包括引线50，电极30与引线50电连接，电极30内置于密封容置腔中，引线50穿过密封容置腔，通过引线50实现电极30与外部信号采集装置电连接，同时确保密封容置腔的密封效果。在其他实施例中，还可以是电极30从密封容置腔内部延伸到外侧，用于与外部信号采集装置电连接，具有相同的效果。
在本实施例中，电极的材质包括铬和金中的至少一种，例如可以是铬电极、金电极或者铬、金掺杂电极等。电极的厚度可以为25nm～90nm，例如可以是25nm、40nm、55nm、70nm、80nm、90nm。
实施例2
实施例2与实施例1的不同之处在于：该二维MXene基声音探测器还包括偏压电源，偏压电源两端分别与一对电极30电连接，且偏压电源用于给MXene薄膜20提供偏压Vbia。此时，信号采集装置可选为电压表、电流表等，用于检测MXene薄膜20上的电压或者电流变化，同样能够将初始声波转换成频率、振幅相应变化的电信号。
在本实施例中，信号采集装置优选为深度学习网络，一对电极30与深度学习网络信号连接，且深度学习网络用于检测MXene薄膜20的电阻值变化。借助于深度学习网络，能够将从电极30获得的电信号处理成高清的声音电信号，即解析出初始声波对应的电信号，并基于该电信号识别、拟合出更高清的声音电信号，处理后声音电信号的振幅、频率更为特定、准确，由声音电信号转换成的终端声波也更准确、发声准确。
在本实施例中，如图2所示，深度学习网络优选为SR-CNN网络，借助于SR-CNN网络的超高分辨率算法，实现对检测到的电信号进行智能识别、优化，最终处理得到高清晰度的声波电信号(即高清电信号)以及与该高清电信号对应的终端声波。
更优选地，SR-CNN网络依次包括第一卷积层(由16个卷积核组成的卷积层，大小为32×1)、第一池化层(内核大小为8×1的最大池化层)、第二卷积层(由32个卷积核组成的卷积层，大小为32×1)、第二池化层(内核大小为8×1的最大池化层)、第三卷积层(由64个卷积核组成的卷积层，大小为16×1)、第四卷积层(由128个卷积核组成的卷积层，大小为8×1)、第五卷积层(由256个卷积核组成的卷积层，大小为4×1)、第三池化层(内核大小为4×1的最大池化层)、第六卷积层(由512个卷积核组成的卷积层，大小为4×1)、第七卷积层(卷积层由256个大小为4×1的卷积核组成)。经过多层次优化算法，能够获得高清音频信号，最终转换成高清、精准的声音振动，完成从初始声波的检测-转换成电信号-电信号的智能识别、优化-输出高清电信号等一连串过程，解决现有声音探测器存在的无法高分辨率检测、采集初始声波、电信号分辨率低、无法转换成高清的终端声波等缺陷。
更优选地，SR-CNN网络依次包括第一卷积层(由16个卷积核组成的卷积层，大小为32×1)、第一池化层(内核大小为8×1的最大池化层)、第二卷积层(由32个卷积核组成的卷积层，大小为32×1)、第二池化层(内核大小为8×1的最大池化层)、第三卷积层(由64个卷积核组成的卷积层，大小为16×1)、第四卷积层(由128个卷积核组成的卷积层，大小为8×1)、第五卷积层(由256个卷积核组成的卷积层，大小为4×1)、第三池化层(内核大小为4×1的最大池化层)、第六卷积层(由512个卷积核组成的卷积层，大小为4×1)、第七卷积层(卷积层由1024个大小为4×1的卷积核组成)、第一神经元层(具有1024个神经元的完全连接的层)、第二神经元层(具有512个神经元的完全连接的层)。
实施例3
一种人工电子喉咙，包括实施例1或者实施例2任一项中的二维MXene基声音探测器及发声装置。其中，二维MXene基声音探测器用于探测振动并生成电信号，发声装置用于将电信号转换成初始声波。使用时，二维MXene基声音探测器实现高效检测和分辨初始声波，并基于检测的初始声波生成对应的电信号的功能，发声装置基于生成的电信号转换成终端声波，模拟出初始声波并对外发声。该人工电子喉咙能够高效解析出有效的声波振动，且有效辨析出不同初始声波之间的频率及振幅变化，实现高分辨率探测初始声波。
实施例4
MXene薄膜的制备过程包括：
第一步，取500目Ti 3AlC 2粉末0.1g置于9ml质量分数为40％氢氟酸中，在45℃的水浴环境下刻蚀48小时，刻蚀后的反应液经转数为3500r/min的离心条件下离心10min，重复上述离心操作5-6次，再调节pH值至6.5～7.0后，得到MXene溶液。
第二步，将MXene溶液转移至恒温水浴超声(40KHz，超声功率350W)1小时后得到含有MXene薄片的溶液。
第三步，将含有MXene薄片的溶液真空抽滤，得到MXene薄膜，其中，含有MXene薄片的溶液的体积为30ml，真空抽滤的滤膜孔径为0.22μm。
将上述Ti 3AlC 2粉末以及制备出的MXene薄膜分别进行表征。如图3-a及3-b所示，分别为Ti 3AlC 2和MXene薄膜扫描电镜(SEM)图，如图3-b所示，MXene薄膜经过氢氟酸刻蚀后形成明显的多层结构，该多层结构类似于手风琴。
取上述MXene溶液中的MXene薄片进行SEM测试，图3-c为MXene薄片的扫描电镜图，如图3-c所示，该扫描范围中出现一层数较少的MXene薄片，该MXene薄片的轮廓通过闭环点状线显示。
取上述Ti 3AlC 2粉末以及制备出的MXene薄膜进行X射线衍射(XRD)表征，如图3-d所示，分别为Ti 3AlC 2粉末及MXene薄膜的X射线衍射图。如图3-d所示，Ti 3AlC 2粉末经过氢氟酸刻蚀后，104峰消失了，表明Ti 3AlC 2中的铝离子已经完全被氢氟酸刻蚀掉。与Ti 3AlC 2的XRD谱图进行比较，MXene薄膜(Ti 3AlC 2T X)的002峰出现红移现象。另外，002峰出现拓宽的现象，可能与MXene薄膜的无序性增加有关。
实施例5
二维MXene基声音探测器的制备过程：
第一步，将PDMS的A液于B液按照10:1的比例配置，制得配置好的PDMS；取2～5ml配置好的PDMS旋涂到模具上形成PDMS基底层。其中，旋涂过程包括低速旋涂和高速旋涂，低速旋涂为300r/min旋涂10s，高速旋涂为2000r/min旋涂30s。
第二步，将实施例4制备的MXene薄膜转移至PDMS基底层上，80℃温度条件下真空干燥1h，由此MXene薄膜牢固地粘附在PDMS基底层上。
第三步，在MXene薄膜上设置一对电极，且一对电极通过MXene薄膜电导通。在具体的实施方式中，一对电极可以通过导电胶与MXene薄膜粘接，也可以通过金属焊盘将电极固定在MXene薄膜上。本实施例优选为导电银胶粘接，再通过导电银胶与铜导线引出电极，实现电极与外部信号采集装置之间的电连接。电极设置完成后，再转移至室温且通风的环境中自然干燥1h，导电银胶固化。
第四步，再取2～5ml配制好的PDMS滴涂在探测器顶部，旋涂制备PDMS包覆层，真空干燥，制得二维MXene基声音探测器。旋涂及干燥过程同上述第一步、第二步。
实施例6
一种实施例1或者实施例2中的二维MXene基声音探测器在人工电子喉咙上的应用，具体表现为应用二维MXene基声音探测器制备人工电子喉咙。具备二维MXene基声音探测器的人工电子喉咙具有声波分辨率高、检出限高等优点，能够高效解析出不同振幅或者频率的初始声波，进一步基于检出的电信号发出终端声波，实现从初始声波-电信号-终端声波的转换过程。该高性能二维MXene基声音探测器制备的人工电子喉咙，能够帮助发声障碍的人群有效发声、正确发声。
在具体的实施方式中，二维MXene基声音探测器在人工电子喉咙上的应用方法包括以下步骤：
第一步，将二维MXene基声音探测器贴附于人体喉咙部位，并将一对电极与信号采集装置电连接。
第二步，喉咙部位发声并产生振动，振动使得二维MXene基声音探测器弯曲变化，二维MXene基声音探测器的内部电阻值变化，信号采集装置采集一对电极之间的电阻值变化信号，生成电信号。
在一具体的实施方式中，信号采集装置为数字万用表，数字万用表自带电源并能测试MXene薄膜的电阻值变化。更优选的，也能通过计算机系统计算数字万用表显示的MXene薄膜的电阻值变化，并通过显示界面显示初始声波产生的电信号，包括电阻值振荡的幅度及频率。
在另一具体的实施方式中，一对电极两端附加上偏压，通过信号采集装置采集MXene薄膜两端的电压信号或者电流信号，实现将喉咙发声产生的初始声波(振动)转化为对应的电信号，此处的“对应”指得是产生与初始声波的频率、振幅变化对应的脉冲电信号。
在具体的实施方式中，信号采集装置为深度学习网络。深度学习网络采集电阻值变化 信号后，滤除干扰信号并合成喉咙部位振动对应的声音电信号，所述声音电信号用于输出解析后的终端声波。
作为优选的实施方式，深度学习网络SR-CNN网络。
作为优选的实施方式，声音电信号与扬声器连接，声音电信号控制扬声器发出终端声波。
效果实施例
效果实施例1：对不同频率、不同声音强度的单音频信号的检测
如图4所示，将实施例1制备的二维MXene基声音探测器贴附在扬声器的振膜上。通过电脑控制分别播放了50Hz、100Hz、150Hz、200Hz、250Hz、300Hz、350Hz、400Hz、500Hz的单音频声音信号，每一种信号的播放时间持续五秒钟、两次播放间隔五秒钟，测试结果图如附图4-a(横坐标表示时间，纵坐标表示电阻变化率，ΔR表示电阻变化，R0表示初始电阻，R表示变化后的电阻)所示，可以看出，实施例1制备的二维MXene基声音探测器对不同频率的声音信号有不同的响应结果，可以基本实现对不同频率的声音信号的检测。特别的，在250Hz的声音信号作用在器件上的时候，器件的电阻变化率最大，在50Hz的声音信号作用在器件上的时候，器件的电阻变化率最小。
随后，控制声音的频率不变，此处选择的声音频率为100Hz，通过改变声音信号的输出强度，本次实验选用了五个不同的声音强度：87dB、94dB、101dB、106dB、110dB。(声音的强度单位为dB通过测试麦克风在半消声室环境中测试得到)。实验结果如附图4-b所示，可以看出，实施例1制备的二维MXene基声音探测器对不同声音强度有着不同的响应结果，其响应结果大小(电阻变化率大小)随着声音强度的增加而增加，两者呈现出正相关关系。
综上，实施例1制备的基于MXene的声音探测器不仅可以对不同频率的声音信号进行检测，而且可以实现对相同频率下不同声音强度的检测。
效果实施例2：对人体喉部不同发音的检测
如图5所示，将实施例1制备的二维MXene基声音探测器贴附人体喉部舌骨位置。测试者对六个不同的单词进行发音，这六个单词分别是“上”、“下”、“左”、“右”、“我”、“你”，然后记录电阻变化波形，结果如附图5-a所示，从图中可以看出，二维MXene基声音探测器对不同的读音有着不同的响应结果，其中“上”的读音引起的电阻变化值最大，这有可能是因为测试者读“上”的时候喉部肌肉的运动幅度相对其他几个读音较大引起的。然后本效果实施例也进行了重复性检测试验，测试者连续读“你”五次，其结果图如附图5-b所示，五次响应结果的变化趋势基本相同，说明器件的重复检测结果较好。最后本效果实施例也对不同的汉语声调进行了测试，通过对“ō”和“ó”两个声调分别阅读两次，得到的实验结果图如附图5-c所示，从图中可以看出实施例1制备的二维MXene基声音探测器可以实现对不同音调的检测，其中第二声调的特征峰相对于第一声调的特征峰较多。
效果实施例3：实施例2制备的二维MXene基声音探测器进行语音识别
从上面的效果实施例1和2可以知道，实施例1制备的二维MXene基声音探测器可 以对不同的音箱声音信号和人喉发声信号进行检测，我们可以将这些实验结果结合深度学习网络，尝试达到语音识别的目的。实验的总体流程图如附图6所示。我们一共测试了两组数据，分别是：①扬声器播放“a”的长元音和短元音的音频信号②人喉发音“a”的长元音和短元音。实验流程如下：
①扬声器播放音频信号。扬声器分别播放750次“a”的长元音和短元音(共计1500次)，将1500次的测试结果中的70％的数据即1050个数据作为训练集(包括525个长元音和525个短元音)，剩下的450个数据作为测试集(包括225个长元音和225个短元音)。将训练集(1050个数据)输入到我们的SR-CNN网络中，网络详细的结构如附图6所示，在SR-CNN网络经过训练集的充分训练后，将测试集数据(450个数据)输入到网络中得到识别结果，识别结果如表1所示，从识别结果中我们可以看出，我们的网络对长元音的识别准确率为：83.6％(188/225)，对短元音的识别率为：88.9％(200/225)，总体平均的识别准确率为86.2％。
表1.对扬声器播放音频信号的识别统计
Figure PCTCN2020123909-appb-000001
②人体喉部发音。测试者分别进行200次“a”的长元音和短元音的发音(共计400次)，将400次的测试结果中的70％的数据即280个数据作为训练集(包括140个长元音和140个短元音)，剩下的120个数据作为测试集(包括60个长元音和60个短元音)。将训练集(280个数据)输入到我们的SR-CNN网络中，网络得到充分的训练后，将测试集(120个数据)输入到网络中得到识别结果，识别结果如表2所示，从识别结果可以看出，我们的网络对人喉发音长元音的识别率为：70％(42/60)，对短元音的识别准确率为76.7％(46/60)，总体平均的识别准确率为73.4％。
表2.对人体喉部发声的识别统计
Figure PCTCN2020123909-appb-000002
分析结果显示，人体喉部发声的信号数据检测准确率低的一个原因就是训练数据的样本量过小，随着样本数据量的增加，深度学习算法将表现出更为优异的识别分辨率。另一个原因在于：从人体喉部收集的初始声波数据比从扬声器处收集的初始声波数据具有更大程度的失真。例如，在人体喉咙发声过程中，喉咙还存在的其它运动，比如吞咽。随着训练数据样本的不断增加，深度学习网络的识别效率会进一步提升，无需额外辅助即能单独实现高效检测初始声波的功能。
以上所述实施例仅表达了本发明的几种实施方式，其描述较为具体和详细，但并不能 因此而理解为对本发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。

Claims (14)

  1. 一种二维MXene基声音探测器，其特征在于，包括基底层、MXene薄膜、电极及包覆层，所述基底层与包覆层合围成用于容置MXene薄膜的密封容置腔；
    所述电极包括一对，一对电极均与MXene薄膜接触，且一对电极通过MXene薄膜电导通。
  2. 如权利要求1所述的二维MXene基声音探测器，其特征在于，所述基底层为PDMS基底层，所述包覆层为PDMS包覆层；
    所述电极与引线电连接，所述电极内置于密封容置腔，所述引线穿过密封容置腔。
  3. 如权利要求2所述的二维MXene基声音探测器，其特征在于，还包括偏压电源，所述偏压电源两端分别与一对电极电连接，且所述偏压电源用于给MXene薄膜提供偏压。
  4. 如权利要求3所述的二维MXene基声音探测器，其特征在于，还包括深度学习网络，所述一对电极与深度学习网络信号连接，且所述深度学习网络用于检测所述MXene薄膜的电阻值变化。
  5. 如权利要求4所述的二维MXene基声音探测器，其特征在于，所述深度学习网络为SR-CNN网络；
    所述SR-CNN网络包括7个卷积层及3个池化层。
  6. 如权利要求2所述的二维MXene基声音探测器，其特征在于，还包括数字万用表，所述数字万用表分别与一对电极电连接，且所述数字万用表用于检测MXene薄膜的电阻值。
  7. 一种人工电子喉咙，其特征在于，包括权利要求1-6任一项所述的二维MXene基声音探测器及发声装置，所述二维MXene基声音探测器用于探测初始声波并生成电信号，所述发声装置用于将电信号转换成终端声波。
  8. 一种二维MXene基声音探测器的制备方法，其特征在于，包括以下步骤：
    提供基底层，在所述基底层上设置MXene薄膜；
    在所述MXene薄膜上设置一对电极，且一对电极通过MXene薄膜电导通；
    设置包覆层，所述包覆层与基底层合围成用于容置MXene薄膜的密封容置腔，制得二维MXene基声音探测器。
  9. 如权利要求8所述的二维MXene基声音探测器的制备方法，其特征在于，所述MXene薄膜的制备过程包括以下步骤：
    取Ti 3AlC 2粉末置于氢氟酸中，在42～48℃的水浴环境下刻蚀30～72小时，离心、调节pH后，得到MXene溶液；
    将MXene溶液经过水浴超声后得到含有MXene薄片的溶液；
    将含有MXene薄片的溶液真空抽滤，得到MXene薄膜。
  10. 如权利要求9所述的二维MXene基声音探测器的制备方法，其特征在于，所述Ti 3AlC 2粉末为400～600目，所述氢氟酸的质量分数为35％～50％，所述Ti 3AlC 2粉末与氢 氟酸的质量体积之比为1:50～150；
    所述离心的转数为1500～5000r/min，离心时间为10分钟，重复离心操作4～6次。
  11. 如权利要求10所述的二维MXene基声音探测器的制备方法，其特征在于，使用配制好的PDMS滴涂在模具上，旋涂制备PDMS基底层；
    将抽滤好的MXene薄膜放置在PDMS基底层上，真空干燥后，使用导电银胶与铜导线引出电极；
    导电银胶干燥后，再取配制好的PDMS滴涂在顶部，旋涂制备PDMS包覆层，真空干燥，制得二维MXene基声音探测器。
  12. 如权利要求1-6任一项所述的二维MXene基声音探测器在人工电子喉咙上的应用。
  13. 如权利要求12所述的二维MXene基声音探测器在人工电子喉咙上的应用，其特征在于，包括以下步骤：
    将二维MXene基声音探测器贴附于人体喉咙部位，并将一对电极与信号采集装置连接；
    喉咙部位发声并产生振动，振动使得二维MXene基声音探测器弯曲变化，二维MXene基声音探测器的内部电阻值变化，信号采集装置采集一对电极之间的电阻值变化信号，生成电信号。
  14. 如权利要求13所述的二维MXene基声音探测器在人工电子喉咙上的应用，其特征在于，所述信号采集装置为深度学习网络；
    所述深度学习网络采集电阻值变化信号后，滤除干扰信号并合成喉咙部位振动对应的高清电信号，所述高清信号用于输出解析后的终端声波。
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