CN111121951A - Two-dimensional MXene-based sound detector and preparation method and application thereof - Google Patents
Two-dimensional MXene-based sound detector and preparation method and application thereof Download PDFInfo
- Publication number
- CN111121951A CN111121951A CN201911281660.0A CN201911281660A CN111121951A CN 111121951 A CN111121951 A CN 111121951A CN 201911281660 A CN201911281660 A CN 201911281660A CN 111121951 A CN111121951 A CN 111121951A
- Authority
- CN
- China
- Prior art keywords
- mxene
- sound detector
- dimensional
- based sound
- film
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000002360 preparation method Methods 0.000 title claims abstract description 14
- 239000010410 layer Substances 0.000 claims abstract description 96
- 230000008859 change Effects 0.000 claims abstract description 31
- 239000011247 coating layer Substances 0.000 claims abstract description 18
- 239000004205 dimethyl polysiloxane Substances 0.000 claims description 33
- 235000013870 dimethyl polysiloxane Nutrition 0.000 claims description 33
- CXQXSVUQTKDNFP-UHFFFAOYSA-N octamethyltrisiloxane Chemical group C[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C CXQXSVUQTKDNFP-UHFFFAOYSA-N 0.000 claims description 33
- 238000004987 plasma desorption mass spectroscopy Methods 0.000 claims description 33
- 229920000435 poly(dimethylsiloxane) Polymers 0.000 claims description 33
- KRHYYFGTRYWZRS-UHFFFAOYSA-N Fluorane Chemical compound F KRHYYFGTRYWZRS-UHFFFAOYSA-N 0.000 claims description 26
- 238000013135 deep learning Methods 0.000 claims description 25
- 238000004528 spin coating Methods 0.000 claims description 23
- 229910009818 Ti3AlC2 Inorganic materials 0.000 claims description 17
- 239000000843 powder Substances 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 16
- 238000011176 pooling Methods 0.000 claims description 16
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 claims description 9
- 229910052709 silver Inorganic materials 0.000 claims description 8
- 239000004332 silver Substances 0.000 claims description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 7
- 238000005253 cladding Methods 0.000 claims description 6
- 238000005119 centrifugation Methods 0.000 claims description 5
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 3
- 238000001291 vacuum drying Methods 0.000 claims description 3
- 239000003292 glue Substances 0.000 claims 2
- WABPQHHGFIMREM-UHFFFAOYSA-N lead(0) Chemical compound [Pb] WABPQHHGFIMREM-UHFFFAOYSA-N 0.000 claims 2
- 239000000463 material Substances 0.000 abstract description 6
- 239000010408 film Substances 0.000 description 61
- 238000012360 testing method Methods 0.000 description 20
- 230000005236 sound signal Effects 0.000 description 16
- 239000000758 substrate Substances 0.000 description 14
- 238000001514 detection method Methods 0.000 description 13
- 238000012549 training Methods 0.000 description 11
- 238000004519 manufacturing process Methods 0.000 description 10
- 239000010409 thin film Substances 0.000 description 10
- 230000008569 process Effects 0.000 description 9
- 230000000694 effects Effects 0.000 description 8
- 239000000853 adhesive Substances 0.000 description 7
- 230000001070 adhesive effect Effects 0.000 description 7
- 238000001035 drying Methods 0.000 description 7
- 239000000523 sample Substances 0.000 description 7
- 239000002135 nanosheet Substances 0.000 description 6
- 238000003828 vacuum filtration Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000002441 X-ray diffraction Methods 0.000 description 4
- 210000002569 neuron Anatomy 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 229910052804 chromium Inorganic materials 0.000 description 3
- 239000011651 chromium Substances 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 238000005530 etching Methods 0.000 description 3
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 3
- 229910052737 gold Inorganic materials 0.000 description 3
- 239000010931 gold Substances 0.000 description 3
- 210000000867 larynx Anatomy 0.000 description 3
- 238000001878 scanning electron micrograph Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000005452 bending Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 239000012528 membrane Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000003252 repetitive effect Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 208000029951 Laryngeal disease Diseases 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- -1 aluminum ions Chemical class 0.000 description 1
- 239000012295 chemical reaction liquid Substances 0.000 description 1
- RZVXOCDCIIFGGH-UHFFFAOYSA-N chromium gold Chemical compound [Cr].[Au] RZVXOCDCIIFGGH-UHFFFAOYSA-N 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 210000003238 esophagus Anatomy 0.000 description 1
- 210000003823 hyoid bone Anatomy 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 210000004717 laryngeal muscle Anatomy 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000001000 micrograph Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 239000002120 nanofilm Substances 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004626 scanning electron microscopy Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000000967 suction filtration Methods 0.000 description 1
- 230000009747 swallowing Effects 0.000 description 1
- 238000009210 therapy by ultrasound Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 210000001260 vocal cord Anatomy 0.000 description 1
- 230000001755 vocal effect Effects 0.000 description 1
Images
Classifications
-
- 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—Larynxes; Tracheae combined with larynxes or for use therewith
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Heart & Thoracic Surgery (AREA)
- Otolaryngology (AREA)
- Pulmonology (AREA)
- Cardiology (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Transplantation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Vascular Medicine (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
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薄膜时，能够带来MXene薄膜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。本发明还提供了一种人工电子喉咙、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。
The invention provides a two-dimensional MXene-based sound detector, comprising a base layer, an MXene film, an electrode and a coating layer, wherein the base layer and the coating layer are combined to form a sealed accommodating cavity 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 two-dimensional MXene-based sound detector of the present invention utilizes the superior electrical and mechanical properties of the MXene material, that is, when the initial sound waves of different amplitudes or frequencies act on the MXene film, the total resistance of the MXene film and the change frequency difference can be brought about. Efficiently detect and resolve initial acoustic waves and generate corresponding electrical signals. The invention also provides an artificial electronic throat, a preparation method of a two-dimensional MXene-based sound detector, and an application of the two-dimensional MXene-based sound detector on the artificial electronic throat.
Description
Technical Field
The invention relates to the technical field of sound detection devices, in particular to a two-dimensional MXene-based sound detector, further relates to an artificial electronic throat, and further relates to a preparation method and application of the two-dimensional MXene-based sound detector.
Background
Throat (vocal cord) is a unique biological structure that is used to produce sounds and facilitate communication with each other. Laryngeal diseases often lead to communication impairment, which is manifested by the inability of most patients to accurately sound through the throat. Currently, there are many solutions for assisting a patient in vocalizing sounds, such as the common use of a sound generator including an esophageal sound generator and an artificial electronic throat. The sound emitted by the esophagus is similar to the sound emitted by the normal throat. However, the esophageal vocalization needs to be trained by various methods, the training period is long, the training process is arduous, and even after a large amount of training, more than 60% of patients still learn that the esophageal vocalization cannot be overcome, which also becomes an obstacle that cannot be overcome by the esophageal vocalization. Artifical electron throat mainly realizes through sound detector, and the concrete expression does: the sound detector converts biological vibration (such as throat vibration) signals generated by sound into electric signals, analyzes and amplifies the electric signals, outputs the analyzed and amplified sound electric signals, and finally outputs the electric signals to loudspeaker equipment such as a loudspeaker to emit sound simulating the sound of the throat of a human body. However, the conventional artificial electronic throat also has a limited analysis function, so that one is difficult to detect and distinguish biological vibration signals, and the two cannot accurately simulate sound signals corresponding to biological vibration.
With the rapid development of wearable electronic products and clinical detection devices, more and more flexible sensor materials with high sensitivity and excellent electrical and mechanical properties are developed by people, and the development of a wearable high-resolution sound detector becomes technically possible and becomes a research hotspot of high-performance medical instruments at present.
Disclosure of Invention
In view of this, the invention provides a two-dimensional MXene-based sound detector, which utilizes the excellent electrical and mechanical properties of MXene materials, can efficiently detect and distinguish sound wave vibration, and generates a corresponding electrical signal based on the vibration, so as to solve the problems of low detection limit, low resolution and the like of the existing sound detector.
In a first aspect, the invention provides a two-dimensional MXene-based sound detector, which comprises a substrate layer, an MXene film, an electrode and a coating layer, wherein the substrate layer and the coating layer surround a sealed accommodating cavity for accommodating the MXene film;
the electrodes comprise a pair, the pair of electrodes are both in contact with the MXene thin film, and the pair of electrodes are electrically conducted through the MXene thin film.
In an embodiment of the present invention, the pair of electrodes are respectively disposed on two sides of the MXene film, and the MXene film and the pair of electrodes are disposed in the sealed accommodating chamber.
Preferably, the base layer is a PDMS base layer, and the coating layer is a PDMS coating layer.
Preferably, the electrode is electrically connected with a lead, the electrode is arranged in the sealed accommodating cavity, and the lead passes through the sealed accommodating cavity.
Preferably, the material of the electrode comprises at least one of chromium and gold; the thickness of the electrode is 25 nm-90 nm.
Preferably, the MXene thin film further comprises a bias power supply, wherein two ends of the bias power supply are respectively electrically connected with the pair of electrodes, and the bias power supply is used for providing bias voltage for the MXene thin film.
In a specific embodiment of the present invention, the MXene thin film further includes a digital multimeter, the digital multimeter is electrically connected to the pair of electrodes, and the digital multimeter is used for detecting a resistance value of the MXene thin film.
In another embodiment of the present invention, the MXene thin film further includes a deep learning network, wherein the pair of electrodes is connected to the deep learning network by a signal, and the deep learning network is used for detecting a change in a resistance value of the MXene thin film.
Preferably, the deep learning network is an SR-CNN (Syllable registration ConvolationNeural network) network.
Preferably, the deep learning network sequentially includes a first convolution layer, a first pooling layer, a second convolution layer, a second pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, a third pooling layer, a sixth convolution layer, and a seventh convolution layer.
The two-dimensional MXene-based sound detector of the first aspect of the present invention. In the external sound vibration process, the MXene film can vibrate along with the MXene film and generate shape bending, and the nanosheets of the MXene film slide relatively to each other and generate cracks or gaps, so that the contact area between the nanosheets is changed. When sound signals with different amplitudes and frequencies act on the MXene film, the contact resistance between the nanosheets of the MXene film is different, and finally the change of the total resistance of the two-dimensional MXene-based sound detector is shown. When the two-dimensional MXene-based sound detector is externally biased, the total resistance of the two-dimensional MXene-based sound detector changes, and the change of a voltage or current signal between two electrodes is detected, so that an electric signal corresponding to a sound signal can be detected. The two-dimensional MXene-based sound detector utilizes the excellent electrical property and mechanical property of the MXene material, namely, when sound wave signals with different amplitudes or frequencies act on the MXene film, the size and frequency difference of the total resistance of the MXene film can be brought, the functions of efficiently detecting and distinguishing the sound wave signals and generating corresponding electric signals based on the sound wave signals are realized.
In a second aspect, the invention provides an artificial electronic throat, which includes any one of the two-dimensional MXene-based sound detector and the sound generating device, wherein the two-dimensional MXene-based sound detector is configured to detect vibration and generate an electric signal, and the sound generating device is configured to convert the electric signal into a terminal sound wave.
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, wherein the two-dimensional MXene-based sound detector realizes a function of efficiently detecting and distinguishing a sound wave signal, and generates a corresponding electric signal based on the sound wave signal, and the sound generating device converts the generated electric signal into a terminal sound wave, simulates an initial sound wave, and generates a sound to the outside. This artifical electron throat can high-efficiently resolve out effectual initial sound wave, and effectively distinguish and separate out frequency and amplitude variation between the vibration of different initial sound waves, realizes the high resolution and surveys initial sound wave.
In a third aspect, the invention provides a method for preparing a two-dimensional MXene-based sound detector, which comprises the following steps:
providing a base layer, and arranging an MXene film on the base layer;
a pair of electrodes is arranged on the MXene film and is electrically conducted through the MXene film;
and arranging a coating layer, wherein the coating layer and the substrate layer surround a sealed accommodating cavity for accommodating the MXene film to obtain the two-dimensional MXene-based sound detector.
In a specific embodiment of the present invention, the preparation process of the MXene film comprises the following steps:
taking Ti3AlC2Placing the powder in hydrofluoric acid, etching for 30-72 hours in a water bath environment at 42-48 ℃, centrifuging, and adjusting the pH value to obtain an MXene solution;
carrying out water bath ultrasonic treatment on the MXene solution to obtain a solution containing MXene slices;
and carrying out vacuum filtration on the solution containing the MXene sheet to obtain the MXene film.
Preferably, the Ti3AlC2The powder is 400-600 meshes; more preferably, the Ti3AlC2The powder is 500 meshes.
Preferably, the mass fraction of the hydrofluoric acid is 35-50%; more preferably, the mass fraction of hydrofluoric acid is 40%.
Preferably, the Ti3AlC2The mass volume ratio of the powder to the hydrofluoric acid is 1: 50-150; more preferably, the Ti3AlC2The mass volume ratio of the powder to the hydrofluoric acid is 1: 90; more preferably, the Ti3AlC2The powder is 0.1g, and the mass volume of the hydrofluoric acid is 9 ml.
Preferably, the ambient temperature of the water bath is 43-46 ℃; more preferably, the bath ambient temperature is 45 ℃.
Preferably, in the process of preparing the MXene film, the centrifugal rotation number is 2000-5000 r/min, the centrifugal time is 5-20 min, and the centrifugal operation is repeated for 3-8 times.
More preferably, in the preparation of MXene thin film, the centrifugation is repeated 6 times at 3500r/min and 10 min.
Preferably, the pH is adjusted to 6.5-7.5; more preferably, the pH is adjusted to 6.5-7.
In another specific embodiment of the present invention, the substrate layer is a PDMS substrate layer, and the coating layer is a PDMS coating layer.
Preferably, the solution containing the MXene flakes is subjected to vacuum filtration, wherein the volume of the solution containing the MXene flakes is 10-50 ml, and the aperture of a filter membrane subjected to vacuum filtration is 0.1-0.45 μm. More preferably, the volume of the solution containing MXene flakes is 30ml and the filter pore size is 0.22 μm with vacuum filtration.
Preferably, the PDMS base layer is prepared by spin coating using the prepared PDMS drop coated on the mold;
placing the MXene film after suction filtration on a PDMS substrate layer, drying in vacuum, and leading out an electrode by using conductive silver adhesive and a copper wire;
and drying the conductive silver adhesive, dripping the prepared PDMS on the top, preparing a PDMS coating layer by spin coating, and drying in vacuum to obtain the two-dimensional MXene-based sound detector.
Preferably, in the preparation process of PDMS, the solution a and the solution B of PDMS are prepared according to a ratio of 10:1, and 2-5 ml of prepared PDMS is spin-coated on a mold to form a PDMS base layer.
Preferably, the spin coating process comprises low-speed spin coating and high-speed spin coating, wherein the low-speed spin coating is 200-500 r/min spin coating for 5-20 s, and the high-speed spin coating is 1000-3000 r/min spin coating for 20-60 s. More preferably, the low-speed spin coating is 300r/min spin coating for 10s, and the high-speed spin coating is 2000r/min spin coating for 30 s.
Preferably, the vacuum drying is carried out at 60-120 ℃ for 0.5-2 h; more preferably, the vacuum drying is drying at 80 ℃ for 1 h.
Preferably, the conductive silver adhesive is placed in a room-temperature and ventilated environment and naturally dried for 0.5-2 h; more preferably, the conductive silver paste is left to dry naturally for 1 hour in a room temperature and ventilated environment.
The two-dimensional MXene-based sound detector prepared by the preparation method of the two-dimensional MXene-based sound detector has the advantages of high initial sound wave resolution, high detection limit and the like, and can efficiently analyze the initial sound waves with 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 process, low manufacturing cost, stable performance of the two-dimensional MXene-based sound detector, easiness in realizing large-scale mass production and the like.
In a fourth aspect, the invention provides an application of the two-dimensional MXene-based sound detector in artificial electronic throat.
In one embodiment of the present invention, the method comprises the following steps:
attaching a two-dimensional MXene-based sound detector to the throat part of a human body, and connecting a pair of electrodes with a signal acquisition device;
the throat part produces sound and vibration, the vibration makes the two-dimensional MXene-based sound detector bend and change, the internal resistance value of the two-dimensional MXene-based sound detector changes, and the signal acquisition device acquires a resistance value change signal between a pair of electrodes to generate an electric signal.
Optionally, the signal acquisition device is a digital multimeter.
In another embodiment of the present invention, the signal acquisition device is a deep learning network.
Preferably, after the deep learning network collects the resistance value change signal, the interference signal is filtered and a sound electrical signal corresponding to throat part vibration is synthesized, and the sound electrical signal is used for outputting the analyzed simulated initial sound wave to make an external sound, namely, the terminal sound wave.
Preferably, the sound electric signal is connected with a speaker, and the sound electric signal controls the speaker to emit a terminal sound wave and emit a sound to the outside.
The two-dimensional MXene-based sound detector is applied to the artificial electronic throat, has the advantages of high initial sound wave resolution, high detection limit and the like of the artificial electronic throat of the two-dimensional MXene-based sound detector, and can efficiently analyze initial sound waves with different amplitudes or frequencies; the artificial electronic throat further emits a terminal sound wave based on the detected sound wave electric signal, and realizes a 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 sound production disorder to effectively produce sound and correctly express the meaning contained in the initial sound wave.
Advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of the invention.
Drawings
In order to more clearly illustrate the contents of the present invention, a detailed description thereof will be given below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a schematic structural diagram of a two-dimensional MXene-based sound detector according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an SR-CNN network according to another embodiment of the present invention.
FIG. 3-a is Ti3AlC2Scanning electron micrograph of the powder; FIG. 3-b is Ti3AlC2Scanning electron microscope spectrogram of the MXene film prepared by etching the powder; FIG. 3-c is a scanning electron micrograph of MXene flakes; FIG. 3-d is Ti3AlC2XRD spectra of powder and MXene film.
FIG. 4 shows the test results of the two-dimensional MXene-based sound probe made in example 1 sounding a speaker; FIG. 4-a shows the test results of the two-dimensional MXene-based sound detector on the initial sound waves with different frequencies (the first peak is 250Hz, 100Hz, 300Hz, 400Hz, 200Hz, 350Hz, 500Hz, 150Hz, and 50Hz from top to bottom, the second peak is 250Hz, 100Hz, 300Hz, 400Hz, 200Hz, 350Hz, 500Hz, 150Hz, and 50Hz from top to bottom, the third peak is 250Hz, 100Hz, 300Hz, 200Hz, 400Hz, 350Hz, 500Hz, 150Hz, and 50Hz from top to bottom, the fourth peak is 250Hz, 100Hz, 300Hz, 200Hz, 400Hz, 350Hz, 500Hz, 150Hz, and 50Hz from top to bottom, and the fifth peak is 250Hz, 100Hz, 300Hz, 200Hz, 400Hz, 350Hz, 500Hz, 150Hz, and 50Hz from top to bottom); FIG. 4-b shows the test results of the two-dimensional MXene-based acoustic detector for different initial acoustic wave intensities (five peaks are from top to bottom: 110dB, 106dB, 101dB, 94dB, and 87 dB).
FIG. 5 shows the test results of the throat-borne sound of the two-dimensional MXene-based sound detector prepared in example 1; FIG. 5-a shows the test results of different words uttered by the two-dimensional MXene-based sound probe (from left to right, sequentially "Up", "Down", "left", "right", "I", "you"); 5-b is a test result of the two-dimensional MXene-based sound detector on the repeated pronunciation of the same word; 5-c are the test results of different tones pronounced by two-dimensional MXene-based sound detectors (the first and second peaks on the left side are "ō", the third and fourth peaks are "Lo").
Fig. 6 is a flow chart of a deep learning network testing in conjunction with a two-dimensional MXene-based sound probe.
Detailed Description
While the following is a description of the preferred embodiments of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention. The present invention is further described, and it should be noted that, in the case of no conflict, any combination between the embodiments or technical features described below may form a new embodiment.
Example 1
Referring to fig. 1, a two-dimensional MXene-based sound detector according to an embodiment of the present invention is shown. The two-dimensional MXene-based acoustic detector comprises a substrate layer 10, an MXene film 20, an electrode 30 and a coating layer 40. The substrate layer 10 is arranged at the bottommost part of the two-dimensional MXene-based sound detector, the coating layer 40 is arranged at the topmost part of the two-dimensional MXene-based sound detector, the substrate layer 10 and the coating layer 40 surround to form a sealed accommodating cavity for accommodating an MXene film, the MXene film 20 and the electrode 30 are accommodated in the sealed accommodating cavity, the MXene film 20 is protected from being oxidized, and the electrode 30 can be protected from being corroded. In the two-dimensional MXene-based sound detector, the electrodes 30 include a pair, and the pair of electrodes 30 are both in contact with the MXene film 20, in this embodiment, the electrodes 30 are directly connected with the MXene film 20 through the bonding pad, in other embodiments, other connection modes may also be adopted, and only the electrodes 30 and the MXene film 20 need to be ensured to be electrically conducted, so that the pair of electrodes 30 is electrically conducted through the MXene film 20.
When the two-dimensional MXene-based sound detector is used, the two-dimensional MXene-based sound detector is firstly attached to a sound production part, such as a throat part, and then the pair of electrodes 30 is connected with the signal acquisition device, wherein the signal acquisition device is used for acquiring a resistance value change signal of the MXene film 20, or the signal acquisition device is used for acquiring a voltage change signal, a current change signal and the like caused by the resistance value change of the MXene film 20. During the vibration of the throat, the MXene film 20 can vibrate along with the vibration and generate morphological bending, and the nano sheet layers of the MXene film 20 relatively slide and generate cracks or gaps, so that the contact area between the nano sheet layers is changed. When sound signals with different amplitudes and frequencies act on the MXene film 20, the contact resistance between the nanosheets of the MXene film 20 is different, and finally the change of the total resistance of the two-dimensional MXene-based sound detector is shown. The two-dimensional MXene nano film has excellent electrical property and mechanical property, namely when initial sound waves with different amplitudes or frequencies act on the MXene film 20, the difference of the total resistance and the change frequency of the MXene film 20 can be brought, the functions of efficiently detecting and distinguishing the initial sound waves and generating corresponding electric signals based on the initial sound waves are realized.
In this embodiment, signal acquisition device can select for digital multimeter, and digital multimeter is connected with a pair of electrode 30 electricity respectively, and digital multimeter is from taking the power and can detecting MXene film's resistance value, with the help of computer system, can effectively get down and save in corresponding storage medium with the amplitude and the isosignal record such as frequency that MXene film 20 resistance value changes, when needs simulation throat sound production, can change the electrical signal input speaker that forms with the resistance value for simulation throat sound production.
In this embodiment, the substrate layer 10 is a PDMS substrate layer, and the cladding layer 30 is a PDMS cladding layer. The placement and fixation of the MXene film 20 can be effectively realized by the PDMS, a sealed accommodating cavity can be effectively formed by adhesion of the PDMS, and the MXene film 20 is prevented from being oxidized or the electrode 30 is prevented from being corroded due to air invasion.
In this embodiment, the electrode 30 further includes a lead 50, the electrode 30 is electrically connected to the lead 50, the electrode 30 is disposed in the sealed accommodating cavity, the lead 50 penetrates through the sealed accommodating cavity, the electrode 30 is electrically connected to an external signal acquisition device through the lead 50, and meanwhile, the sealing effect of the sealed accommodating cavity is ensured. In other embodiments, the electrode 30 may extend from the inside of the sealed accommodating cavity to the outside for electrically connecting with an external signal acquisition device, with the same effect.
In this embodiment, the material of the electrode includes at least one of chromium and gold, and may be, for example, a chromium electrode, a gold electrode, or a chromium-gold doped electrode. The thickness of the electrode may be 25nm to 90nm, and may be, for example, 25nm, 40nm, 55nm, 70nm, 80nm, or 90 nm.
Example 2
Example 2 differs from example 1 in that: the two-dimensional MXene-based sound detector further comprises a bias power supply, wherein two ends of the bias power supply are respectively electrically connected with the pair of electrodes 30, and the bias power supply is used for providing a bias voltage V for the MXene film 20bia. In this case, the signal acquisition device may be selected from a voltmeter, an ammeter, etc. for detecting the voltage or current change on the MXene film 20, and also converting the initial sound wave into an electrical signal with a corresponding change in frequency and amplitude.
In the present embodiment, the signal acquisition device is preferably a deep learning network, the pair of electrodes 30 is in signal connection with the deep learning network, and the deep learning network is used for detecting the resistance value change of the MXene film 20. By means of the deep learning network, the electric signals obtained from the electrodes 30 can be processed into high-definition sound electric signals, namely, the electric signals corresponding to the initial sound waves are analyzed, and the higher-definition sound electric signals are identified and fitted based on the electric signals, so that the amplitude and the frequency of the processed sound electric signals are more specific and accurate, and the terminal sound waves converted from the sound electric signals are more accurate and sound production is accurate.
In this embodiment, as shown in fig. 2, the deep learning network is preferably an SR-CNN network, and the detected electrical signals are intelligently identified and optimized by using an ultra-high resolution algorithm of the SR-CNN network, and finally processed to obtain high-definition acoustic electrical signals (i.e., high-definition electrical signals) and terminal acoustic waves corresponding to the high-definition electrical signals.
More preferably, the SR-CNN network comprises, in order, a first convolutional layer (convolutional layer consisting of 16 convolutional cores, size 32 × 1), a first pooling layer (max pooling layer with kernel size 8 × 1), a second convolutional layer (convolutional layer consisting of 32 convolutional cores, size 32 × 1), a second pooling layer (max pooling layer with kernel size 8 × 1), a third convolutional layer (convolutional layer consisting of 64 convolutional cores, size 16 × 1), a fourth convolutional layer (convolutional layer consisting of 128 convolutional cores, size 8 × 1), a fifth convolutional layer (convolutional layer consisting of 256 convolutional cores, size 4 × 1), a third pooling layer (maximum pooling layer with kernel size 4 × 1), a sixth convolutional layer (convolutional layer consisting of 512 convolutional cores, size 4 × 1), and a seventh convolutional layer (convolutional layer consisting of 256 convolutional cores, size 4 × 1). Through a multi-level optimization algorithm, a high-definition audio signal can be obtained and finally converted into high-definition and accurate sound vibration, a series of processes of detection, conversion into electric signals, intelligent identification of the electric signals and optimization, output of the high-definition electric signals and the like from the initial sound wave are completed, and the defects that the existing sound detector cannot detect the sound wave with high resolution, collect the initial sound wave, is low in electric signal resolution and cannot convert the initial sound wave into the high-definition terminal sound wave are overcome.
More preferably, the SR-CNN network includes, in order, a first convolutional layer (convolutional layer composed of 16 convolutional cores, having a size of 32 × 1), a first pooling layer (max pooling layer having a core size of 8 × 1), a second convolutional layer (convolutional layer composed of 32 convolutional cores, having a size of 32 × 1), a second pooling layer (max pooling layer having a core size of 8 × 1), a third convolutional layer (convolutional layer composed of 64 convolutional cores, having a size of 16 × 1), a fourth convolutional layer (convolutional layer composed of 128 convolutional cores, having a size of 8 × 1), a fifth convolutional layer (convolutional layer composed of 256 convolutional cores, having a size of 4 × 1), a third pooling layer (max pooling layer having a core size of 4 × 1), a sixth convolutional layer (convolutional layer composed of 512 convolutional cores, having a size of 4 × 1), a seventh convolutional layer (convolutional layer composed of 1024 convolutional layers having a size of 4 × 1), A first neuron layer (a fully connected layer with 1024 neurons), a second neuron layer (a fully connected layer with 512 neurons).
Example 3
An artificial electronic throat comprising the two-dimensional MXene-based sound detector of any one of embodiments 1 or 2 and a sound generator. The two-dimensional MXene-based sound detector is used for detecting vibration and generating an electric signal, and the sound generating device is used for converting the electric signal into an initial sound wave. When the two-dimensional MXene-based sound detector is used, the two-dimensional MXene-based sound detector can efficiently detect and distinguish initial sound waves, and generates corresponding electric signals based on the detected initial sound waves, and the sound generating device converts the generated electric signals into terminal sound waves, simulates the initial sound waves and generates sound outwards. This artifical electron throat can high-efficiently resolve out effectual sound vibration, and effectively distinguish and separate out frequency and amplitude variation between the different initial sound waves, realizes the high resolution and surveys initial sound wave.
Example 4
The preparation process of the MXene film comprises the following steps:
firstly, taking Ti of 500 meshes3AlC2Placing 0.1g of powder in 9ml of hydrofluoric acid with the mass fraction of 40%, etching for 48 hours in a water bath environment at 45 ℃, centrifuging the etched reaction liquid for 10 minutes under the condition that the revolution is 3500r/min, repeating the centrifugation operation for 5-6 times, and adjusting the pH value to 6.5-7.0 to obtain the MXene solution.
And secondly, transferring the MXene solution to constant-temperature water bath ultrasound (40KHz, ultrasonic power 350W) for 1 hour to obtain a solution containing MXene flakes.
And thirdly, carrying out vacuum filtration on the solution containing the MXene flakes to obtain the MXene film, wherein the volume of the solution containing the MXene flakes is 30ml, and the aperture of a vacuum filtration membrane is 0.22 μm.
Mixing the above Ti3AlC2The powder and the prepared MXene film were characterized separately. As shown in FIGS. 3-a and 3-b, respectively, is Ti3AlC2And MXene film Scanning Electron Microscopy (SEM) image, as shown3-b, the MXene film forms a distinct multilayer structure after being etched by hydrofluoric acid, and the multilayer structure is similar to an accordion.
Taking the MXene slices in the MXene solution for SEM test, and FIG. 3-c is a scanning electron microscope image of the MXene slices, as shown in FIG. 3-c, a layer of MXene slices with less number appear in the scanning range, and the outline of the MXene slices is shown by closed-loop dotted lines.
Taking the above Ti3AlC2The powder and MXene film prepared were characterized by X-ray diffraction (XRD), as shown in FIG. 3-d, respectively being Ti3AlC2X-ray diffraction patterns of the powder and MXene film. As shown in FIG. 3-d, Ti3AlC2After the powder is etched by hydrofluoric acid, the 104 peak disappears, which shows that Ti3AlC2The aluminum ions in the solution have been completely etched away by hydrofluoric acid. With Ti3AlC2Comparing XRD spectra of MXene thin film (Ti)3C2TX) The 002 peak of (1) shows a red shift. In addition, the broadening of the 002 peak may be related to the increase of disorder of MXene film.
Example 5
The preparation process of the two-dimensional MXene-based sound detector comprises the following steps:
firstly, preparing a solution A of PDMS and a solution B of PDMS according to a ratio of 10:1 to obtain prepared PDMS; and taking 2-5 ml of prepared PDMS to spin-coat on the mould to form the PDMS substrate layer. The spin coating process comprises low-speed spin coating and high-speed spin coating, wherein the low-speed spin coating is 300r/min spin coating for 10s, and the high-speed spin coating is 2000r/min spin coating for 30 s.
In the second step, the MXene film prepared in example 4 was transferred onto the PDMS substrate layer and vacuum-dried at 80 ℃ for 1 hour, whereby the MXene film was firmly adhered to the PDMS substrate layer.
And thirdly, arranging a pair of electrodes on the MXene film, wherein the pair of electrodes are electrically conducted through the MXene film. In a specific embodiment, the pair of electrodes may be adhered to the MXene film by a conductive adhesive, or the electrodes may be fixed to the MXene film by a metal pad. In the embodiment, conductive silver adhesive is preferably used for bonding, and then the electrode is led out through the conductive silver adhesive and the copper wire, so that the electric connection between the electrode and the external signal acquisition device is realized. And after the electrode is arranged, transferring the electrode to a room-temperature and ventilated environment for natural drying for 1h, and curing the conductive silver adhesive.
And fourthly, dripping 2-5 ml of prepared PDMS on the top of the detector, preparing a PDMS coating layer by spin coating, and drying in vacuum to obtain the two-dimensional MXene-based sound detector. The spin coating and drying processes are the same as the first and second steps.
Example 6
An application of the two-dimensional MXene-based sound detector of example 1 or example 2 to an artificial electronic throat is embodied in that the two-dimensional MXene-based sound detector is used to prepare the artificial electronic throat. The artificial electronic throat with the two-dimensional MXene-based sound detector has the advantages of high sound wave resolution, high detection limit and the like, can efficiently analyze initial sound waves with different amplitudes or frequencies, further sends out terminal sound waves based on detected electric signals, and realizes the conversion process from the initial sound waves to the electric signals to the terminal sound waves. The artificial electronic throat prepared by the high-performance two-dimensional MXene-based sound detector can help people with sound production disorder to effectively produce sound and correctly produce sound.
In a specific embodiment, the application method of the two-dimensional MXene-based sound detector in the artificial electronic throat comprises the following steps:
firstly, attaching the two-dimensional MXene-based sound detector to the throat part of a human body, and electrically connecting a pair of electrodes with a signal acquisition device.
And secondly, sounding the throat part and generating vibration, wherein the vibration enables the two-dimensional MXene-based sound detector to be bent and changed, the internal resistance value of the two-dimensional MXene-based sound detector is changed, and the signal acquisition device acquires a resistance value change signal between a pair of electrodes to generate an electric signal.
In a specific embodiment, the signal acquisition device is a digital multimeter which is provided with a power supply and can test the resistance value change of the MXene film. More preferably, the resistance value change of the MXene film displayed by the digital multimeter can be calculated through a computer, and an electric signal generated by the initial sound wave is displayed through a display interface, wherein the electric signal comprises the amplitude and the frequency of resistance value oscillation.
In another specific embodiment, bias voltage is added to two ends of a pair of electrodes, and a signal acquisition device acquires voltage signals or current signals at two ends of an MXene film to convert initial sound waves (vibration) generated by throat sound into corresponding electric signals, wherein "corresponding" means that pulse electric signals corresponding to the frequency and amplitude changes of the initial sound waves are generated.
In a specific embodiment, the signal acquisition device is a deep learning network. After the resistance value change signal is collected by the deep learning network, the interference signal is filtered and a sound electric signal corresponding to throat part vibration is synthesized, and the sound electric signal is used for outputting the analyzed terminal sound wave.
As a preferred embodiment, the deep learning network SR-CNN network.
In a preferred embodiment, the audio electrical signal is connected to a speaker, and the audio electrical signal controls the speaker to emit the terminal sound wave.
Effects of the embodiment
Effect example 1: detection of single audio signal with different frequencies and different sound intensities
As shown in fig. 4, the two-dimensional MXene-based sound detector prepared in example 1 was attached to the diaphragm of the speaker. The single audio sound signals of 50Hz, 100Hz, 150Hz, 200Hz, 250Hz, 300Hz, 350Hz, 400Hz and 500Hz are respectively played under the control of a computer, the playing time of each signal lasts five seconds, and the playing interval of two times lasts five seconds, a test result graph is shown as the attached figure 4-a (the abscissa represents time, the ordinate represents resistance change rate, Delta R represents resistance change, R0 represents initial resistance, and R represents resistance after change), and it can be seen that the two-dimensional MXene-based sound detector prepared in the embodiment 1 has different response results to the sound signals of different frequencies, and can basically realize the detection of the sound signals of different frequencies. In particular, the rate of change of resistance of the device is greatest when a 250Hz acoustic signal is applied to the device and is least when a 50Hz acoustic signal is applied to the device.
Subsequently, the frequency of the sound is controlled to be unchanged, the frequency of the sound selected here is 100Hz, and by changing the output intensity of the sound signal, five different sound intensities are selected in the experiment: 87dB, 94dB, 101dB, 106dB and 110 dB. (the intensity of the sound is measured in dB by testing the microphone in a semi-anechoic chamber environment). The experimental results are shown in fig. 4-b, and it can be seen that the two-dimensional MXene-based sound detector prepared in example 1 has different response results for different sound intensities, and the magnitude of the response result (the magnitude of the resistance change rate) increases with the increase of the sound intensity, and the two-dimensional MXene-based sound detector and the sound intensity show a positive correlation.
In conclusion, the sound detector based on MXene prepared in example 1 can detect not only sound signals with different frequencies, but also different sound intensities at the same frequency.
Effect example 2: detection of different pronunciations of human throat
As shown in fig. 5, the two-dimensional MXene-based sound probe prepared in example 1 was attached to the hyoid bone of the human larynx. The tester pronounces six different words, i.e., "up", "down", "left", "right", "i" and "you", and then records the resistance change waveform, and as a result, as shown in fig. 5-a, it can be seen that the two-dimensional MXene-based sound sensor responds differently to different readings, in which the resistance change value caused by the "up" reading is the largest, which is probably caused by the fact that the amplitude of movement of the laryngeal muscles is larger than that of the other readings when the tester reads "up". Then, in this embodiment, a repetitive detection test is also performed, and the tester reads "you" five times continuously, and the result graph is shown in fig. 5-b, where the variation trends of the five response results are substantially the same, which indicates that the repetitive detection result of the device is better. Finally, the embodiment of this effect is also tested on different tones of chinese language, and the experimental result graph obtained by reading the ō tone and the "Lo" tone twice respectively is shown in fig. 5-c, and it can be seen from the graph that the two-dimensional MXene-based voice detector prepared in embodiment 1 can detect different tones, wherein the characteristic peaks of the second tone are more than those of the first tone.
Effect example 3: example 2 preparation of two-dimensional MXene-based Sound Detector for Speech recognition
From the above effect examples 1 and 2, it can be known that the two-dimensional MXene-based sound detector prepared in example 1 can detect different sound signals of a sound box and a sound signal of a human larynx, and we can combine the experimental results with a deep learning network to try to achieve the purpose of speech recognition, the general flow chart of the experiment is shown in FIG. 6. we totally test two groups of data, namely, ① speaker playing audio signals of long vowel and short vowel of "a" and ② long vowel and short vowel of the sound of the human larynx of "a", the experimental flow is as follows:
① speaker plays audio signal, speaker plays 750 times of "a" long vowel and short vowel (total 1500 times), 70% of data in 1500 times of test results, namely 1050 data are used as training set (including 525 long vowels and 525 short vowels), the rest 450 data are used as test set (including 225 long vowels and 225 short vowels), the training set (1050 data) is input into our SR-CNN network, the detailed structure of the network is shown in FIG. 6, after the SR-CNN network is fully trained by the training set, the test set data (450 data) are input into the network to obtain recognition results, the recognition results are shown in Table 1, from the recognition results, we can see that the recognition accuracy of our network to long vowels is 83.6% (188/225), the recognition rate to short vowels is 88.9% (200/225), and the overall average recognition accuracy is 86.2%.
TABLE 1. identification statistics for audio signals played by loudspeakers
② human throat pronouncing, the tester pronounces the long vowel and the short vowel 200 times respectively (400 times in total), 70% of data in the test results of 400 times, namely 280 data are used as training sets (comprising 140 long vowels and 140 short vowels), the remaining 120 data are used as test sets (comprising 60 long vowels and 60 short vowels), the training sets (280 data) are input into the SR-CNN network, after the network obtains sufficient training, the test sets (120 data) are input into the network to obtain recognition results, and the recognition results are shown in Table 2, and as can be seen from the recognition results, the recognition rate of the human throat pronouncing long vowel by the network is 70% (42/60), the recognition accuracy of the short vowel is 76.7% (46/60), and the overall average recognition accuracy is 73.4%.
TABLE 2 recognition statistics of human throat vocalization
Analysis results show that one reason that the detection accuracy rate of the signal data generated by the human throat is low is that the sample size of the training data is too small, and the deep learning algorithm shows more excellent identification resolution along with the increase of the sample size. Another reason is that: the initial sound wave data collected from the human throat is distorted to a greater extent than the initial sound wave data collected from the speaker. For example, there are other movements of the throat during the vocal production of the human throat, such as swallowing. Along with the continuous increase of training data sample, the recognition efficiency of deep learning network can further promote, need not extra supplementary function that can realize the high-efficient initial sound wave that detects alone.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (14)
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911281660.0A CN111121951A (en) | 2019-12-13 | 2019-12-13 | Two-dimensional MXene-based sound detector and preparation method and application thereof |
| PCT/CN2020/123909 WO2021114906A1 (en) | 2019-12-13 | 2020-10-27 | Two-dimensional mxene-based sound detector, manufacturing method therefor and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911281660.0A CN111121951A (en) | 2019-12-13 | 2019-12-13 | Two-dimensional MXene-based sound detector and preparation method and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111121951A true CN111121951A (en) | 2020-05-08 |
Family
ID=70498641
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911281660.0A Pending CN111121951A (en) | 2019-12-13 | 2019-12-13 | Two-dimensional MXene-based sound detector and preparation method and application thereof |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111121951A (en) |
| WO (1) | WO2021114906A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021114906A1 (en) * | 2019-12-13 | 2021-06-17 | 深圳瀚光科技有限公司 | Two-dimensional mxene-based sound detector, manufacturing method therefor and application thereof |
| CN114689164A (en) * | 2022-04-01 | 2022-07-01 | 中国科学院半导体研究所 | Composite film sound sensor and preparation method and application thereof |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114597268B (en) * | 2022-03-07 | 2023-04-07 | 中国科学院半导体研究所 | Photoelectric detector and preparation method thereof |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150082888A1 (en) * | 2013-09-20 | 2015-03-26 | Kabushiki Kaisha Toshiba | Acoustic sensor and acoustic sensor system |
| US20160033343A1 (en) * | 2014-08-01 | 2016-02-04 | The Board Of Trustees Of The Leland Stanford Junior University | Methods and apparatus concerning multi-tactile sensitive (e-skin) pressure sensors |
| CN105333943A (en) * | 2014-08-14 | 2016-02-17 | 北京纳米能源与系统研究所 | Sound sensor and sound detection method by using sound sensor |
| CN107004415A (en) * | 2015-06-09 | 2017-08-01 | 韩国科学技术院 | Ultralow-power flexible piezoelectric voice recognition sensor for Internet of things |
| CN107478320A (en) * | 2017-08-23 | 2017-12-15 | 京东方科技集团股份有限公司 | Transistor sound sensing element and preparation method thereof, sonic transducer and portable equipment |
| CN107532938A (en) * | 2015-03-16 | 2018-01-02 | 加利福尼亚大学董事会 | Ultrasonic microphone and ultrasonic acoustic wireless device |
| CN109238438A (en) * | 2018-09-13 | 2019-01-18 | 太原理工大学 | A kind of fexible film acoustic vector sensors based on nano material |
| CN110044469A (en) * | 2019-03-19 | 2019-07-23 | 深圳大学 | A kind of motion detection apparatus and preparation method and application |
| KR20190136878A (en) * | 2018-05-30 | 2019-12-10 | 한국과학기술원 | Voice Recognition Sensor having Multi Frequency Channels with Curved type |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11470424B2 (en) * | 2018-06-06 | 2022-10-11 | Drexel University | MXene-based voice coils and active acoustic devices |
| CN109576905B (en) * | 2018-12-05 | 2023-07-07 | 河北工业大学 | An MXene-based flexible polyurethane fiber membrane strain sensor |
| CN110108375B (en) * | 2019-04-26 | 2021-01-12 | 中国科学院上海硅酸盐研究所 | MXene material-based electronic skin and preparation method thereof |
| CN110501095B (en) * | 2019-07-23 | 2020-11-20 | 复旦大学 | A biomimetic flexible pressure sensor based on lotus leaf microspinous process/MXene composite structure |
| CN111121951A (en) * | 2019-12-13 | 2020-05-08 | 深圳瀚光科技有限公司 | Two-dimensional MXene-based sound detector and preparation method and application thereof |
-
2019
- 2019-12-13 CN CN201911281660.0A patent/CN111121951A/en active Pending
-
2020
- 2020-10-27 WO PCT/CN2020/123909 patent/WO2021114906A1/en active Application Filing
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150082888A1 (en) * | 2013-09-20 | 2015-03-26 | Kabushiki Kaisha Toshiba | Acoustic sensor and acoustic sensor system |
| US20160033343A1 (en) * | 2014-08-01 | 2016-02-04 | The Board Of Trustees Of The Leland Stanford Junior University | Methods and apparatus concerning multi-tactile sensitive (e-skin) pressure sensors |
| CN105333943A (en) * | 2014-08-14 | 2016-02-17 | 北京纳米能源与系统研究所 | Sound sensor and sound detection method by using sound sensor |
| CN107532938A (en) * | 2015-03-16 | 2018-01-02 | 加利福尼亚大学董事会 | Ultrasonic microphone and ultrasonic acoustic wireless device |
| CN107004415A (en) * | 2015-06-09 | 2017-08-01 | 韩国科学技术院 | Ultralow-power flexible piezoelectric voice recognition sensor for Internet of things |
| CN107478320A (en) * | 2017-08-23 | 2017-12-15 | 京东方科技集团股份有限公司 | Transistor sound sensing element and preparation method thereof, sonic transducer and portable equipment |
| KR20190136878A (en) * | 2018-05-30 | 2019-12-10 | 한국과학기술원 | Voice Recognition Sensor having Multi Frequency Channels with Curved type |
| CN109238438A (en) * | 2018-09-13 | 2019-01-18 | 太原理工大学 | A kind of fexible film acoustic vector sensors based on nano material |
| CN110044469A (en) * | 2019-03-19 | 2019-07-23 | 深圳大学 | A kind of motion detection apparatus and preparation method and application |
Non-Patent Citations (1)
| Title |
|---|
| HUIJING DING 等: "Recent advances in nanomaterial-enabled acoustic devices for audible sound generation and detection", 《NANOSCALE》, pages 1 - 17 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021114906A1 (en) * | 2019-12-13 | 2021-06-17 | 深圳瀚光科技有限公司 | Two-dimensional mxene-based sound detector, manufacturing method therefor and application thereof |
| CN114689164A (en) * | 2022-04-01 | 2022-07-01 | 中国科学院半导体研究所 | Composite film sound sensor and preparation method and application thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021114906A1 (en) | 2021-06-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lass | Contemporary issues in experimental phonetics | |
| WO2021114906A1 (en) | Two-dimensional mxene-based sound detector, manufacturing method therefor and application thereof | |
| JP6180547B2 (en) | Electronic skin and its manufacturing method and use | |
| Hess | Manual and instrumental pitch determination, voicing determination | |
| Dromey et al. | Glottal airflow and electroglottographic measures of vocal function at multiple intensities | |
| Gilbert et al. | Isolated word recognition of silent speech using magnetic implants and sensors | |
| KR102110203B1 (en) | Attachable vibration sensor and method for preparing the same | |
| Khan et al. | Design analysis and human tests of foil-based wheezing monitoring system for asthma detection | |
| CN110631750B (en) | High-sensitivity multichannel flexible pressure sensor and preparation method thereof | |
| Shkel et al. | Continuous health monitoring with resonant-microphone-array-based wearable stethoscope | |
| CN111091830A (en) | Language identification system | |
| Zhao et al. | Anti-interference self-powered acoustic fabric for complex acoustic environments | |
| CN115346561A (en) | Method and system for evaluating and predicting depression based on speech features | |
| Liu et al. | Machine learning-assisted wearable sensing systems for speech recognition and interaction | |
| Song et al. | Study on optimal position and covering pressure of wearable neck microphone for continuous voice monitoring | |
| Liu et al. | A wearable acoustic sensor for identification in harsh noisy environments | |
| CN118303870A (en) | Cough detection method and system based on audio signal and vibration signal | |
| Dong et al. | A super-flexible and high-sensitive epidermal semg electrode patch for silent speech recognition | |
| Garnier et al. | Efforts and coordination in the production of bilabial consonants | |
| Houston et al. | Development of sound source components for a new electrolarynx speech prosthesis | |
| US20220291060A1 (en) | Elastomeric Sensor | |
| CN202821349U (en) | Electroglottography measuring device of vocal cords | |
| CN110044469B (en) | Motion detection device, preparation method and application | |
| Laštovička-Medin et al. | Utilization of Low-Cost Sound Sensors with a built in Microphone as a Respiratory Pattern Sound Indicator and a Risk Mitigation Tool: In response to COVID-19 | |
| CN119296591A (en) | AI diagnosis method and system for cardiopulmonary diseases based on sound stethoscope |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200508 |
|
| RJ01 | Rejection of invention patent application after publication |