Friction nano-generator integrated on mask and breathing interaction application method of friction nano-generator
By integrating a flexible triboelectric nanogenerator with a multi-layer thin film structure into a mask, a high signal-to-noise ratio signal is output using respiratory airflow. This solves the problems of portability and signal interpretation in respiratory monitoring devices, enables real-time respiratory pattern analysis and interactive feedback, and improves the device's environmental adaptability and user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHEASTERN UNIV AT QINHUANGDAO
- Filing Date
- 2026-03-02
- Publication Date
- 2026-05-22
AI Technical Summary
Existing respiratory monitoring devices are not portable, have low user compliance, and are difficult to use continuously for a long time. Furthermore, the integration of TENG into masks and signal interpretation have not yet been effectively resolved.
A flexible triboelectric nanogenerator unit with a multi-layer thin film structure was designed and integrated into the breathing channel of a mask. ZnO@ZIF-8 modified nanofiber membrane and polyvinylidene fluoride nanofiber were prepared by electrospinning technology to drive breathing airflow and output a high signal-to-noise ratio signal. Combined with back-end circuitry and algorithms, the breathing pattern was identified and interactive feedback was provided.
It achieves stable operation in environments with fluctuating humidity, improves electrical output performance and mechanical sensitivity, can analyze multiple breathing patterns in real time and provide intuitive interactive feedback, and promotes the development of self-powered wearable devices.
Smart Images
Figure CN122073446A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wearable smart devices and human-computer interaction technology, specifically to a triboelectric nanogenerator seamlessly integrated with a mask, a respiratory interaction system including the generator, and a method for operating the system, for realizing self-powered respiratory status monitoring and interactive feedback. Background Technology
[0002] In recent years, due to the increasing demand for fitness monitoring, health assessment, and early disease warning, self-powered personalized physiological signal monitoring has attracted widespread attention. As one of the important vital signs for human health monitoring, respiration carries a wealth of physiological information about an individual's health and potential diseases.
[0003] Breathing, as one of the most basic life activities, is of great significance for real-time monitoring in health assessment, disease early warning, and even the development of new human-computer interaction methods. Currently, most common respiratory monitoring devices rely on external power sources and complex sensors, resulting in problems such as poor portability, low user compliance, and difficulty in long-term continuous use.
[0004] Triboelectric nanogenerators (TENGs), as a technology that converts environmental mechanical energy (such as human movement, vibration, and airflow) into electrical energy, offer a new avenue for developing self-powered sensors due to their strong structural adaptability, high output signal, and low manufacturing cost. Among their diverse applications, TENG-based respiratory-driven gas sensing platforms are developing rapidly, demonstrating the potential to transform the fields of clinical diagnosis, preventative health early warning systems, and human-computer interaction.
[0005] However, integrating TENGs into everyday wearable devices in a comfortable, seamless, and efficient manner, and enabling their output signals to be effectively interpreted as specific breathing patterns to drive interactive behavior, remains a challenge for current technology. This requires addressing a series of issues, such as the structural design of TENGs in masks, signal stability, pattern recognition algorithms, and real-time feedback. Summary of the Invention
[0006] (a) Technical problems to be solved
[0007] One of the objectives of this invention is to provide a highly integrated triboelectric nanogenerator structure that can be comfortably integrated into a mask, directly driving power generation using breathing airflow and generating a high signal-to-noise ratio breathing characteristic signal.
[0008] Another object of the present invention is to provide a wearable breathing interaction system including the above-mentioned generator, which can automatically identify multiple breathing patterns and provide intuitive interactive feedback accordingly.
[0009] Another objective of this invention is to provide a breathing pattern recognition and interaction method based on the above-mentioned system.
[0010] (II) Technical Solution
[0011] To achieve the above objectives, the core of the technical solution adopted by this invention lies in: • Design a flexible triboelectric nanogenerator unit with a multilayer thin film structure, which is made extremely sensitive to pressure changes in breathing airflow through ingenious interlayer spatial arrangement; • This unit is precisely integrated into the breathing channel of the mask, directly converting the mechanical energy of breathing into characteristic electrical signals; Then, through back-end circuitry and algorithms, the electrical signals are decoded into specific breathing patterns, and corresponding interactive commands are triggered.
[0012] In the above scheme, a bifunctional nanofiber membrane was developed by electrospinning technology, namely ZnO@ZIF-8 modified polyacrylonitrile (PAN) nanofibers and polytetrafluoroethylene (PTFE) nanoparticles (NPs) embedded in polyvinylidene fluoride (PVDF) nanofibers.
[0013] Functional nanomaterials are integrated into wearable functional nanofiber interface triboelectric nanogenerators (FNI-TENG) and seamlessly integrated with face masks.
[0014] Preferably, in the above scheme, the mass fraction of ZnO@ZIF-8 relative to polyimide is 4 wt%.
[0015] Preferably, in the above scheme, the mass fraction of PTFE relative to polyvinylidene fluoride is 3 wt%.
[0016] (III) Beneficial Effects
[0017] (1) High-performance nanofiber membranes with high surface area to volume ratio, enhanced flexibility and stronger mechanical strength were obtained by applying electrospinning technology, which improved the electrical output performance of TENG.
[0018] (2) ZnO@ZIF-8 and PTFE nanoparticles have been hydrophobically modified to minimize the dissipation of triboelectric charge in humid environments, ensuring stable operation in complex environments with fluctuating humidity.
[0019] (3) The voltage of the triboelectric sensor based on the bifunctional nanofiber membrane was increased by 221%, and the mechanical sensitivity reached 27V kPa. -1 Furthermore, it maintains a voltage retention rate of 54.1% at 90% relative humidity, demonstrating exceptional environmental adaptability and gas sensitivity.
[0020] (4) Real-time breathing pattern analysis and interaction have been realized, making a significant contribution to the development of self-powered human breathing detection systems. A brand-new platform has been established for breathing drive technology in various applications, promoting the application of functional nanofiber materials in self-powered wearable electronic devices. Attached Figure Description
[0021] Figure 1 This is a schematic diagram of the layered structure of the triboelectric nanogenerator (FNI-TENG) integrated into a mask in an embodiment of the present invention.
[0022] Figure 2 This is a schematic diagram illustrating the working principle of FNI-TENG driven by respiratory airflow.
[0023] Figure 3 Micrographs of copper micromesh electrodes with different mesh densities (NM) and their influence on the amplitude of the FNI-TENG output voltage signal are shown in the diagram.
[0024] Figure 4 This is a schematic diagram showing the influence of different interlayer spacing (SD) on the amplitude of the FNI-TENG output voltage signal.
[0025] Figure 5 The typical respiratory voltage waveform output by FNI-TENG under optimal NM and SD parameters is shown, with the inspiratory and expiratory phases marked.
[0026] Figure 6 A comparison of the characteristic voltage signal waveforms output by FNI-TENG showing significant differences when the user performs normal breathing, deep breathing, and rapid breathing.
[0027] Figure 7 The graph shows the changes in the FNI-TENG output voltage signal under different respiratory rhythms.
[0028] Figure 8 This is a schematic diagram of the signal conditioning and transmission circuit module used in a respiratory pattern analysis system. Specific implementation methods
[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below in conjunction with embodiments and accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the described embodiments without creative effort are within the scope of protection of this invention.
[0030] First, the structural design and integration of the wearable functionalized nanofiber interfacial triboelectric nanogenerator FNI-TENG of this invention are shown in the schematic diagram of the specific layered structure. Figure 1 As shown;
[0031] 1 is the upper copper microgrid electrode, which serves as a breathable flexible current collector for collecting triboelectric signals;
[0032] 2 is a polyacrylonitrile (PAN) based nanofiber membrane, used as a positive triboelectric material;
[0033] 3 is a hollow ring-shaped polyethylene terephthalate (PET) spacer layer, used to maintain a constant space that allows airflow and physically isolates the upper and lower triboelectric materials;
[0034] 4 is a polyvinylidene fluoride (PVDF) based nanofiber membrane, used as a negative triboelectric material;
[0035] 5 is the lower copper microgrid electrode, whose material and function are the same as the upper electrode;
[0036] During assembly, the aforementioned layers are aligned and stacked in the order described above, and their edges are sealed with a flexible encapsulation material to form a complete, mechanically robust, sheet-like power generation unit. This unit is typically made into a circular structure with a diameter of about 3 centimeters to fit the internal space of most masks.
[0037] The FNI-TENG power generation unit is integrated by placing it within the inner lining of a mask (especially a medical mask or similar protective mask), precisely aligning it with the core breathing area in front of the user's mouth and nose, such as the location of the common exhalation valve or its surroundings. This arrangement ensures that during breathing, the exhaled and inhaled airflow can directly act on the unit, causing mechanical deformation and effectively converting the mechanical energy of breathing into electrical signals.
[0038] The working principle of FNI-TENG is based on contact-separation triboelectric charging and electrostatic induction. For example... Figure 2 As shown, when the user inhales or exhales, the airflow pressure causes periodic slight contact and separation between the upper PAN-based nanofiber membrane and the lower PVDF-based nanofiber membrane.
[0039] When in contact, the two materials are positioned differently in the triboelectric sequence, and their surfaces generate equal amounts of opposite charges. When separated, due to electrostatic induction, a potential difference is generated between the upper and lower copper microgrid electrodes on the back side, thereby forming an alternating current / voltage signal in the external circuit.
[0040] A complete respiratory cycle (one inhale and one exhale) corresponds to a complete electrical signal cycle.
[0041] Furthermore, to achieve optimal respiratory signal acquisition performance, two key structural parameters of FNI-TENG were optimized. The first parameter is the mesh density (NM) of the copper micromesh used as electrodes.
[0042] Microscopic images of copper micromesh electrodes with different mesh densities (NM) are shown below. Figure 3 As shown;
[0043] When the mesh size is too sparse, it affects the conductivity uniformity and mechanical strength of the electrode, and the microscopic image cannot be fully displayed; when the mesh size is too dense (as shown in the figure, 60 mesh and 100 mesh), it hinders the effective contact between the two nanofiber membranes; therefore, NM=30 mesh is preferred as the electrode parameter.
[0044] Secondly, the spacing (SD) between the two layers of triboelectric material is determined by the thickness of the PET spacer layer.
[0045] like Figure 4 As shown, the system tested the effect of different spacing (SD) values on the output voltage. The results show that the output voltage signal reaches its peak when the SD is 0.3 mm; excessive spacing weakens the electrostatic induction effect in the separated state; insufficient spacing may lead to inadequate contact separation and material adhesion.
[0046] Therefore, SD=0.3 mm is preferred as the interlayer spacing parameter.
[0047] The FNI-TENG prepared using the above optimized parameters (NM=30 mesh, SD=0.3 mm) can capture respiratory activity with high sensitivity. Figure 5 As shown, under normal and stable breathing conditions, the FNI-TENG outputs a regular sinusoidal voltage pulse sequence.
[0048] In the waveform, a complete rising and falling cycle precisely corresponds to one inhalation and exhalation process, and its frequency is the user's real-time breathing rate.
[0049] The output signal characteristics of FNI-TENG are highly correlated with respiratory pattern. For example... Figure 6 As shown, the voltage waveform exhibits significant differences when the user performs different types of breathing:
[0050] Normal breathing produces pulses of moderate amplitude and regular cycle;
[0051] During deep breathing, the amplitude of a single pulse increases significantly, while the period widens, reflecting the characteristics of strong and slow airflow.
[0052] During rapid breathing, the pulse frequency increases sharply, while the amplitude of a single pulse decreases relatively.
[0053] To further demonstrate the ability of the device of the present invention to capture complex respiratory activities, Figure 7The complete variation spectrum of the FNI-TENG output voltage signal was recorded as the user experienced various different physiological states in a continuous time series. The voltage waveform showed rich and distinguishable characteristics as the respiratory rhythm and state changed.
[0054] During the "no breathing" phase, the signal amplitude is close to zero with only slight fluctuations, indicating that there is no effective respiratory airflow.
[0055] During the "slow breathing" phase, the amplitude of the voltage pulse is small and the interval between adjacent pulses is long, corresponding to a low-frequency, shallow-flow breathing pattern.
[0056] After transitioning to "normal breathing," the signal recovers to a typical, periodic, stable, medium-amplitude pulse sequence;
[0057] During the "deep breathing" period, the amplitude of the voltage pulse increases sharply to over 10V, while the pulse width increases significantly, which indicates the characteristics of deep and slow breathing.
[0058] When entering a "rapid breathing" state, the pulse frequency increases significantly, while the amplitude of a single pulse is lower than that of normal breathing, presenting a dense waveform;
[0059] For a sudden activity like "coughing," the signal manifests as one or more transient, high-amplitude, irregular, sharp pulses;
[0060] The "breathing" state corresponds to a series of continuous pulses with relatively high frequency but uneven amplitude;
[0061] The "rapid breathing" period is characterized by a high frequency similar to rapid breathing, and may be accompanied by unstable amplitude.
[0062] Finally, in the simulated breathing state after "exercise", the signal showed continuous, high-amplitude regular oscillations, reflecting the synchronous increase in breathing depth and frequency.
[0063] By performing real-time analysis of voltage signals (such as calculating pulse frequency, amplitude, and waveform integral area), various respiratory rhythm changes can be accurately distinguished. These distinctive and quantifiable waveform differences provide a reliable and information-rich input source for automatically identifying, classifying, and monitoring the user's real-time respiratory status through algorithms.
[0064] Based on the voltage output characteristics of FNI-TENG in respiratory modes, an integrated respiratory mode analysis system was developed, such as... Figure 8 As shown;
[0065] Based on the voltage output characteristics of FNI-TENG in respiratory modes, an integrated respiratory mode analysis system was developed, such as... Figure 8As shown, the system mainly includes: 1 is the MCU main control module, 2 is the voltage comparator, 3 is the amplifier circuit, 4 is the Bluetooth transmission module, and 5 is the visual indication module.
[0066] Its workflow and functions are as follows:
[0067] The amplifier circuit is used to receive and amplify the raw, weak electrical signal generated by FNI-TENG, while filtering out some environmental noise interference.
[0068] The voltage comparator converts the amplified and conditioned analog signal into a digital square wave signal, which facilitates subsequent digital processing and analysis.
[0069] The MCU main control module receives the square wave signal, calculates the signal's frequency, duty cycle and other characteristics in real time through the embedded algorithm, realizes the identification and analysis of the breathing pattern, and generates control commands based on the analysis results;
[0070] The Bluetooth transmission module wirelessly transmits data such as respiratory rate and mode type obtained by the MCU main control module to external terminals such as computers and smartphones for further display, recording or analysis.
[0071] The visual indication module receives instructions from the MCU main control module and provides intuitive status feedback;
[0072] During normal, steady breathing, the system only records and transmits data, while the visual indicator remains in standby mode.
[0073] When the system identifies a preset abnormal breathing pattern (such as continuous rapid breathing or coughing) or a specific breathing training state (such as deep breathing), the MCU main control module will drive the visual indicator to activate, providing real-time status prompts and warnings through changes in light color or flashing pattern.
Claims
1. A triboelectric nanogenerator integrated into a face mask, characterized in that, The device comprises an upper copper microgrid electrode, a positive triboelectric material layer, a spacer layer, a negative triboelectric material layer, and a lower copper microgrid electrode, which are stacked sequentially. The positive triboelectric material layer is a polyacrylonitrile (PAN)-based nanofiber membrane, and the negative triboelectric material layer is a polyvinylidene fluoride (PVDF)-based nanofiber membrane. The spacer layer is a hollow ring-shaped polyethylene terephthalate (PET) layer used to maintain a constant spacing between the positive and negative triboelectric material layers. The generator is a flexible thin-sheet structure integrated into the inner lining of the mask and located in front of the user's mouth and nose in the breathing area.
2. The triboelectric nanogenerator integrated into a face mask according to claim 1, characterized in that, The polyacrylonitrile-based nanofiber membrane is doped with ZnO@ZIF-8 modified material, which has a mass fraction of 4 wt% of polyacrylonitrile.
3. The triboelectric nanogenerator integrated into a face mask according to claim 1, characterized in that, The polyvinylidene fluoride nanofiber membrane is embedded with polytetrafluoroethylene (PTFE) nanoparticles, the preferred mass fraction of which is 3 wt% of polyvinylidene fluoride.
4. The triboelectric nanogenerator integrated into a mask according to claim 1, characterized in that, The mesh density of the upper and lower copper micromesh electrodes is preferably 30 mesh.
5. The triboelectric nanogenerator integrated into a face mask according to claim 1, characterized in that, The preferred spacing SD between the two layers of triboelectric material is 0.3 mm.
6. The triboelectric nanogenerator integrated into a face mask according to claim 1, characterized in that, The generator has a circular structure with a diameter of approximately 3 centimeters.
7. A respiratory interaction system, characterized in that, Includes a triboelectric nanogenerator integrated into a face mask as described in any one of claims 1 to 6, and a back-end processing module electrically connected thereto; the back-end processing module includes: • Amplification circuit, used to receive and amplify the electrical signal output by the generator; A voltage comparator is used to convert amplified analog signals into digital signals. • The MCU main control module is used to receive digital signals and identify breathing patterns; • Bluetooth transmission module, used to wirelessly transmit respiratory data to an external terminal; • Visual indication module, used to provide visual feedback based on breathing patterns.
8. A breathing pattern recognition and interaction method based on the breathing interaction system of claim 7, characterized in that, Includes the following steps: • The triboelectric nanogenerator inside the mask captures electrical signals driven by respiratory airflow; • Amplify, filter, and digitize electrical signals; • Extract the frequency, amplitude, and waveform characteristics of the signal; • Identify breathing patterns based on features, including at least one of the following states: normal breathing, deep breathing, rapid breathing, coughing, etc. • Trigger corresponding interactive commands based on the recognition results and provide feedback through the visual indication module or wireless transmission module.